{"id":30,"date":"2022-06-09T12:52:33","date_gmt":"2022-06-09T12:52:13","guid":{"rendered":"https:\/\/eck.uidev.site\/?page_id=30"},"modified":"2023-08-29T16:18:36","modified_gmt":"2023-08-29T16:18:36","slug":"takeaways","status":"publish","type":"page","link":"https:\/\/markets.eckerson.com\/building-data-products-with-data-mesh-and-data-fabric\/takeaways\/","title":{"rendered":"Key Takeaways"},"content":{"rendered":"
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Key Takeaways<\/h1>\n\t\t\n\t\t
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Here are key takeaways from the event contributed by Kevin Petrie and Wayne Eckerson in the final session. In some cases, we have referenced the person who made a comment that turned into a key takeaway.\u00a0<\/span><\/i><\/p>\n

Wayne<\/b>: People liked the term \u201cdata mush\u201d as a the potential downside of data mesh. Wayne hopes it will become a new buzzword!\u00a0<\/span><\/p>\n

Kevin<\/b>: Data product is the primary catalyst for change and adoption of best practices for mesh\/fabric implementations.<\/span><\/p>\n

Wayne<\/b>: There\u2019s not just one data store, but potentially many stores with an uber store on top. Maybe we call it \u201cdata shopping mall\u201d?<\/span><\/p>\n

Kevin<\/b>: Data fabric and mesh are symbiotic approaches to managing distributed data\u2014fabric is technical approach and mesh is organizational.<\/span><\/p>\n

Wayne:<\/b> Rankin says 90% of the challenge with data mesh is orgnizational and cultural.<\/span><\/p>\n

Rick Hall:<\/b> Biggest change over the next 18 months: decentralization of IT and data ownership\u2014and cross-enterprise collaboration to make that feasible<\/span><\/p>\n

Wayne:<\/b> Data mesh and data fabric show how to deliver self-service: with organizational, governance, and architectural models – all federated.<\/span><\/p>\n

Kevin:<\/b> To succeed, data products must offer views of lineage, usage, and relationships to other products.<\/span><\/p>\n

Paul Rankin<\/b>: \u201cData products demand product management, a discipline many people in data aren’t trained in.<\/span><\/p>\n

Kaycee<\/b>: Data fabric has the potential to provide generative AI models the full access, data quality, and explainability they need.<\/span><\/p>\n

Rick Hall<\/b>: \u201cwithout data products, generative AI is just going to be trouble\u201d.<\/span><\/p>\n

Wayne<\/b>: Days of IT centralization are over; IT must facilitate, not dictate.<\/span><\/p>\n

Paul Rankin:<\/strong> Data mesh is good cultural fit for decentralized organization where IT can manage much of the company.<\/span><\/p>\n

Wayne<\/b>: Data fabric can inject agility into a monolithic data environment.<\/span><\/p>\n

Wayne:<\/strong> Roche was impressive in getting out in front of its data mesh with a centralized data platform first.<\/span><\/p>\n

Wayne<\/b>: Data quality and data bottlenecks are the KEY pain points that all tools, architectures, methods need to solve.<\/span><\/p>\n

Matt Fuller<\/b>: The future of data products is virtual data products.<\/span><\/p>\n

Sue Tripathi<\/b> (attendee): Data fabric must align with business strategy.<\/span><\/p>\n

Kevin<\/b>: Data fabric supplements data lake & DW.<\/span><\/p>\n

Phil Hendrix<\/b> (attendee): Data products should be co-owned & prioritized according to their <\/span>contribution to the business.<\/span><\/p>\n

Kaycee Lai<\/b>: You should be able to discover, build and govern data products in one environment.<\/span><\/p>\n

Matt Fulle<\/b>r: Data product is the primary catalyst for change and adoption of best practices for mesh\/fabric implementations.<\/span><\/p>\n

Kevin:<\/b> Data fabric and mesh are symbiotic approaches to managing distributed data\u2014fabric is technical approach and mesh is organizational.<\/span><\/p>\n

Paul Rankin:<\/b> Most implementation challenges center on organizational processes and culture.<\/span><\/p>\n

Kaycee Lai: <\/b>Catalogs help expert users, but fall short of data fabric\/mesh requirements because they don\u2019t help citizen users inspect the data itself.<\/span><\/p>\n

Rick Hall:<\/b> To succeed, data products must offer views of lineage, usage, and relationships to other products.<\/span><\/p>\n

Kaycee Lai: <\/b>Data fabric has the potential to provide generative AI models the full data access, data quality, and explainability they need.<\/span><\/p>\n

Kevin: <\/b>\u201cWithout data products, generative AI is just going to be trouble\u201d<\/span><\/p>\n

Wayne: <\/b>Data mesh appeals to enterprises whose IT organization is already highly <\/span>decentralized<\/span><\/p>\n

Wayne:<\/b> Data products depend on strong data governance programs to ensure quality<\/span><\/p>\n

Wayne: <\/b>Data products require granular metadata to be shoppable, deliverable, and <\/span>returnable<\/span><\/p>\n

Matt Fuller:<\/b> Data lake contributes to fabric\/mesh by helping find, integrate, understand, and <\/span>operationalize data<\/span><\/p>\n\t\t\t<\/div>\n\t\t\t\t\t\t

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youtu.be\/LuU8un_6t-o<\/div>\n\t\t\t\t\t\t