#57 Using a Knowledge Graph for a Data Marketplace and Data Mesh for Retail - KGC Takeover Interview w/ Olivier Wulveryck and Guest Host Ellie Young
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There is no by the book way to implement this. Adapt is better than adopt. It's how do you build this living system inside. The big thing for companies that are thinking about adopting mas, or thinking in this direction may be a technical component. But then there's also this cultural piece that has to really form a major part of it.
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Ellie's LinkedIn: https://www.linkedin.com/in/sellieyoung/
Olivier's LinkedIn: https://www.linkedin.com/in/olivierwulveryck/
Knowledge Graph Conference website: https://www.knowledgegraph.tech/
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In this episode of the Knowledge Graph Conference takeover week, special guest host Ellie Young (Link), founder of Common Action, interviewed Olivier Wulveryck, Senior Consultant and Manager at OCTO Technology.
In the first two thirds of the interview, Olivier and Ellie chatted about a lot of concepts specifically around data mesh and then they linked in the concepts around knowledge graph in the last third.
For Olivier, a knowledge graph is the map for the data that is available - each data product or node in a data mesh is the representation of the knowledge within the organization. The knowledge graph is the abstraction of that knowledge across the data mesh - a logical representation on top of the data mesh nodes to help people make sense of the data mesh.
Currently, Olivier is working with a client sharing their data in a data marketplace. They are working on implementing a knowledge graph on that but not on their internal data. If they are seeing value from applying a knowledge graph externally, they may apply to their internal usage.
Olivier shared his view that it's easier to start with a data mesh than a knowledge graph - any first steps with a data mesh will bring you value. It is not the same with knowledge graphs - you have to do more work to get to value with knowledge graphs.
Olivier previously worked on the operational side of software engineering. He realized they had lots of data sitting in databases but the data was just a consequence - it was state data, there was no temporal dimension. He wanted to apply machine learning to the data but he was suffering from low quality data, just like the data team. The producing team never really thought about the data to be shared with others. For Olivier, the way to fix this is to put data back at the center of the domain and flip the script - make the operational datastore the consequence of changes in the data.
Olivier believes there is a need to think about the semantics of the data as it will be used by the rest of the organization - data can no longer be just an internal asset of the domain. If we believe we need the data to actually be used, we need that data to actually provide value. Make the data usable and useful.
To get specific, Olivier shared a use case of a clothing retailer. They might be getting people's body type and measurements to help them choose clothes that fit better. But the company could also use the data to make better fitted clothes for a broader range of their customers. They might have more insights to change the way they tailor or design their clothing.
Ellie asked about can we standardize how we capture and share data. Olivier is not sure if we can harmonize how we capture data. So instead, we need to harmonize on aggregation and integration. Olivier also talked about the challenges around finding the equilibrium between data consumer and data producer needs/wants.
Per Olivier, adapt is better than adopt. There is no by-the-book way to implement data mesh because that would never be applicable to any real organization. Olivier also mentioned the need for a framework for how teams will communicate and work together, e.g. Team Topologies.
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