emmairwin/gist:ee0f7160749e72f77c16fc6815ca5ccb

## gistfile1.txt
One of the greatest challenges is organizing metrics in a way that's organized, all in one place.

**About Augur**
Currently we collect all data in a relational database, and we ensure quality with tests we have to make sure that data is populated correctly
You can build an API.

We have metrics that are in CHAOSS, and others conecieved emerging in discussions with organizations, that will make it there.
We have what we call an insight worker system.  It goes through every single end-point, and identifies where the greatest
anomolies.

In an example on the screen, we see an anomolie where contributions dropped in a significant way.  So you can dig into the orgin of the insight. Augur identifies these anomolies within a timeframe that you identify.
The second thing we can show you, are specific things about a repository.
In the example on the screen, there is information about lines of code, biggest ommiters.
We can see lines of code added by the 10 authors visualized.

(I asked, how do you determine organizational affiliation), answer was that it's a combination of getting Github Ids, and a
a worker process that goes out and does a best guess (paraphrasing).
There is potential in the future to do something like Hyperledger Indy, which is a distributed identity system, where people ight share
things about themeselves, who they contribute on behalf, where they live maybe other personal information that remains private.  "Beyond scraping public data for metrics"
	One of the greatest challenges is organizing metrics in a way that's organized, all in one place.

	About Augur
	Currently we collect all data in a relational database, and we ensure quality with tests we have to make sure that data is populated correctly
	You can build an API.

	We have metrics that are in CHAOSS, and others conecieved emerging in discussions with organizations, that will make it there.
	We have what we call an insight worker system. It goes through every single end-point, and identifies where the greatest
	anomolies.

	In an example on the screen, we see an anomolie where contributions dropped in a significant way. So you can dig into the orgin of the insight. Augur identifies these anomolies within a timeframe that you identify.
	The second thing we can show you, are specific things about a repository.
	In the example on the screen, there is information about lines of code, biggest ommiters.
	We can see lines of code added by the 10 authors visualized.

	(I asked, how do you determine organizational affiliation), answer was that it's a combination of getting Github Ids, and a
	a worker process that goes out and does a best guess (paraphrasing).
	There is potential in the future to do something like Hyperledger Indy, which is a distributed identity system, where people ight share
	things about themeselves, who they contribute on behalf, where they live maybe other personal information that remains private. "Beyond scraping public data for metrics"