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Metadata Day 2020
by
Paco Nathan
KGC Editor
For a full day on December 14, 2020, LinkedIn sponsored a virtual workshop called Metadata Day, followed by a public online meetup called Metaspeak. View videos of the event on their website, including a set of lightning talks provided by several of the speakers.

The gist is that circa 2018, the pending regulatory compliance for General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), etc., drove urgency in enterprise organizations to have a better understanding and accountability about datasets and their use cases. About a dozen tech firms responded with a kind of “design pattern” emerging for how to handle these compliance challenges. Given how the datasets in question are connected to one or more use cases, and how each use case requires use of one or more datasets to train machine learning models, when auditors begin asking difficult questions about data provenance, the answers usually require graph-based approaches.

For instance, Mark Grover presented about the Amundsen project from Lyft, which coupled KG work with discovery and search UX and led to a stark discovery: “Over 30% of analyst time was wasted in finding and validating trusted data.” Lyft has 250 data analysts, so their 30% overhead translates to roughly $20M/year. Given this new “360 view” of their data assets, they also began to discover relations among their connected datasets that could open new business opportunities.

Another key observation: three tech startups have recently spun out based on this new category of data context, with these use cases and open source projects as a core. Venture capital investors describe this kind of situation in terms of category-defining companies. In other words, as certain startups begin to reshape the industry, equity growth typically follows.

Recognizing that an industry “moment” was in progress, LinkedIn organized Metadata Day to pull together the people leading these projects. The event was structured in two parts. First, an invitation-only virtual workshop (Foo Camp style), which split into one track for systems implementations (i.e., issues about production and scale) and another for articulating use case issues (i.e., expertise about metadata and knowledge graphs). The workshop track discussions worked to articulate key points about their practices and quickly assemble them into slide decks. Then the tracks reconvened to compare notes.

The second part of the Metadata Day event was a public meetup online, introduced with a keynote talk by LinkedIn CDO Igor Perisic. Both tracks presented their slide decks as panel discussions, open to much audience Q&A. The full video is at https://youtu.be/LS2LxEsj-94.



One quintessential insight was articulated by Natasha Noy during our discussion about “What is a knowledge graph?” Natasha described how Google’s Knowledge Graph project, while it has well known use within the search engine results, also fulfills another crucial purpose to help cohere Google’s data context across many different data silos. This practice is echoed across LinkedIn, Netflix, Lyft, PayPal, etc., with growing adoption across enterprise (even if enterprise does not recognize the KG practice by name quite yet 😅).  While this is certainly not a “solved problem,” nor are graph-based methods a panacea for resolving tech debt and cross-team impedance in enterprise IT, graph-based approaches are proving to be effective for the general case of data context problems in industry.

The above is excerpted from our full article available on Medium.
Read It Here
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