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What would you like to do?
Text answers from the dataviz survey 2017 by Elijah Meeks et al.
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Comp
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Getting data into the right format/place to do analysis & vis
Poor data governance
Antiquated data architecture
Try to be data driven- but our data efforts aren't consistent
firewalls preventing the movement of data and marketing professionals with no analytical knowledge
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The lack of time to do original data journalism: lack of proper training and opportunity to expand skills into coding.
Not enough visibility for the moment.
Clarity on expectations
Not enough coding capacity in the team
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Not enough other data visualization professionals to work with
lack of understanding and lack of interest in gaining it.
have to do others things such as emailing or editorial design
The lack- or frequency of- user feedback
slow computer- not enough knowledge on my side
not enough time
Too many demands- difficult to prioritise and focus on key tasks
Emphasis on tools and technology over methods- design- and effectiveness
Need more coding assistance
Clients who design the visualization then ask me to execute it.
Not having a data team to work with -- having to figure everything out on my own.
Not having a mentor to learn data science from.
Keeping up with new tools and learning how to use them (steep learning curves- lack of on-the-job training).
Management
Strict deadlines don't leave much time to develop more sophisticated visualizations
figuring out a place within the newsroom
Lack of support for the importance of good data visualization
too much time on data engineering -- too many ideas no time to implement
Finding contracts
Slow pace of change
"Trying to legitimize the importance of design in the area of research and my expertise in these areas. Trying to convince ""old school"" social science researchers there are new and better ways of doing their jobs. "
They don't teach me data viz or any programming. I have to be self taught.
Little time
Data silos
Business ask on reports/dashboard different from data requirements/ETL.
Lack of stakeholder engagement during requirement gathering. Too often- executives wait until a project is nearly finished before communicating fully the questions they want to ask of the data.
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Difficulty in hiring a team.
communication with client and co-workers is hard - human interactions are still the biggest challenge in all industries I've encountered.
Want to do work that is more technical
Spending too much time gathering the data
Increasing data and visual literacy of others
Resistance to change
Slow-moving organizational culture
seeing- everywhere- the awful data storytelling most advocates do
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Organizational silos
significant delays
Workload / time pressures
Time to learn new software/learning curve.
Access to data
Stubborn users
Low wage
Decentralization with little prior controls or documentation to standardise practices and quality
Lack of focus on data visualization
No support from management.
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lack of QA on design and art direction
Lack of tech training for my support staff
Obtaining and normalizing data
Lack of capacity to do things better
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Communication
Not enough data visualization roles
Clients have unrealistic expectations for budgets
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A complicated role as a senior designer / junior developer.
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Lack of quality data.
Finding time to keep up with the page of change of tools and research
Want data viz to be a larger part of my job but there is little demand
people don't seem to know what they want until they see it
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Resourcing (we're a startup)
It is primarily a senior frontend position and I am not spending enough time practicing data viz to become a master. My D3 skills are considerably more valuable to the company then my frontend knowledge- but there is no organizational understanding of data viz and no ability to capitalize on the asset. For freelancing- the frustration is being unknown to potential clients and not having the option to tackle difficult projects.
Wish I could spend more time doing viz.
Slow IT change calendar
Distance from end user- lack of support
Getting good data
People have no data science background- they design needs assessments without a proper data analysis and visualization plan. When the data is collected rare people know how to produce visualizations to help understand the situation and support decision making.
time management
The unknown
lack of projects that offer enough creative freedom
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Lack of authority to effect change
Pay / Hrs worked
too many meetings
Plenty to do- not much time
the public
Data exploration. The data warehouse and IT is a blackbox
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I want to do advanced analysis but users need to buy in before it would be useful.
Js fatigue
Lack of communication between analyst and consumer of data
Data
when package upgrades break code
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Not having the time to do more data visualization
Colleagues and stakeholders do not always have visualization literacy to understand anything beyond basic line or bar charts.
Messy data- tight deadlines
Inconsistent priorities from business partners
Bad Data
data cleaning
The best tools have an interaction model (CLI) stuck in the 70s
ETL
Communication
Not enough support/resources/time
Will of adding always new things rather than finish the important ones.
Lack of understanding of data visualization as a discipline unto itsel.
teaching people to do their own analysis
Not enough hours in the day to learn new techniques and spend time developing.
Distraction
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Lack of focus on my interests
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People don't understand the amount of thinking and code that goes into visualisation.
Getting pulled in to non-data viz projects related to my skill set.
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data viz doesn't fit within the modular structure of the company
Cash flow
Lack of adoption of my viz
Something unrelated to data visualization
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Lack of resources/time to advance knowledge and use of visualization
Getting swept into UX/UI projects
Not challenging enough
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People who think they know how to do my job / my job is not as *scientific* as theirs
Lack of access to use some of these tools- no web dev capability
Keeping track of data sources and data cleaning that has been done. Lack of reuse of data resources.
Impact attributable to polished viz work is unclear
Having to spend too much time preparing the data
Tight budgets / schedules- managing multiple projects at the same time
Variety of data and analysis makes it very hard to visualize
That data viz isn't a thing yet here in germany
Inability to choose my own data vis tools
Technology integration
To much to do
Job security
Poor management
Too many meetings and too much email
Always in a time crunch and always looking for profits in 1st- 2nd and 3rd places. The rest comes later.
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deadlines
Clients pretending to know what to do design-side
cost of data - I do sports analytics
Administrative tasks
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Other responsibilities
No plotting libraries in Perl 6 !!
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Low level of statistical knowledge in colleagues.
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Data cleaning
Ambiguous project requirements
seeing workflows that are really difficult
Lack of technical support.
data munging
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Nit having access to tools that are not free
Working with untidy data
consistent access to data
Lack of audience knowledge (have to train them!)
people don't take communicating their results seriously
My version of R is so old- damn it. I have to use plyr instead of dplyr. WTF.
lack of good data prep tools
Forced to use SPSS Modeler
Not enough time to learn new tools
Underpayed
Not much of an opportunity for advancement
Involvement
Learning- appreciation
Working on wrong hypothesis
Poor specifications
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Data collection is expensive and difficult
Not doing enough data science- and especially visualizations
Lack of collaborators
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Clients
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Limited use of tools allowed
Rework
Database tables are messy and not normalized
Underestimation of the value of Visualization
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Data prep. No one responsible for information architecture.
Not undertanding how to extract value of the visual story
had to do regular front-end tasks (such as build forms) in addition to vis work and expected to handle that regularly
Little recognition
low Pay
managing client expectations; clients seek innovative insights- but want to defer to traditional methods
being brought in at the end- when its too late to fix bad strategies- poor design- and products based on little-to-no user research
not enough time to learn and do everything I need to
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I'm not smart enough.
"many ""bosses"""
Entrenched interests which block adoption of many new approaches
Balancing research w/ development work (role requires both)
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Too many meetings and difficulties hiring team members
Lots of writing- less visualization (true of most vis PhDs)
Not being included in the big picture- just being asked for a specific thing- which doesn't show the whole picture.
Getting clients to understand the process.
Funding support
Lack of data knowledge amongst collegues
IT security
low compensation for number of hours worked
Lack of understanding of reporting vs analytics
Challenge I: Fully integrate non-tech/non-data literate team members into the process and decisions. Challenge II: expectation on client side of cost involved in data visualization projects
Not enough data visualisation work
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Not enough contact with end users.
CLIENTS
Asked to focus on many things aside from data visualization so it just is rarely prioritized.
Data wrangling. Data scraping- data availability
We're a charity- so cash flow to do bigger and better things + govt getting out of the way.
My personal skill level
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Not enough hours in the day
rushed schedules
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Globally distributed internal user-base- with differing priorities
So much to learn- so little time.
Too much time cleaning and preparing the data
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Details always eat up more time than expected...
Servers
Don't do enough machine learning
Still learning how to do reporting and come up with interesting news ideas
Feature creep driven development- some components become too complex
Siloed work environment
COTS software security approval
Getting user feedback
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Not enough time to learn new skills or work on long-term projects
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deadlines
Difficulty getting feedback
Lack of collaboration
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Work overload
Lack of open mind to new ideas from higher management
Lack of buy-in to better workflows
Data Viz influences requirements- but is usually not given credence until late in the project.
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Clarity of user issues/questions
Data quality is not a primary focus hence makes it hard to extract valuable info
I.T. infrastructure- incomplete requirements
Data visualisation is not given proper attention
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Not enough time for innovative design
I have to do other things- not just vis
money
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Bureaucracy
Old technologies
Too many libraries to work with .
micromanagement by executives.
More ideas than hands
I feel mostly lucky but we have a lot of non productive meetings & may have several things on the go- so can be difficult to focus.
The amount of grunt work I have to do to be able to do my research.
I wish I knew more tech & design
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Lack of willing from stakeholders and/or users to explore new ways of using computers in their daily jobs- including new tools- technologies- interaction and visualization techniques and designs.
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Lack of investment in R&D and modernization in enterprise.firms could do more
Not able to quickly tell a story. long period between idea and result
Creating tools (upon request) which end up forgotten and generally unused.
Finding talent- customers
Management lack of vision and understanding
Often the quickest way to get the point across is through simple bars and lines- but I no longer get enthusiastic about building those
There's a data analytics team that's supposed to be reducing our big production database into something more consumable for analytics and visualization- but there are lots of roadblocks that keep that from getting off the ground.
Finding interesting ideas that are worthwhile to pursue.
Expectation of capability vs. time
(not) working in a team project.
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Poor technical leadership; not enough senior developers or leadership with strong development background.
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Dealing with the IT framework of the parent organization.
blurred line between design / viz
Time pressure -too much to do
Lack of collaboration and interest within my university department. Need to outreach to other Institutes/centers/etc.
Lack of simple tools
The tools I use are not fully integrated with our production systems.
Demand is so high there is not enough time to innovate.
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not enough time
Access to data
Lack of long term vision
Lack of help from other teams
Data prep takes a long time
People thinks is like making excel chart
The indecisiveness. We jump too quickly from one goal to another. Leads to burnout pretty easily.
Mobile reaponsiveness
Dealing with misunderstandings of the data
Cleaning Data
Poor money and menial tasks
Lack of proper Q&A forums for the data visualization sources I'm using.
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Resistance to data viz in some parts of the university
Non visualization tasks.
Fragile APIs
Data visualization is not taken seriously.
The lack of time to build the visualization
freelance isolation
Not enough time for all I want/have to do
Awareness/understanding of what datavis is
Data access
Not enough datavis
hard to work on different subject matter all the time / getting up to speed with domain experts
don't really have any- but doing administration is not my favorite activity
Not enough time to learn
Data prep
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Time
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Lack of appreciation- and therefore- training for high-level data vis
We don't know the data from a functional point if view
Data quality
The logistics of running courses often eat up more time than developing content.
Lack of other people doing visualization
Spending too much time preparing unnecessary- unused reports.
My lack of bandwidth to work on the various projects
Not enough people focussed on data prep.
Sole analyst on the team so managing work flow
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Pay
Repetitive nature of job
My organization is very set in its ways and unwilling to try or learn new things (even if it improves our projects)
Boss doesn't have time for Research
My skill is too rudimentary for my vision.
organisation not mature enough to value/exploit benefits of decent data management and subsequent strategic planning
Not being able to use my dataviz expertise. Generally- have to do what the internal customer wants even if it's not best practice
Data management
Inability to experiment with cutting edge software packages or other tools: Enterprise policy prevents most user installations of programs; onerous process to add to approved program list.
execs always wanting more analysis faster
A lack of understanding of the data from superiors
lack of advancement opportunities/job growth
Limited time to learn
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constant ad hock requests
Tool pricing not decreasing as user base increases
Data quality
Low buy-in
Short term company vision
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poor organisational culture
Differences between different technologies (R- Python- Javascript)
No investment in new approaches
There are no design thinking fellows arround
Not having adequate data/content before the design process
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Stressful deadlines- when executives do not grasp the time and effort it takes to refine and depict data in useful- actionable ways.
confusing journal specifications
"The people I support know they should ""use data"" but don't know why or how."
Lack of executive sponsorship
Not doing enough data viz
Lack of open source tools
Not enough focus on analytics and data visualization
Product managers
Lack of innovation
Resources: time/money/mgmnt support for doing adequate job & money for SW
Visualization is becoming a commodity talent and often outsourced if it's tool based.
poorly payed
People expect help from me due to my wide breadth of knowledge- but i often run into problems that I can't really find help with
Lack of organizational focus
Lack of budget/time to produce the immersive visualisations I would like
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Lack of communication with management and editors.
Helping rich people or bad people becoming richer and richer
Frustating evaluation of work
Infrastructure and planning.
Convincing people stories can be best told with visualization- getting buyin on atypical viz design
Lacking accurate and complete data- with little ability to change process to ensure more accurate data
Time spent is not properly paid for.
Fragmented tooling across PIs I work for make it hard to get a fairly standard work flow
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Lack of integration between tools
Hard to get enough contracts solely for visualization. Perception of visualization by potential customers and funding agencies is biased : they think it does not require particular skills- or it is not sufficiently innovative in itself.
Getting past data issue
Individual projects often need visualization at some point- but often times this is not recognized during the project planning/proposal process. It is often recognized later in the project- meaning that opportunities to grow the visualization side of the business are limited.
Lack of equipment
Need more time to implement instead of doing data processing
Not having benefits as a contractor/freelancer/consultant.
Data cleaning
Lack of vision
Understaffed and under appreciated front-end team
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No one really gets visualization other than the charts they see in excel
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payment
React
Client's lack of ambition or taste
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web development frameworks
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Need for additional training
Lack of innovation and agility
work on too many different projects- never feel like i have enough time to devote to any of them
What I do and what I am needed to do are different than my job description and how I am compensated. My job description has little to do with dataviz and so there is little formal opportunity to grow. However- I am still known as the dataviz guy.
so much to do- so little time
procrastination
understanding of value from customers internal and external
Slow change in people's mindsets
To many hours behind screen
Too many etl
the structure and organization of the underlying data
I'm supposed to do more science instead of data analysis
Communicating to organizations why they would want to use dataviz / how it could benefit them.
Administration tasks take up too much time
Preset chart types are limiting
Managers and some peers don't recognize the contribution we can do to reach the strategic objectives of the organization
Few understand the importance of design
Lack of documented business rules
Dirty or unreliable data
Data clarity
need a skilled data visualization (beyond how the tools work)
Getting right skill set to do Data Visualization work
Computer memory
Data prep- clients not understanding their own data
Lack of professionalism
Stuck pumping out Tableau dashboards instead of custom viz.
the changement management for people that use excel for years and don't see at first look the adavntage of dat visualization tools
Bad data
Having too many projects that each need a little work a lot of times and take forever to finally complete them.
Reporting through routine rather than need
Using cutting edge tools means they are buggy and not always feature-rich
Stakeholders understanding the time it takes
Insecure work flow as part of being freelance
the tools don't match the speed of my analysis
battling the perception that analysis=reporting
Administrative duties
time it takes to design
No real code sharing repository
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Alienation of labor as a product of capitalism as a mode of labor relations.
Missing data
implementing standard functionalities from scratch due to tool limitations
implementing trivial UIs consumes lot of time
Getting a dataset for publications
Dealing with legacy code
The diversity of tasks that I'm asked to do
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Finding the right people with the right skills to help me execute my ideas
Demand is to high- lack of human resource
Too much work- not enough time
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Old stack- lack of time for refactoring
Needing to constantly convince people the value of data visualization- explaining the kind of data required for data visualization- and making people realize that good data visualization takes time to do
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JavaScript- WebPlatform
Siloed
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Lack of time for research (d3- JavaScript- wtc)
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Lack of resources
Decisions not made from the visualisations
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Not having the assets or skills to tell my own data stories- in many cases.
Communication
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Meeting requirements set by individuals who don't understand the underlying data structure.
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"Resistance to innovation in visualisation. Keeping the same format for decades because ""that's how it's always been done"""
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Not working with other devs/visualizers
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doing many other things that are not data visualization but are necessary to get to the visualization bit
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I don't like data piping
Mostly everything that is _not_ involved in the work of data viz
Not enough time. Too many projects.
Deadlines- weird decisions
Answering email
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finding interesting projects
Inconsistent goals; stakeholders request visuals before data/content
Lack of creative input/ability to story tell
Content is not interesting or socially valuable.
Dataviz is a viewed as subsidiary to data science
Data wrangling
Data-based research is- in general- a very new endeavor for my institution- and so there is no established career path outside my current project if I want to remain working here.
time constraints
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Not enough iteration
Not enough time for implementing ideas
Technological limitations
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Disparate data sources
Client not knowing what they want
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data cleaning
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technical issues with high performance computing facilities as part of the process of generating data for visualization
Educating leadership about data
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Clients
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Not spending enough time doing visualization work
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Data integrity questions
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Lack of resources- unclear career path
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Inconsistent data across teams(governance)
Higher ups not utilizing graphs properly
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Tooling
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Limited time
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Coordination with other deptartments
Being asked to provide instantaneous org info without having clean/full data to rely on. Simple inquiries take forever to answer accurately if they can be at all.
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Difficulty of getting publicity for work.
Time constraints
Data visualization is not valued highly enough
Too many administrative chores- too much data monkeying
Lack of usage of my info
Not enough time in the day
Lack of opportunities to visualize
Asshat sales people
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"Being asked ""so what does the data show you"" instead of being asked specific questions or objectives"
I want to vis everything
Explaining scope of the job to others
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Not enough time to focus on creating custom interactives.
Little time to invest in pushing visualization
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Endless data cleaning
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Timelines- not enough people
Too little time designing vis
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Lack of collaboration
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Too much to do
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Too many tools
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not enough time to finish work
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Not enough people self learning to build visualisations with the data we have
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creativity restraint
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Having to color everything instead of showing hard numbers
lack of time
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Level of interest is not that high
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Getting the data from our clients in good shape.
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sometimes I want to spend more time on single projects
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rejection of great ideas
Not being able to explore more aspects in learning more tools and ideas
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Management
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When I can't persuade a client that something is a bad idea and we have to go with it.
Poor data management- the relational database simply exports every table as CSV- I have to link the data again rather than being able to use the relational database from which the data is exported.
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data sources
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finding new client
Data cleaning- scrapping
doing work that is not related to dataVis design
finding relevant data
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sometimes missing a 'common thread'
Insufficient time for design
falling between technology- design and editorial depts. with their competing demands- requirements and expectaions
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Not getting payed fair.
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Non standard development process
Data cleanliness. Lack of vision from executive team on what they want communicated.
Lack of full tool access for both creators and users
lack of flexibility to do data vis
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Enterprise on traditional BI (Microstrategy). Business users want Tableau but we have limited licensing on Tableau online.
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Unrelated to data visualization (politics at work)
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Being the only data visualization specialist at my job- so when I have trouble with something- I rely heavily on online forums
Not being given enough time to learn new skills/languages tools to level up our dataviz.
alone doing visualisation
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salary
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Slow backend
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Science is hard
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to little time- to much to do
Learning d3
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Pressure to produce
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Its not exploratory enough.
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Stupid people
Limited sophistication of data consumers
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low pay
I'm technically an IC engineer but my day job is a mix of: EM- product designer- product manager... it's difficult to find the time to do everything in a day's work.
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lack of recognition
data analysis tool is not eye catching
Communicating uncertainty- statistical illiteracy
company data management/quality practices
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Folks not taking effective communication with graphics as seriously as effective communication with text.
That I have to do too much of the data pre processing. When I got help from developers to do the work- I ended up spending too much time validating their code. There were too many bugs and issues. So- unfortunately for the next projects- I preferred to do it my self.
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Lack of innovation/vision
Expectations for more in too short time- unrealistic deadlines
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"Developers I work with think I'm just disturbing their workflow by giving them designs- they think they can do my job because in their opinion it only requires common sense.
"
Don't do analysis
Getting enough 3rd party tools
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unrealistic expectations on effort and time- not enough people to do the work.
boredom- lack of growth
Difficult to creat change in my organization
I wish I was better at coding (I'm getting there slowly!)
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Knowledge gaps of clients
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I don't have enough time to focus on data visualizations
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Making useless stuff from time to time
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Data quality and availability
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Rapid change in technologies makes it hard to keep up.
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Everything related to preparing the data
Not enough resources
Not having enough time to add more tests or solve technical debt (Angular 1.X vs Angular 2 etc)
People thinking of visualization as decoration
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People don't care
too many tools out there
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time available for data work
i work on a small team so it is balancing priorities and managing people
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Not enough understanding of the work that goes into getting data for analysis
Doing unnecessary work
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Access to relevant data
Money
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Data Quality
Broadening peer and administration understanding of visuals.
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Pay- and Managment
waiting on queries/extracts
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Clients!
Time
getting enough billable work
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Too much work- too much bad data
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Non experts using data incorrectly
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Slow organisation
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"Fighting the mentality that ""making charts is easy"" "
data quality limiting analysis and visualization
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data availability and formatting
Lack of time
estimates and changing requirements
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We like to claim that we're having a bigger impact than I think we are.
I have many bosses and sometimes they don't coordinate and it's difficult to organize work
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Ability to do more data visualization
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Too busy to keep up with learning new technologies / programming languages
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Deadlines
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Poor understanding from leadership
Data wrangling
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People trying to rebrand what I do
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Time to do it
Reluctance of the other people in the organization to change workflows
Poor enterprise data management structures
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Lack of recognition for the importance of understanding data - accusations that the job is not 'value-added'.
Scarce interaction with stakeholders
Not being able to get new technology readily.
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conflicting brief
Lack of dependancy on data by end-users
Time constraints
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Inability to do in-depth analysis due to lack of time
Pressing deadlines- waguely defined aims.
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Data impacts of decisions are often neglected
Misunderstanding of my managers
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Aligning incentives for all people to work together.
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Not having the skill level of our programmer analyst- limits my ability to aid with visualization.
Government bureaucracy
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the reliance on opinion over data
Getting the right data to be able to answer a question or provide meaningful insight. Often the data you want is not something that has been recorded or exists yet- or takes significant manipulation or cleaning to use.
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either using 3rd party expensive tools or having to build-from-scratch with code.
No opportunities for training/trying different techniques. Being expected to whip up beautiful insightful things at the last minute. Required changes and additions from people who are not familiar with the data but have the power.
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Scoping & visibility on full range of the problem is difficult as freelancer
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Students
data that is inconsistent and varying formats from different primary sources
Organizational structure- role defintion
Organizational structure- role defintion
Lack of clarity- coordination- oversight in terms of requirements for me to fulfill
Time needed to learn the data visualization tools available to me
Lack of a focused research question to be answered with analysis
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Lack of funding/resources
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People don't really understand what infographics are- so you have to beg for good data.
Lack of time to create products- always stuck with mundane tasks
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Low IT responsiveness
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not enough hours in the day to do everything we'd like
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Working in silence in the studio
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Lack of clear purpose of our org
Too many other tasks
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Lack of access to resources- new visualization tools
Changing requirements- project creep.
delays
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Time
Communication
Time required to make data delivered to me usable
lack of process and resources
data in shambles- not structured and not central
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Clients who don't know what they want
system complexity / data volume
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Lack of resources and talent to hore in my country
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Lack of technical support
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Fixed set of tools- not the best tools for the job
Creative freedom
Tight deadlines inhibit taking time to learn new tools that could be better in the long-run.
Turnaround time
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Access to technology and data.
Different data viz reqs
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Visualizing the results.
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current learn
No one understands data science- but they pretend to.
I do not learn a lot from my colleague
No common language to tell visual stories
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getting access to necessary data
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Data Quality
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Inexperience
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Web platform is getting better but wasn't designed to build dynamic applications
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Utilization and lack of time for creativity/professional development
Tearing apart stuff that has been built to start from the ground over
More relevant topics
lack of focus
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When customers do not listen.
Missing top management understanding and support
Lack of previous requirements.
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I have to stick with Excel and Powerpoint and I can't code my own solutions
deploying visualisations
Tool's limited functionality
getting access to the data
To find the best visualization format
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Too little information about some projects and the story we are trying to tell to create effective visualization.
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Salary
Lack of statistical awareness (I wish more people saw the relevance of boxplots in categorical data visualization and understood tests for significance between categorical data–very powerful methodologies with HR data and segmented populations of employees)
Promoting analytical evidence
No ideal results
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designing data
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No money to buy licenses
low maturity of people about visualization potential
Client lack of understanding data and what a design process is and mis management of time
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Data Integrity
Data quality and integration
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Too many hats
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The basic rules of Few
The transformative potential
I can write- too!
How important it is to understand the data you're working with to know how best to visualize it.
How difficult data prep is because of poor architecture
The time & effort to make them readily consumable for the organization
General low awareness- literacy in the data viz for systems/infra engineering.
the need for clarity and visualization rather than just Excel style tables
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They don't understand its potential for handling big data. Some bureaux track the data coming into their offices- don't even think to pass on data-rich reports- so we are still picking them up at the last moment instead of having time to prepare the material properly.
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How to tell a story with visuals
How time consuming it is to get and process the data beforehand
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How much thought goes into the unseen.
how hard it is to do.
it importance to reach the public in order to make desicions
Value
most users are only concerned about what they can read and understand in less than 5min. while there's so much more on display.
visualizing complex subjects takes time & experimentation
How complex the process is- especially around data preparation
"My decisions are based on science and proven best practices- not whim or an ""eye for design."" Good visualization works well because it is based on known principles and is not the result of happenstance or good luck."
Complexity- especially given the need for responsive visualizations
I'm self employed but I wish clients understood that it's my job to come up with the visual.
They don't understand the crazy skill set that's involved. They think I just push a few buttons and the software magically creates something for me.
How much time is spent cleansing data to make it usable
Two things: 1) the amount of time it takes to produce data viz. 2) the breadth of data-viz options available to them.
People think it's just bar charts/pie charts and nothing more
That it takes time and it isn't being spit out automatically by the computer
I think they get it just fine.
When it is done well- it's an iterative and collaborative process
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Why I don't like pie charts with more than three sections.
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"That it's more than just ""making things pretty."" It's taking a pile of data- analyzing the problems to overcome and then finding the best solutions to those problems. It's about drawing people in to give a shit about all of their research. "
How hard it is
How much time it costs
It requires a lot of work- not just 5 minutes in excel. Also- that it is a blending of science and art that requires knowledge of effective practise.
People think data vis is just the same as UI/UX.
Simplicity in presentation is anything but simple
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How domain specific design is necessary for many domains.
the power of answering questions with the data.
the value (and ROI) of it
Yes- they largely think it's like generating a powerpoint.
That it is as critical a communication piece as text
"Often times- clients get caught up in what's ""sexy"" and lose focus on what's truly the right design choices for their use case."
Visualizations aren't complete through software alone; the annotation layer is essential
i'm a sole proprietor
That the basic principles exist to make analysis and storytelling better
A lot of things but I'm not complaining about it.
the importance of data
How long it takes- the skills and effort used - good data viz looks effortless (dammit!)
How important it is and how hard it is to visualize bad data.
Approach
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Every component of a visualization should be warranted - more is not always better. Don't design for print- design for digital interaction.
Why it can't be a table of numbers
Effective visualization is just as important as the data. If it is not visualized well or correctly- the message will be lost.
The importance of and work that goes into creating beautiful- usable dashboards for them to use.
""
some parts/concepts are just still very alien
How it opens clear understanding.
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""
It's often as much about people- psychology- and perception as it is about the data.
That it is more powerful by combining it with ML/automatic methods.
It's worth spending the time to learn how to read data visualization
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""
I'd like to move towards interactive infographics- the other guys are pretty chart-oriented.
As a freelancer- people get it. Or maybe I've just been lucky. :)
That it's an important part of the process- and not always the easiest.
How much thought goes into creating even a simple visualisation in order to make it useful.
How much time and intention it takes
the work that goes into prepping the data
They don't understand how custom a visual story can become; however- this team brought me on board because they hope that I can do something unique.
We're small- so I'm fine with the level of communication
"They don't understand why anyone would want to do visualization all the time- and they absolutely cannot imagine how data viz could be a source of profit on par with frontend or backend. They also believe that data viz is not a ""real"" software engineering focus that will prepare juniors for architect and management positions."
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Difficulty of making effective visualizations. Choices of what to leave in and what to leave out.
Time and thought that's put into design. Good design looks intuitive- so people believe it naturally occurred like that.
"Some people do understand the entire process. They think it's just- ""create a quick chart"". "
""
they don't ask deep questions
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how truly considered my design approach is: cognitive aspects- graphic choices and white space- overall structure- level of drill down- the importance of maintaining the narrative throughout it all... I am completely earnest in this work because it is so important to do it well.
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That they can do it too
Good engineering is slow. I might spend hours making tiny adjustments to CSS or D3 selectors to track down subtle bugs. I don't think it's because I'm a bad developer- I think it's that I've reached the point where the bugs I spend time on require deep analysis.
seen as decorative
It takes plenty of time and effort to create truly effective data visualizations.
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time requirements
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Good design is invisible and it takes a good amount of effort to strip out the unnecessary aspects.
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The intricacy- skill that is needed. The need for DBAs to provide clean- useful data
Amount of effort
they are smart
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They stoutly refuse to appreciate the hours of work that went into deciding whether my curves should be red or blue.
Data needs access- understanding before & after- then action from result.
The amount of time it can take
Complexity
Not enough have a good understanding of technical communication principles
The point
how hard it is to get it right
Why some seemingly minor aspects are important- and why not producing simple bar charts is a good thing.
Time taken to explore dataset
How to see the story
Its a data viz company- they all get it😉
Some people don't see the value of working in details of data viz.
The importance of user interaction and the importance of responsiveness.
They think it's magic. I don't think they get the work that needs to get done to have data prepped.
The time and effort that goes into it.
Complexity
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Underlying data and time consumed
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The amount of data wrangling behind the illustrations.
Justifying the investment in a data warehouse and business intelligence tool instead of relying on Excel.
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the need to have a working familiarity with math- design- UX/UI- engineering-data science- etc- which can seem dilettantish
We're all good in that department
The big picture
The power of design and the scope of deliberate design choices that are required for quality work
Actually not a trivial thing to do
The complexity of preparing the data in a manner conducive to allow visualization without introducing inherent bias to be displayed
That there are always going to trade-off in data display decisions
the complexity of the data manipulation required to clean data for visualisation
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That it is a scientific field of study!
I want to pack more into less- they want what they're used to
The amount of prep work necessary to put a visualization together- especially if the data has not been curated before beginning work on the visualization.
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They think it's easy to adapt the different data formats to make meaningful visualizations.
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An appreciation of how data viz and help deepen their understanding
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The amount of knowledge and work it takes to produce a visualization that tells a important story
Necessary effort
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Software limitations
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Why it's worth putting that much time and effort into communication of results
It transforms data into knowledge and it is an incremental work. It needs feedback and it needs to be highly prioritized.
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we are all data viz driven people
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Value of visuals
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The value of a well designed graphic
They partly do
importance of data visualization for science communication
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Historical fixation on a rudimentary grasp of the t-test.
That excel isn't the be all and end all.
It's really freaking hard to make a clean beautiful simple informative plot
The iterative nature of exploration before arriving at a meaningful visualization
the power of interactivity for insight and model specification
It takes time to create a visualization that tells a meaningful story appropriate for the audience in mind (e.g. other engineers vs. executives).
data munging
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The specific skills it requires
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that 80 of the work goes into cleaning the data
The underlying logic and design philosophy
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That R is a free download for God sake. Let me get the new one.
the possibilities in visualisations- if you work with it
They don't yet get how valuable data visualization is as a means of communication and explanation.
The amount of thought and preparatory work it takes
The amount of time spent getting the data in right format
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How valuable it is and how much time it takes to do well.
they dont know about what data visualization actually is
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The time and effort taken to produce a meaningful visualisation that can be easily consumed
That they could do it too if they spent the time to learn
It's not a substitute for statistical analysis and critical reasoning
Design matters
How much code goes into each visualization.
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""
Data visualization depends on the underlying data type- question of interest- and context of problem. There are many aspects that go into the decisions behind which visuals to use and not every graph type is necessarily appropriate for a given data set.
Effort it takes to produce
It takes time... people think an idea they have can just be shown on the screen in hours.
How powerful a tool it is for exploring relationships and correlations
Everyone thinks they can make a decent visualization (hint: they can't)
I'm self-employed- as are dozens (hundreds) of us- so this question is n/a.
The amount of time it takes to clean data in order to present it.
How long it takes. The background data prep. What the DVs show.
The amount of time spent on a piece over the value of it
Non-engineers just want something eye-catching. Some think visualization has values but does not see how we can help them. Some cannot tell the difference between vis person and front-end dev. Knowing how to code in D3 and designing an effective vis are different- but not every get this.
Recognition
The importance of data preparation before visualization
The amount of time it takes to produce; the computational components that produces the visual results
They think a developer choosing a graph from a typology and mashing a dashboard together is acceptable
I don't feel that way- my work is widely recognized within the org as being one of our primary thought leadership vehicles.
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Just because it's FUN doesn't mean it's not serious work!
that it's iterative; first draft always dies
That it can make them much more effective at their own work
Some believe visualization is only for presentation- others for data analysis; we need to communicate that there is a continuum between the two- and that different techniques are suitable for different points along this continuum- hence the need for research to identify the right techniques.
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The time and effort required to create visualizations when working with large transactional systems that require extensive cleaning and preparation.
"With visualization research there tends to be a perspective towards ""expanding human knowledge"" rather than valuing open source systems research output- which can be frustrating at times."
""
How design decisions follow from patterns in the data- rather than a preconceived narrative.
Graphic design principles
The presentation selected matters to the understanding of the information
That I trying to make it easier for them to understand the data
how much time it takes
Compromises we have to make
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The background in design- perception- data analysis that is required
They could do a good amount of it if trained properly.
The value of working with end users to determine their needs- rather than working to an arbitrary spec.
simplicity is not a negative thing
How to effectively ask for it to be done. It is valued when done- but nobody knows how to really ask for it well.
The time spent to produce the vizualizations
That's not a thing. Our whole org exists to visualise data.
The work and thinking that goes into it
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How it can be misleading if done wrong
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How complex could it be to develop an advanced visualization
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The stats I show.
Somewhat. People aren't good at reading charts and just want them to confirm their narrative often rather than to really inform
I think they do understand the data visualization work
Leadership totally gets it - Steve/Archie/Amanda/Hannah are all great!
It's fast to prototype- lengthy to implement a prototype in the dashboard
That statistics matter
Why it's helpful
That it is a starting point- not a place for confirmation
D3 custom charts take longer than excel
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Sometimes- it's ok when the line chart is boring
How difficult it is to make it better than our competitors' work
That Matlab is awful.
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"It's very difficult. ""just add a graph"" is a lie"
Most people don't realise the importance and beauty of the art of showing hidden trends through visualization which are otherwise not spotted easily
That there were whole books and papers I had to read before I was satisfied with our organization's standard bar chart.
Vendors do not have the answers- and often bias people against good solutions.
The choices that are made with regard to spacing- shape- color- are more pronounced and thought out than they think.
The potential of unveiling data
the amount of work required to put things together
They don't realize how manual or coding-intensive it is. i.e. D3.js
How it can help get new insights in the way we operate in our line of business
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Dont understand the data enough
It requires lots of time
the right value of it
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How much time it can take to implement interactive visualisations.
That Javascript and Java are two different things
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""
misconceptions about simplicity/complexity.
How complex it really is
That my design decisions aren't just pulled from thin air!
How easy the basic stuff is and how hard the advanced stuff is.
The size of its impact on what the ultimate user experiences as value- information- engagement- fun- corporate (or other organizational) image; the constraints and goals of the design space; the dependence of datavis on an overall sensible and working approach from company strategy to service and product management to data capture and management to client marketing and user experience.
Data visualisation compounds the problems with statistics. A lot of work has to go into being sure that people's interpretation of a visualisation is in line with the underlying data.
The value of pushing the limits of standard and known practices
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Never has been a problem for me
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How much work certain charts are- that they are not throw away projects.
Data visualization is not a business plan
The added value data viz brings.
How much time it takes to think of a good design and even more how long it takes to implement- especially interactivity
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The difficulty and time-intensiveness of creating quality visualizations.
I don't currently have this problem- in my previous job however- there were many people that were data illiterate and had to stare at bar charts for a couple of minutes before they could comprehend what it meant. Now if anything those I show my visualizations to know what it conveys faster than I do- despite having made it!
My creative process. They see early stages like a lump of clay- and it doesn't fire their imagination- like it does mine
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They think that design can happen before getting hold of a data set.
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Difficulty in creating viz
They don't get how time consuming it is!
Inspired but Cautious
There is a tendency for those consuming the work to be less interested in analysis and more focused on reporting- which is less insightful.
When it's done really well- the effort and thinking that goes into a visualisation is not obvious- the message just cuts straight through- so the people who benefit the most from it don't correctly value it.
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Subconcious details do make a difference
How long it takes
Impact that good viz practices have on quality and useability of deliverables
Charting types outside of line/bar charts
Little differences ha make a chart communicate
It's under-appreciated. The visualizations are there to draw in customers and provide at-a-glance monitoring for those using the product- but it's treated only as a flashy add-on.
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How hard it is
They underestimate the amount of effort that goes into a good visualization
How hard it is
I don't care; they love it whenever it's done; it gets a lot of funding offers.
The amount of data manipulation- planning- and coding it takes to produce a custom visualization.
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It's not trivial.
Non coders don't appreciate how these visualizations are actually made
That it's more than making pretty pictures.
Still a lot of people are very naive when they hear about data visualization and feels only bar charts and pie charts are data visualizations
how good it is (should work on my self esteem)
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"Stuck in ways and ""just want to see the data"" or claim ""I'm not an analyst"""
Aesthetics are more important that functionality
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That it takes time- especially web based
That it involves a great many choices.
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How long it takes
""
Time needed to construct the right visualisation
The importance of beauth
Structure and quality of data
The blend of design and development
Difficulty in massaging data for use
It takes time! I don't press a magic button to produce visualizations.
That it could be better if I had more time for it
What's possible- what's not.
How much time it takes to do certain tweaks
""
The benefit gained from investing more time/resources into dataviz
Some upper management do not realize that good visualization cannot be done with cookie cutter solutions. They are often unique or mostly custom made. This makes implementing them into the regular data dissemination chain challenging.
That it's just as important to make a memorable data visualization as it is to make a table summarizing the data.
you can't make a pie chart without numbers just for the graphic (no- i'm not joking)
That many people are more visully oriented than text oriented. We need BOTH media to commnuicate effectively to larger audiences.
what you need as a base to get the best results
the amount of data prep/wrangling that is needed before you can do the visuals
Why
The amount of inglorious toil involved in producing the final deliverable from questionable data sets.
beautiful plots aren't an accident
""
visualization is an analysis tool- we almost exclusively use it for communication/reporting out
The effort it takes to produce good material.
That it's not just about looking pretty- but also not misrepresenting the data (and telling the story clearly).
the benefits and usefulness of investing time and effort into creating the best visualisation for the purpose
""
The underlying data structure.
It's precise info that means something- not just feel-good marketing
The caveats and assumptions behind the data
"They think it's ""pretty"" but not essential. "
it's not just making a chart. it's finding the story/insights in the data and building a visualisation to tell the story
Don't realise how long it takes
it takes time
""
That it takes a long time to organize information/content/data correctly in order to be able to make sense of it and design the most appropriate visualization.
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"It takes large blocks of uninterrupted time and attention to achieve. And- that even if I look like I'm not ""doing"" anything- or I'm taking a walk- it's to clear my mind and reflect on the work. Data work is recursive- and requires rest and reflection to be sure it progresses smoothly and in interesting directions."
""
It's limited by the quality of the underlying data.
The value of clear and simple presentation of data
The time and consideration put into things
The ability visualization has to teach
How important a good visualization is in communicating results and insights
They really really think pie charts are great.
Why it is necessary
While many like the results- many don't know the talent or imagination it takes.
That it requires expertise in concept drafting and development- data mechanics- data engineering- and design. Tools like Tableau make them think it's really easy. And if all you want to do is create a bar chart of some simple rows of data then it is.
that is trial and error based
I'm not just building Tableau. And what I build can not just be replicated in Tableau by low-cost resources.
How time consuming it is
As an ex finance professional I have a wider experience of data analusis than my immediate colleagues.
""
We're all pretty informed and get along well- actually. But our editorial process could be strengthened.
Tableau is not data visualization
Creativity
Our Data Science projects should be managed like a non-profit manages Grants.
"""Just making the chart/map this way"" isn't a great piece of feedback"
We require accurate and complete data- and time investments on their end regarding day-to-day data entry is necessary to facilitate that.
The comprehension of statatistical procedures.
Time required to produce publication quality viz- even if it is simple ones
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How much time it takes to design and build things that actually work
They confuse visualization with bar charts and scatter plots. They don't understand it's a valuable tool for analysis- complimentary to data mining and machine learning.
Excited about it not sure if they buy into it
That there is a wealth of knowledge derived from scientific studies used to guide visualization design; that visualization can require just as much expertise as something like machine learning.
The link between data visualization and design subjects.
data processing is not as fun
""
Different viz types tell different stories
That it exists and how to use it
The intricate complexity of it
The difficulty of creating programmatic day visualizations that can handle a wide variety of data
That there are not pre-canned solutions for communication. I think everyone wants me to tell them what chart to use. I feel like that is the most boring conversation in the world in viz.
""
things take time ...
That splitting the components of the visualization too much makes them hard to work with in terms of animation
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The work that goes into them
It is a collaborative effort and requires them to think more about what they are trying to say. They don't want to think about it- they just want to put it on a slide a be done with it.
achieving a simple- well-designed data viz product takes more time than a cluttered one
Like building a survey or conducting research- people assume that they can just build a dataviz and that it means exactly what they want it to mean.
the power of simplicity
requires a great deal of creative procrastination
the value proposition- and proper implementation
Effectiveness is better than illustration
The amount of time the prices takes
That it's useful
""
Many never learned basic general charting principles
That quite a bit of thought goes into the work - it isn't just slap-dash graphing.
That going from effective to professional takes way more time than you think even though only small things are being changed.
Importance of considering pre-attentive attributes
How relevant is transforming data in information and insight- and the relevance of data visualization to communicate key messages- insight and stories.
They see it as something superfluos
How much effort it is backend to create the final visualization.
How difficult it is
Time it takes to analyse data
how much effort goes into it
Most of them feel Data Visualization is just enhancing look and feel of dashboard and mobile application where as DV is all about representing data effectively.
The effort required for good work
I spend the majority of my time getting to know the data and shaping/wrangling/cleaning it. And a small portion of time vizzing.
""
Appreciation for insights when looking at multi dimensional data in a viz compared to a table. Lack of patience to understand and learn different chart types.
the added value against the traditionnal BI & excel
That data visualizations are only as useful as the data behind them.
What data viz can provide them - they think too narrowly
That anything other than a default chart is worth spending time on.
Data viz is really the only UI clients see. They don't care how we wrote out ETL or built our data mart.
Time
I'm the only employee
How good it could be compared to what the tools allow it to be
how complicated it is to line up the data and how bad the data actually is
How useful it is.
It takes time
How long it takes
The need for shared data connections
"The importance of avoiding ""fishing expeditions-"" but really having strong statistical arguments and justifications for examining data in specific ways."
That it requires expertise
it takes a lot of work- good designs while simple are not trivial to reach
effectiveness and expressiveness dimensions in the visualization design
""
That it's not (just) about cool pictures
what data viz is
""
In most cases- the data doesn't speak for itself...you have to package and present it in a way that inspires action
garbage in -> garbage out.. no matter how great a data visualization tool is- if you don't have good data- scope of a beautiful dashboard is limited
Good design takes time
That everyone needs basic data vis training
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The difficulties with the old stack; browser issues
It's just as a legit form of journalism as text and multimedia; needs time to do it right
It is not all art. There are well-known principles for how people perceive information and those principles should be followed- in general
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It’s based on their needs and use-cases- they need to communicate what they want/will be using this for.
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They are accounts- ie I have had to explain weighted averages
People might be stuck in conventional ways of thinking.
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Other people are lacking in the skills or interest (or both) to create good data stories.
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The importance of non-static data visualizations. Allowing data viz experts to lead data exploration process.
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Don't understand that data viz is a narrative
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that it is important to pay attention to details
some see in visualization pretty pictures and do not acknowledge the design- engineering and research involved
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Design is just a small part of datavis engineering
It took me some time to teach this so it's not true anymore- but the length of time it takes to develop an effective visualization was severely under-appreciated at first.
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I'm a programmer among journalists: they have no clue
That it's more than about making it pretty- and that every component should have a purpose. Also that it's highly exploratory work- in trying to understand the data and how to visualize it.
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The time involved for creative- effective visualizations
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Main focus on data science- exploration or navigstion of data is not perceived as useful
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They do not understand that data-based research and visualization is an iterative process that requires many rounds of discussion and revision.
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The simplicity of messages you can transport with data vis coméared to tables/text
Theyre not exposed to analytics or data visualization so they dont see much value in it
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How much time it takes to prep data.
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Simple is hard to do.
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How much more potential there is for communicating insights about our data through visualization(s)!
It's our job to demo the value to them.
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Importance
Importance
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It's usefulness through interactivity
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It is a specialised job
Need for data engineering
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Importance of clearly consumed data in timely manner
Where data comes from and how it is handled en masse by string-searches for reporting and percentages.
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The value of customization for each visual.
the level of complexity behind the visualisation
How many data visualizations they interact with on a daily basis
How demanding it is keeping up with changing tools and standards
Why it matters
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The complexity in creating the infographics
"Magic ""just happens"" apparently"
I think they understand the importance- but resources for beefing up the team are nonexistent.
The absurd amount of time it takes to prep the data
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Need for visualization
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How much time it takes to create good visuals.
It can drive the story not just support it
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It's ok to make a visualization that takes more than a second to understand
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It takes time to figure out the best way to present information.
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That we can use it to analyze data not just summarize
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People think it's an afterthought
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Data prep
The grammar of graphics
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data visualization's full value
That text tables are not the answer
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How much work it is to prepare data extracts to power the dashboards
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Visual thinking
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It is math. I can't just get different results.
sometimes they don't see the potential of visualization to explore data- they just see static images
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Don't realize the full stack of collect/integration/engineering before plotting result
The difficulty to create them and the thinking behind design decisions
The importance of real / live data instead of a mock or sample data set.
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don't met the problem yet- as my project is a work in progress
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They don't understand the reasoning for visualizations
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Understanding the complexity of making it work
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Value in the data and usability vs. aesthetic.
How much time it takes to prepare the data before doing the analysis and then the time taken to present the results in a coherent self-documenting and reproducible manner (I use Knitr and/or RMarkdown to produce reports).
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It's not as easy as it looks.
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Fast knowledge
dataVis development is a design process- and the same as with any design process- you don't know the kind of results you will get before doing the work
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time it takes to design something if the problem is complex
I think they understand & value- but its not primary focus of output
The amount of effort required to make something simple is often greater than that required to make the eye-catching flashy thing -- and that the effort is worth it.
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How complex and difficult it is.
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Complexity
They don't understand the power of a picture and are still hung up on spreadsheets to communicate all information.
That there is potential for self-service work with a good foundation
my lack of design of data vis
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Visualization can make or break an argument (when done right or wrong)...
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Takes time- collaboration and iteration. Not a simple one step process.
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Complexity
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The difference between my plots which are honest and others' which can be LIES
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Visualization is as important as tabular display
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Literally everything
My organization gets it; external consumers are the bigger problem.
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that it's not about knocking down features or making things look pretty- but getting it right. many get that though
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time spent carrying them out
they think I'm just working on simple line charts
"The effort and exploration required to establish and discover ""the right visualisation"". "
it actually requires skill- and not just a click-of-a-button skill
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Stats/methodological rigor comes first- visualization second (sort of!)
They only understand the value after I finish the deliverable. They can not even begin to imagine how to work more efficiently with data.
That basic Excel is not the answer to problems.
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Appropriate types of charts for appropriate data- appropriate set of colors
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"That I do not design charts in a certain the way just because ""I like it"" that way or ""I feel like this today"" but my design decisions are based on studies of visual perception."
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It's potential to be applied.
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they still think pie charts are useful and just don't understand there are better ways of presenting so that it's easy to understand.
amount of fine tuning that goes into effective visualization
For some a bar chart is just a bar chart and they don't care about trying to tell a data story
It's time consuming
Don't start with a chart type in mind. Start with what info you want to show and figure out the best visual representation.
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They only like paid for ESRI products
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That most colleagues believe it is the last step (only communicating data) instead of using visualization to explore the data
How it could be used if engaged
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How difficult and time consuming it is.
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People hardly get that numbers are just the start. I work to make things more understandable- but you need to act on the results in order to change things.
It takes time to make them good
They find a cool visualisation in some random place in internet and tell you that it would be cool to have that in our site- without really thinking about the implications (our data may not fit- the data scale may not fit- etc). It often happens that they see an static infographic design and think that it is possible to create a full web component that can create the same visualisation on any data you put there.
design = decoration .....
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Complexity
it can produce more questions than it answers and that's good too
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don't realize or acknowledge the importance of communicating clearly and effectively
many think more is more- and tend to over complicate pages or leave out context. Our goal is to have the right amount of data and context.
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how much goes into just getting the information into one place
They think they are experts
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The time required to do the work
Consuming product not at their level
They get more than me :-)
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The data complexity that underlies it
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That DV can speed understanding of relationships and show issues stacks of numbers hide.
Visual literacy and appreciation
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value of exploring variety
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That if they tried they could create great visuals and not the garbage that i see all over the place.
that a good dashboard doesn't just happen
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I'm self-employed.
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How much better it is compared than tables for communication.
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Work is well respected.
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Building the foundation is more important than the bar chart you build on top of it.
why it's worth the effort
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The amount of work dedicated to data cleanup
the importance of understanding the data
Things are more ambiguous than is convenient for reports and presentations
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Thinking outside the box
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Stories matter as much as data
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Attention to details
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Design
"How long it takes to make the graph ""just"" right."
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How long it takes
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resistance to change
How long it takes to cleanse and model the data
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How much effort goes into making something that is easy to understand: just because it's presented in a simple way does not mean it was simple to produce.
Data are valuable information- design is the key to show them- not to fill the holes they leave in their product (i.e. newspaper and news site pages)
Data visualization (especially infographics) takes understanding and analysis on the designer's part to make it communicate effectively.
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The time it takes to get data in the correct format + trial/error nature of data visualization
Don't understand the amount of data preparation required
The inherent value of effective presentations
In the research institute- where I work everybody is so smart- it is quite hard to surprise anyone. However the most recent project is extremely complicated... but our job is to make it easy to understand to anyone.
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Structuring the data and the benefits of built in functionality
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Not always sure how to use the information provided.
The use of symbol size/color differentials as effective or ineffective due to slightly different sizes/shapes/colors.
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it's more about concepts behind visuals like distribution- percentiles and density.
They often only see the visualization or results and don't understand the data collection and organizational culture/structure needed to support it.
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time to produce work- data knowledge required
They don't understand the importance of reporting honestly- without intentionally or unintentionally misrepresenting data. Many see it as only something in service of the marketing dept.
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viz is not just about standard business dashboard stuff
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simplicity is key
simplicity is key
How much time it takes.
Too many don't understand statistics and how data visualization models can be misleading
the time it takes for a good visualization
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Effort required to produce each visualisation
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How much time it takes- how many choices must be made along the way
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What it takes to get data that communicates the project's message
How it helps. How long it takes to do the work. The high level of skill- everyone thinks because some things are done quickly it must be super easy.
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You really have to understand the data- to effectively visualize
How many people and how much work it takes to produce
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the critical importance of design at multiple levels: information hierarchy- UX and interaction- encodings/colors/etc- and aesthetics
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Time and skill involved in putting together viz that communicates clearly and the cognitive factors of consuming data via visualization
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More infographics- less static charts.
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"that it isn't simply ""beautifying"" data"
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Time-consuming
Making it look simple means that it is very complicated underneath
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that it's really not exciting
they write long reports that can just be summarized in pictures and maps
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That the data is impartial and sometimes shows trends that make people uncomfortable.
the complxity
Don't realize the value of visualization- thought visualization are just graphs- charts. Something nice to have- but not really useful.
the complexity and time spending in developing the vis
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It takes time
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They think in terms of Excel
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The complexity
They don't see the business value of it
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The output of our work is quite brief and simple by intention- but the effort it takes to cut down details and spurious facets of the data can be missed by people not directly involved in data work.
It's not magic- I'm not a wizard. Just did the same things enough times until I got good at them
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How much time it takes to create a dashboard in R.
Level of effort- complexities
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"That it is not easy. That much work and thought and effort was put into one ""simple"" chart."
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Data visualization need to tell a story- not just plot a bunch of numbers
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That it's more effective than a spreadsheet for quickly gleaning insights
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R scripting
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A visually driven story can be a very clear and compelling story.
How hard it is to handle dynamic data
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That it's more complicated than you think. Half of the time we devote is towards data validation/prep rather than design.
Reporting Requirements are means to an end no the end in itself
Input from the global datavis community
why it matters
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dedication
In my organization and large part of the country they relate data visualization to IT - Information Technology
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There are more valuable visualizations rather than pie and bar charts
That it takes time to build and especially with custom D3 vis' we need to have a steady and clear design for the vis very early on.
Limited exposure
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How complex it could be to generate a nice visualization
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how each tiny little thing appearing in a data viz have been thought
It's importance in conveying information
That data clean-up is 3/4 of the work. Also- that you have to make data visualization decisions and think through the story you are tryng to tell before starting to design and execute a visualization. Its like building a building- you don't start laying bricks until you have designed the building.
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That there is value in making the data more easily human consumable.
The amount of data prep/cleaning that goes into creating a single visualization. My data visualizations are not scalable because each one is a manual process often built from a static dataset that does not update in real time. Oftentimes- consumers believe that since I've done it before- I can just do it again on a different dataset in a matter of hours.
They do- they just need to open it first.
They don't get the biggest benefits of data visualization.
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That data visualization is important- it's not just for aesthetics
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there isn't a magic design button
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They don't know how to analyze basic data sets- and how to use basic visualization tools
The amount of backend work that goes into creating
Not enough passion or curisority
How hard the preparation step is
We can make this file beautiful and searchable if this error is corrected: No tabs found in this TSV file in line 0.
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Less pontificating and more application to the real world.
Effective executive level visualization
Theory & principles
More about effective interactive/dynamic dashboards
more methodology sharing rather than just the final visualization- i.e. Propublica Nerds page or Data Stories podcast
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Would like access to training in coding. But generally- there's plenty of great stuff out there -- need time off to expand- improve my skills.
How to work together on projects.
Better resources to for sharing and tracking our work
Share as many learnings as possible
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Confident come backs from intense meetings where leadership tried to overturn a wise visualization decision in favor of something worse (ex. a palette that isn't color blind friendly)
more on how to create specific data visualizations in various tools/apps/languages. etl is also a blind area there should be a set group of learnings/skills for etl cwork.
data viz for mobile devices
Science based statements
have them call the stakeholders to commit to use the data as presented and learn more about what it contains
"how to counteract the old wives tale that ""figures lie and liars figure"""
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More advocacy for data visualization specialists to be taken seriously among other scientists. Climate scientists- biologists- and engineers are unlikely to listen to data visualizers on design- color- etc. We can bridge that gap to help convey data visualization as an evidence-based discipline.
How to accomplish specific tasks
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More detailed guides about where to start with data science and visualization -- classes- books- software advice
Continue embracing R visualizations and data manipulation
More in-person workshops/training/meetups for beginners where people of all skill levels could come to talk through ideas and work on projects with help from peers or mentors.
Conferences in Ireland
More entry level tutorials on coding specifically for dataviz would be welcome
more thoughts about what role data vis should play
More on how to create a design oriented culture
functional/interactive domain specific dataviz where usability/use value trumps speed of understanding (how quickly dataviz can be grokked)
Love more on the thought process behind the vis
I'd love to see more region-specific advice i.e Canadian specific. Also- being able to reach out to someone with a viz question would be awesome.
Less technical (here's a trick in this software) and more research about reading and understanding data viz.
More examples of advanced data visualization where they walk through code
Making research on infographics scientific so I can present them as facts for my scientific audience
More Science and research into the field
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move forward data vis literacy.
downloadable examples for tweaking or quick guides on aesthetic tips
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I would like them to engage- rather than just post something and not respond to comments and questions
I'd like to see them be transparent in their learning process and short-comings.
More applied research with business decision makers on what types/approaches/techniques work better (or don't work at all)
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more focus on exploratory data analysis and the use of visualization
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More learning sessions.
More story telling- capturing the value of analysis
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What are tool-agnostic habits that develop more effective data storytelling
How to overcome enterprise issues with data preparation and enable analysts at scale to focus more on analysis than data prep
I love MakeoverMonday- TableauTipTuesday- and the like and more of that on various platforms would be great
More support for non-traditional analysts.
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less rigidness- allowing data vis to be applied differently depending on context
More how to design workshops
Formal review on weekly basis of results
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More diversity in terms of perspectives and less absolutism
More discussion on what people/analysts/domain experts in industry can understand. I often see situations where techniques are theoretically better but are not understood/appreciated in practice.
How to measure impact
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Better getting started tutorials- this is all very overwhelming to a noob developer.
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The evolution of visualizations in corporate messaging.
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Communicating about the benefits of data viz- especially for organizations
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"I'd like to see a ""method"" for developing a data visualization that encourages collaboration. What should we be saying and asking to create a collaborative work space?"
I'm keenly interested in machine learning for autonomous visualization. I also want to break out of the idea that visualization is an end product.
"Everyone- thank you for the informative and inspirational pieces describing our industry. There're an incredible number of articles- interviews- podcasts- and meta-visualizations available to anyone interested in the job. That said- please share your negative or difficult data viz career experiences as well. You can be anonymous! For many data viz practitioners the market is dismissive and ignorant (even hostile) but positivity is the constant byword of the community. Consider a struggling artist or actor; they have access to leagues better advice than ""just work harder and wait for your inevitable big break"". Our beginners do not have a clear career path and it is very easy to lose the plot."
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Step by step of making choices- not just technique
Best/new ways to present data- exploration of tools
Access to training- seminars and conferences.
How to visualize or even standardize visualization of information on humanitarian needs
novel ways to use out of the box tools
Examples of real world impact
Where data visualization is currently situated with regard to its context and how we can support and promote further interdisciplinary data viz.
"Deep dives into non-traditional viz roles. Like ""How can we use dataviz to improve communication in industry X""."
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More support for open source developers.
more public examples of using data viz for actionable analysis of data
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Intro sessions to new tools with a step-by-step walk through. Break it down to be really basic and get me setup.
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Less on visualizing data- more on cleaning and munging data for useful visualization
Better documentation
Presenting data
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simplification of collection from public sites
An understanding of the limitations of public sector work and strategies for overcoming these limitations
Discussion about complexity- colour and mixing different approaches
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Practical examples/How-tos and whys
tutorials- knowledge sharing
Establishing norms and tools that support those norms.
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Communicating with leaders
Data/viz literacy
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Multimodal data storytelling skills as a tenet of the data visualization professsional. Ability to combine visualizations with narrative.
Continuing what to do. I've learned a lot from the community
More step by step guides to setting up visualisation platforms and integrating different tools.
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is there any broad survey / tutorial that describes everything one might want to learn- from wireframing to typography to D3 to color theory etc etc? that would be nice.
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I love all the examples you give
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Visualization tools available to non-profits or donation of time from data visualization experts to assist non-profits utilize their data better
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convince other departments (sales teams etc) of its importance
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*constructive* scientific critique
explanations of why they decided to use a particular tool/how to make that call
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Make this necessity more clear and evident
Articles/books on managing complexity- expert interfaces and dashboard design. Combining UX and data vis.
Focus on the ability for better data viz to make better decisions
How to measure data literacy
Thoughtful discussion (rather than competitive debate). I find the idea of putting others' work down to make your own look better is off-putting (e.g. difference between Stephanie Evergreen's posts that are ADDITIVE to the knowledge sphere rather than DESTRUCTIVE e.g. Policy Viz posts or Robert Kosara tweets)
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More opportunities to practice and collaborate- beyond just focusing on learning about skills- tools and processes
data engineering
"Well- most should ask themselves: ""What question am i trying to answer"" and pick a viz- not the other way around (unless question is unknown and exploration is the objective)"
Examples of when data visualization made a difference in projects
More collaboration with designs- ideas- etc...
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How to deal with clients and their ideas of data visualization- analytics dashboard and other complex tool. Dataviz is not only charting but also creating the entire complex application that exist behind. A bl.ocks.org page doesn't represent data visualization but only a small part- a test- an example. there is much more
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More online presentations
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Improving the impact of graphics
MOOC courses on data visualisation with Perl 6 desperately.
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Increased dashboard capabilities in Shiny
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Systematic research on meaningful visualizations
prospects for integration of web-graphics with spatial data
More tutorials- advice on best practices.
value
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Reproductible examples
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open source being a valid option
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"No pretentious TedTalk-type garbage. Discussions re: how non-data people (see: ""normal"") people best absorb different types of information. I want to blow people's minds- but more than that- I want to effectively communicate with visuals."
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Real life case studies.
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More meetups
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Easier ways to build interactivity and modify it to fit purpose
Spread the message!
"Mechanism to communicate iterative process to arrive at a result. For example ""better"" notebooks which allow the visualization of the process without cluttering the main story."
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Interactive client-side visualizations from ggplot2
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Course material incorporated into curriculum! I would have loved a dedicated data visualization course- rather than needing to learn these tools (which are part of the job) on my own time.
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Opportunities for non computer scientists to learn more about how to create their own visualizations.
Short tutorials on how to get started
How to improve the status of the field and change mindsets of data visualization consumers to recognize they need to invest more time and money into good visualizations.
Leave them be. Don't bother them by asking for advice.
How to have upward mobility in the data viz field. How make your 'spaghetti code' neater.
Blogs.
More effort integrating the tools to the designer's workflow
How to build a successful data visualization team? Strategy for finding projects that create impact and how to insert ourselves into other teams workflow? How to effectively build new interesting projects and maintain multiple systems from the past?
More research in communication
More examples of the full pipeline of work from collection to cleaning and integrating different tools like ggplot and illustrator
The ethical and civic responsibilities that need to be considered when collecting- handling or visualizing social data. Although humans play a large role in creating data and are often the subjects of data. Visualization- data science and sociological research are often discussed separately
next generation tools and technologies
I use a Slack channel with leaders in the security data viz field extensively for help in R and visualization. I'd like to see that sort of leadership available to others.
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"More universality- interoperability and pluggability! Stop competing for ""top app""!!"
what does the future look like; not just the future you're working on
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What makes an effective visualization team? What unsolved questions do practitioners want researchers to answer? (i.e.- what should researchers focus on? What should visualization researchers focus less on? How can researchers better communicate our findings to practitioners?
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Strategies for educating the audience about data visualization and why certain choices are made.
"More information on how to navigate the ""soft skill"" issues that come with visualization practice (e.g.- finding data- dealing with the reality that most people don't want anything more complicated than a bar chart- etc.)"
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Balancing tool making with customization.
How to publish more effectively
How to teach staff how to use new tools and be interested in data (amongst non-experts)
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"more about how to create culture change within orgs that are not already ""there"""
"Keep on the mission to make Data Visualisation the ""normal"" way analytics is done"
Less quantity more quality of output. But discussions and polite & interesting exchange are already at a pretty high level
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Designers/engineers making more tools for the masses without the need for coding.
Connecting the experimental to the practical.
how to make clients not dumb
Where more work needs to be done and published to move the field forward.
Proven technology stacks
More focus and discussion around accessibility (including screenreaders- automatic caption/descriptor generation). This includes accessibility gaps caused by education/knowledge- where many people don't have the skills to accurately read/understand dataviz- especially when clear metadata is divorced from the resulting viz. It's driving towards a world where people thing data is definitive- rather than subject to limitations and biases like anything else.
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More examples of how to answer a question with data viz
Guidance on creating enterprise data visualizations- i.e. building a suite of interactive data visualizations that share the same data and coexist in the same application. Guidance on modular design principles.
Have proper forums to easily find partners and validate new visualizations
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How the data visualizations were used throughout their company and the different questions they answered
Not sure what I should be working on now to keep ahead of the curve in the future. will vega put us all out work??
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More fundamentals of design
Show confidence in security of open source software libraries
Process of design
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Documentation and examples
More explanations of how they did really cool things
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Comparison charts of free offerings that are written by a user vs cursory reviewer
Spread awareness about the importance of the art of data visulaization
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More insight on the failures of real-world projects; simple pie charts are meaningless.
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Consolidation of visualization methods/tools
Show what type of data visualizations work the best for different goals/type of data
Concerted effort to move 2D SVG visualizations (D3.js) to hardware accelerated or WebGL renderers.
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More example applications
How to do vis with data- not just random numbers calculated
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Thoughts on how Data Visualization complements other research/analysis roles
More complete samples
Provide good better and precise documentation.
debunk visualizations myths.
Feedback on what we produce
More practical online- structured courses
More creation of shareable tools- if that's possible?
I'm mostly interested in cutting edge designs and tools- esp. highly interactive (or customized/responsive) and dynamic things- as well as machine optimization for layout etc. (ie. the opposite of a D3/ggplot made- Illustrator finalized static thing); but I also liked most other topics from dataviz podcasts through Little of dataviz to slightly zany experiments from... khm... I think you know
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More about creative uses of data and visualization
Combining visualization with advanced analytics (machine learning/ data mining/ statistics)
A better communication and follow up from Microsoft team in regards to Power BI Visuals it has been poor so far
more examples of complex story telling- techniques that can be employed
Advice on how presentation relates to communication of ideas.
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The strategic advantages of data viz. A data viz syllabus so the knowledge is well structured and formalized.
"Like is now more common in UX/Web design- how can I create measurable ""factors of success"" for a data visualization? How can I show them that the visualization works and how well"
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I do a lot of JS. I wish I could learn more about developing robust applications. Too often tutorials seem to end at the produced visualization. But how to create a changeable- interactive visualization- cleanly structured and coded?
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"Getting beyond the d3 syntax stuff- and into frameworks (css/html) to help build pages/dashboards. I mean lightweight stuff- not flavor of the week shit (react- angular- reflux- redux- and whatever $sexy company might be using). I mean stuff like a lightweight (no J query/minimal js) bootstrap etc. Nice to have a way to instantiate widgets and attach callbacks.
Basically-more support around the stuff that's around the svg/canvas dom element."
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More discussion of workflows instead of just tools- promoting visualization for exploratory analysis instead of just presentation
More about structuring/feeding data for visualizations and more about good design practices for data vis based web applications.
how to do nonstandard vis- making better supported
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More process articles than viz critique
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Actually the community is pretty great at the thing I really value- which is knowledge sharing.
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business use cases over news/entertainment
Design- web dev best practices
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How to leverage an array of tools from BI to custom to create a suite of deliverable types my team can build
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time they spend and how the put cost to creations
More advice in general. The leaders feel unapproachable- especially if one isn't a pro.
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increased doc is helpful- tutorials are great
how to make a case for a dedicated data viz position- and what those positions look like
Methodology issues
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Higher level tools for collaborative interactive datavis design and construction.
D3 and other visualization library integration with modern front end technologies
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Anything to everything related to design and development
more focus on real-world problems- less hype- more honesty about the complicity of datavis in hyping the 'big data will solve everything' narrative
sharing good practices
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Continue to share and lead
Evidence-based analysis and critique of visualization techniques
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case descriptions- process descriptions- behind the scenes look etc. are always best
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Better networking opportunities to interact with other data viz professionals.
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more on meaningful exploratory visualizations (where immediate interpretation is not the most important thing)
Design and technical inspiration.
Skill tutorials
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How to do quantitative analysis as well as design - together
More technically detailed blogposts
Creative ideas
A larger focus on visualizing socially relevant data -- less time spent building widgets that are beautiful/funny/intriguing but detached from the current political context.
Moving from beginning to intermediate
Data visualization for hospital & healthcare professionals
More things like Nadieh and Shirley are doing with explaining the development process
What technologies to adopt.
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Pushing the importance of data visualizations to consulting companies
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How data visualizations can lead to actionable results
excel tips
Educational opportunities concentration on data visualization for young people inside and outside of schools
Loving the discussions about the quality / usefulness of various different types of visualisations- really appreciate seeing new ways to represent / communicate data- and the discussions of various tools are great too
How to get started- how to get inspired- how to build up your skillset- examples
Step-by-step.
Strategies for justifying value of data vis specialists to management.
more info on learning d3
How to adapt visualizations for non data people
a community to access- listserv? common webpage? I don't know of something like this
Books
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how to make data visualisation a first class citizen within an organisation
"discussions on the ""supernova"" of change we are seeing more in the foreground"
"How to structure your data and guidelines for how ""big"" data can get before models need to be reworked. "
How to prep data effectively
How to be creative and get better and choosing what type of graphic suits your data
Self-teaching tools. Marketing-specific data viz. What non-data executives want to see from a data viz staff person.
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Everything is good I suppose- no need for big changes
More talks at technology/design/dev conferences- more focus on how to make visualizations accessible- and how to deal with complex visualizations on responsive/mobile platforms.
more research and experiment articles rather than implementing or applying libraries- because for me- I'm in a large tech company so it'll be light years (in tech years) before I can implement any of that in my day job.
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How to explain things to folks with the math / data viz intelligence of a two-year-old
Already have so much. Couldn't ask for more.
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Start looking at VR and AR data visualization seriously
How to focus on why data visualization is important and the impact it can have
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Education aimed at managers that require them to explain how visualization helps (or not) so they understand their own biases. Secondary; examples and case studies.
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how do you charge your clients being freelance? hourly? by project?
How to bring data and design together from a teaming standpoint
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Understanding new tools / frameworks - and matching best toolsets to requirements.
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Transparency about how ther teams are organized- how projects are reported and edited.
Using for more ethical and social projects
Sharing methods and experience (rises and pitfalls)
I love recipes. And series that start at beginner and progress through more advanced topics.
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More visualization solutions that are geared towards changing data- rather than a finalized data set- with a finite # of subsections.
Implement as a real career- yet from distinct backgrounds
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"More published analysis- discussion about how visualization helps communicate- less time spent on ""making pretty things"" for their own sake "
"Advice on how to ""sell"" data visualization to customers + to research funding agencies."
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In my experience- most people already see the value of visualization- but I would like to hear advice on how to communicate to people that visualization is a deep and broad field; it's more complex and requires more skill than simply copying examples from D3's website and pasting your data into them.
Discussions about the expresive role of data visualization and its effect on people
Share more use cases for frameworks startups and big companies are doing for data viz for internal or external customers.
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Remakes
"Integrating data visualisation into the core of application designs- deprecating stilted ""UX/UI"" ""Research"" and ""Testing"" based on a terrible initial selection of interaction patterns."
More meetups
Perhaps more collaborative and volunteer opportunities for data vis
i wish we discussed the merits of more difficult to measure metrics on visualizations impact on perception- attention and behavior. If I hear about pie charts and angles one more time I'll die.
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How to migrate between versions
Helping designers use tools to produce data vis
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tips/advice - I love Stephanie Evergreen's newsletter
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more that involves map design and innovative ways to display geographic data other than the choropleth map
What mix of data science/data engineering/statistics are most desirable and marketable.
"guides for workshopping / requirements gathering. more focus on the ""meat and potatoes"" types of dashboards and vizes that most people spend 80 of their time on- instead of peripheral data sets and problems that people can't relate to"
how to overcome lack of creativity/productivity
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Situation evaluation
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Public education on data literacy.
How can you become a part of the community- especially if you're not good with twitter?
Lessons learned after dashboards/visuals implementation- usage etc
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Best practices- challenges with provided feedback-
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where can i find people that truly understand the art/science of data viz not just the tools
Lead by examples.Most of the thought leaders talk about DV and everybody appreciates importance of it but there very less visualization or dashboard- BI apps examples which shows a perfect balance between UI and DV and what works well from end user preservative.
Better tutorials on how to deliver and deploy interactive charts
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More on dashboards
Platform agnostic and focus on effective and impactful viz.
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I like to see samples and walkthroughs of different methods used to create solutions
A crucial topic is how to get an audience to understand and make the most of a given data visualization
Ideas
More on exploratory data viz and how to do this in large organizations (provide tools- teach others)
More courses
How your skill wider community to data viz
why people save data in PDF
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emphasis on using data visualizations for data exploration and analysis
how to communicate effectively
Sharing work- how to develop viz as teams
Center of Excellence discussions
More discussion of the ethics of the improper presentation of data. How bad vis can actually impact decision-making and policy for the worse.
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seriously reflecting on how to integrate vis with machine learning
more discussion about the relationship between Machine Learning and Visualization
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Welcome people into vis community- especially people for whom vis will not be their primary focus.
Frontiers of data viz
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I would love to hear more from other entrepreneurs/freelancers about the business and operations side of things
Best way to deliver predictive analysis findings- we have learned this along the way through trial and error but I think others will benefit from this (not only about the predictive piece but also about the bottom line to the audience)
Data viz for social good
The hard problems: conveying uncertainty- dealing with crappy data- high dimensional data- accessibility in vis- graphs- maps- vis education at all levels. I would like to see more bake offs between existing tools and libraries. I would like
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Maintaining old code
how to deal with difficult clients- how to convince decision makers value of custom visualizations that are resource intensive
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Tips for beginners
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More advice on the basics for js style libraries - core js
That our field is still very fresh and a lot of it is still undefined
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I like blogs- especially those outlining thought processes
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how to run projects in networks of diverse but complementary professionals
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Programming patterns to share charts- integration layer- data layer
More meetups in the Bay Area (but if we're being totally honest: the meetup would have to occur within a few blocks of my home in Oakland since I have a 5 yr old kid and my social capabilities are limited).
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How to communicate a new visualization idea to general public
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Less tweets that says 'Amazing' in it and way more critical thinking.
Dataviz should be part of the entire data procrssing activity- so kuch attention to integration with data science and design.
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Discussion and knowledge sharing
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ideas on which graphs to make and how step-to-step
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How to promote the value of
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Case studies!
Success stories
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Importance
Importance
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More amateur help/college level courses
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More open free knowledge
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Open-source bi
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Best practices in all forms
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How to publicize work better
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What are business wins they have seen and/or been part of
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More open support of public data
Examples. Workflows. Ideas
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"There's too much focus on machine learning and not enough focus on actual business applications and usage. it feels like a lot of ""procrastination through preparation"". As data science skills diffuse into other professions- the ability to influence business decisions and apply domain knowledge are going to be what keeps people employed. "
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The fundamentals but also uncoventionsl types of visualizatoond
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How to use different visuals
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CAO
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where data visualization is headed in the future
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Opening up the mind
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Data science and visualization is more than just right tool
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Less dogmatism about visualization techniques and more openness about the really hard parts of our job: getting the visualizations into the hand of our users.
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interdisciplinary research is needed- atm everybody looks into his/her niche
The opportunity to share
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Parsehub
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more advanced level training
I prefer the current open software approach where Data Vis examples are shared on blogs- GitHub- etc. and everyone can learn from them and have opportunity to connect with designers.
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Exchange knowledge- open the process
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How to evaluate which tools are right for a job
paper prototype with designers
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Integrating knowledge and experience into high school curricula (where accepted) to strengthen the understanding and appreciation of th trade.
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"more details about the different jobs in data visualization sector
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Guidelines on how to measure the effectiveness of particular visualizations.
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Thought process
Advice on sorting through thicket of available tools
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more discussion about use of datavis in corporate settings. I don't care about d3 or tableau or adhoc projects.
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convergence of too many tools
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Easy high level language with primitives to create custom charts
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I want to see them stop talking about individual charts and start talking about the whole package of how to build a very large page report (and maintaining/updating it each month/quarter/year/whatever) that includes a very large number of charts with lots of explanatory text.
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Good examples of datasets to learn new coding languages in
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They give many valuable support on the theory side- but less in the actual process
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A recommended roadmap to learn technologies.
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Case studies. I am especially interested in understanding the impact that data visualization had on a specific case. What changed after people used your data visualization? Did you inspire them to do something different?
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how much working with industry experts is necessary to guide how you interpret- analyze and present the data
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best tools to collect/prep/unify data from multiple sources
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Didn't get the idea of the question
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Less alphabet jargon to the uninitiated
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visions for data visualization tools- processes- and presentation in 10 years
More tips and tricks for beginners.
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how to allow complex questions through a simple interface
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Anything practical
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How to traverse corporate political landscape as a data engineer/analyst/scientist.
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how data visualization is not magic- but it does require work
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more transparency on roles- pay- and responsibilities
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Acknowledge and understand the gap between data viz theory and real life pressures to get reports out.
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Tutorials
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On evolution of data-conscious organization
Clear examples of data management structures AND HOW THEY RELATE TO DATA VISUALISATION; simple- consistent paradigms and examples of general good practice.
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More guidance on communicating with non-technical consumers of the data visualisations.
Interest in public information
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Engagement metrics for different presentation strategies
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Innovative ideas on complex statistical graphs
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an ability to discuss data visualization needs and concerns in human terms.
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How to get leadership buy-in to develop more data-minded culture- how to start grassroots data analysis and visualization movement- how to leverage free or low cost tools and trainings.
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"More stories about ""how to get started/get into dataviz"" - creating a playbook for acquiring the necessary skills and intuition- much like how people talk about breaking into software engineering and data science"
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professional development opprtunities
Basics to learn new tools like R
Basics to learn new tools like R
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online forums and webinars
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Data viz is a powerful and efficient tool when engaged in community-based work with a large number of diverse stakeholders. I would love to see the nonprofit/community-based sector recognized as an important audience who can use data viz for social change - while acknowledging the funding limitations that interfere with data viz execution.
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Pay is low in my field (government). It's just not recognized as vital.
Best approaches to getting started
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Advice on demonstrating the strategic value of investing in data visualization
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continued focus on storytelling
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"Accessible guidance on new technologies and tools that are / could be useful for data visualization
Also discussions on how people's background influence their approach (style/method/goal/format/...) when producing/designing data visualizations"
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Additional opportunities for learning
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how to make data viz economically sustainable
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communities for each tool- very repsonsive forums for problems
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How to make a vis that tells 1000 words
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More tutorials and how-to
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More advocacy of open source
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Best practices.
Future of open source- predictive
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More help in how to think about abstracting the results of your very much detailed work.
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understand the data before visualisation
Storytelling advice
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Crash-courses in mathematical principles that are generally assumed
Open to any type of advice.
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data analytics communities
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monthly columns
Emphasize on the importance to make data visualization stand as a worldwide recognized discipline
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tips on their best visualizations
More advertising of our job
Easy and innovative ways
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Best visualization format guidelines
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Help networking and finding jobs
Discussion on how data journalism (story telling to reveal surprising insights) is different from dashboarding (data pulse to quickly grasp vitals of the business). More advice on designing for mobile experiences.
Greater visability
Skill improvement
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more explanatory articles rather than lots of poorly documented example code.
Ethics in data visualization
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be more critical
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More tutorials on using different tools for data visulaization
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What's the future of this line of work? How to build a sustainable and intellect challenging career.
How to keep long term background projects organized and moving
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Tooling
More maps!
We'd buy RStudio Connect :-)
Stronger analysts as user base
New data architecture
More UX/UI effort in our engineering teams.
more respect
Get more of the trivial stuff automatized to free up time for more important work.
I'd work in a team.
Harder deadlines for projects- more medium-length stories
More resources- more specific roles in the team
I wouldn't have to chase money and could just find- analyze and visualize interesting data to make the world a better place.
More time to study and experiment with new learnings.
add manpower.
doing more data viz
Have more open data projects
I would use more machine learning- use dashboards to better explore the findings
expectations of time required to create an effective visualization
Clearer vision for our work
Work remotely 100 of the time
More coding resources
Delegate more.
Add more data-focused employees
Have a centralized analytics team
More on-the-job- hands on trainings on new tools- so I can learn by doing.
Work closer with knowledgeable graphic designers
Expanding the dataviz team to make space for more sophisticated viz
better co-workers :D
Getting data that is already formatted the way you want
work as part of larger team
Simplify the process. Less trend more fundamentals.
More opportunities to do education through demonstration with colleagues.
Teaching me JavaScript- design- and advanced data visualization
More pay
Better pay
Change the process such that we think about data and data vis at the same time
More time for experimentation- design- and polishing
Have more data engineering support.
add more data science and analysis to the process.
able to do some data science in my role
Data aggregation to be made easier
Access to a peer group to share ideas
I'd prefer to do a more even mixture of data visualization delivery and advising/training/thought leadership.
More self-sufficiency of research- stronger uptake of analytical outputs
Getting a studio space
more focus on data knowledge
More staff to free up my time to concentrate more strategically (rather than fire-fighting)
More training.
Available time
Do more storytelling with data
Higher wage
For people to embrace graphs and leave tables of numbers and their own Excel work behind
I would prefer to have a data analyst job titleMo' money- mo' money- mo' money."" Uncle Phil "
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More support for data prep so I could focus on data vis design.
Make it only about analysis
More time to work on communication
More freedom
More time for research
More data visualization
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Faster feedback loops and better tools enabling more time to be spent in the design phase rather than cleaning/analysis/execution.
More data!
More work time to learn
more appreciative clients
higher pay
"Many people think of me as someone they can ask to ""grab data""; mainly because they don't fully understand what I can do to help them. So I'm creating some scroll stories to show them what I can do."
More devs
To convince senior leadership that data viz is critical to our product.
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Add sandbox environment for tooling
Network of peers to bounce questions off
Easier access to the data that I need.
Salary :) and more time/awareness/training on data visualization
I'd do data viz 100
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higher pay
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Make it easier for others to create viz
Pay more / fewer hours
fewer meetings
Greater access to more powerful tools and how to use them.
the readers
data definitions
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A larger team to help instill best practices- additional training for business users- and advanced analysis.
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More time to improve skills- more feedback from consumers
Better source data
more package upgrades that break code
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My salary!
Better access to clean data.
Better data
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Less ETL
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Better tools with better guidance.
Better data sources
More time to focus
Spend more time visualizing data and less time cleaning it
Remove 50 of data viz- then improve the other half.
More time to create unique projects.
Hire more people
Less data prep. More data visualisation.
Administrivialities: get rid of them
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More dedication to my interests
Do more data visualization work
For people to understand more about how much work and thought is behind making usable graphs- illustrations and apps.
I'd like to focus more on data viz work.
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it would be nice to be part of a larger dedicated team
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Contribute more to the open source community
Something unrelated to data visualization
Better students
Have additional people supporting my role to allow more time towards robust reporting mechanisms and data visualizations
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work with more interesting data
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"That people understand that I am trained in it as they are trained in the field that they are expert in- and treat my work in that way rather than as a ""recommendation"""
that people would come for answers to where to go next- rather than where we've been
More people interested or concerned with improving data visualization. It's still a low priority for most people
increase amount of time spent on viz
I'd hire someone to prepare the data I work with in visualizations
More collaboration
Speed
Just give me the perfect tool. Easy and flexible.
Privacy issues therefore I can't experiment with any cloud-based services
IT Support response
Allowing time for user research- design and usability testing
Job security
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More time to do focused work
More exploratory philosophies.
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clients
Increase the percentage of time dedicated to study
not have to code
Eliminate administrative tasks
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See no patients
Get a Perl 6 plotting library.
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Use more R
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Clear and approved procedure to agree on project requirements
centralized task schedule
More IT support for using the tools.
visibility
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A better information system
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more analysis
More focus on the most interesting g data and viz problems
$
Besides money- newest version of R. A smoothie bar would be nice too.
strengthen the statistical capabilities in my team
Freedom to use best tool for each task
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Better colleagues
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Stakeholder buy-in
Self-demand
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Proper project management prior to commencing work
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Better data quality
Less interference from boss
Have more engineers to collaborate with.
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Work agile with clients
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Better software
less rework
Get more resources to focus on data and analytics
"The level of recognition.
Everyone thinks they can make a decent visualization (hint: they can't)"
Get someone else to do invoicing for me! What a time-suck.
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Working to a coherent data strategy- architecture- led by practical people of vision.
Bring closer the design team to using real data on their design
clear scope of work that separates front-end dev and vis role.
Timing
Wider adoption and understanding of scripting approaches (preferably R) but any would be nice. Would clarify my work processes immensely
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It might be nice to get PAID- but then there'd be pressure....
Less admin work; more time for data
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More development support and more collaboration during visualization development (I'm currently doing all development work myself)
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Staffing
Spend less time writing and more time implementing
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Better project management- and more time for R&D.
Unlimited funding
Flexibility in use of tools
More time to explore
more time to be thoughtful/innovative
More R&D time
Visual tools for on-the-fly data wrangling
100 focus on data presentation- design and consultancy
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More emphasis on research and data vis; less on business/admin.
Get clients that are smarter than amoebas
Not having to go searching for the dataset to do data visualization about as often.
Cleaner data
More money- more developers.
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Clients would be better at finding me/I'd have to market myself less and spend more time visualising
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more time for design
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Better modeling of our collection data and coherent data interfaces between divisions in our company
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Customers happy to pay what it cost to develop a proper datavis
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Hire infrastructure engineers.
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To do more data science and machine learning
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Working more across teams- rather than within user interface
More collaboration
COTS software security approval
Less time with user support
Old tech
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Have more time to pick projects and work on long-term stories
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I would work only 3 days a week
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Hire a helper
Automatic the routine stuff and exploring new ideas
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More professional level training made available.
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More time spent on data visualization!
reduce the manual work via process automation
Switch to an Engineering-based culture instead of a Sales-based culture
Give myself more space and time to explore new ways of exploring data
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More time for research
Have more time
getting more money out of clients- who don't see sometimes the benefits of a good viz
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Have more people like me on he job.
The pace at which things get done
More design- less coding
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change supervisor.
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More time for research.
Having an assistant to help me.
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Do more explorative and experimental solutions.
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Big cleanup of fake experts lol
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Fewer projects so I can focus more time on the quality of each.
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Better infrastructure for faster analysis
Helping out the marketing team more to create one-of-a-kind creative data visualizations
I actually really love working here.
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take some of the pressure away and have more time to assess work
More d3
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Entirely or majority onshore development team instead of dealing with variable quality offshore developers.
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more interesting vis
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Develop a sense of how to use images to do scientific research- and to communicate results. I am in the geography department of a major American university- but almost nobody ever makes a map. And when they do- it is more an illustration that actual content.
Make simple tools
I'd like a toolkit that's integrated with all of our data sources.
More time to experiment. I have lots of ideas- but only enough time to work on one or two at a time in the background.
more opportunity for experimenting with webGL and D3
More research time
More vis- less data curation
More input into decisions about company direction
More vizzing!!!!'
Higher data quality & pipeline building
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Transparency and explanations for the shifting- which would help. Also- just being able to have a clear roadmap would be nice.
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compensation
More freedom to produce visualizations with different tools
More interesting stuff
Have more time to do MY OWN project (e.g.- mobile apps) instead of client's
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All Open Source
Larger team
Taking data vis more seriously.
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more job security
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More time doing design/viz
Less non-datavis work
have unlimited funding
even better balance between client work and personal projects
More freedom to explore domains outside of the scope of my industry