First, what's a data network effect? Well, it's based on network effects. A network effect is when a product or service gains more value as more people use it. Think Facebook becoming more valuable than MySpace. A DATA network effect is where something becomes smarter as it gets more data. This is particularly relevant to machine learning and other AI algorithms. Generally, the more data on which they have to work, the better the results.
It is the first two of these that give you a data network effect. By connecting available data (public and private), and keeping it a format that machines and people can both use, you improve the results of the algorithms. In our experiments, improvements in perceived quality have been up to 27%.
I say "perceived quality" because the test we did was matching candidates to jobs. In the short run perception is only way we have to assess that match. In the long run there will be more objective measures of course.
Of course having comprehensive, fresh data available wherever and whenever you need it has a lot of benefits - but data network effects are one you'll be needing to get on board with.
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