{"id":2907,"date":"2024-01-10T18:34:16","date_gmt":"2024-01-10T18:34:16","guid":{"rendered":"https:\/\/markets.eckerson.com\/how-to-create-govern-and-manage-data-products\/?page_id=2907"},"modified":"2024-01-10T18:34:16","modified_gmt":"2024-01-10T18:34:16","slug":"takeaways","status":"publish","type":"page","link":"https:\/\/markets.eckerson.com\/how-to-create-govern-and-manage-data-products\/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.\u00a0<\/span><\/i><\/p>\n

Keynote Takeaways:<\/b><\/h3>\n
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  • Data products are more than data assets<\/span><\/li>\n
  • They are reusable, shared, and continuously funded, with target users and rigorous data governance. Can be code or data<\/span><\/li>\n
  • Really needs a data market \u2013 like a catalog of data products \u2013 unlocks value with frictionless data sharing<\/span><\/li>\n
  • Data product platform: product studio, storage, sits on top of fabric<\/span><\/li>\n
  • Build, buy, mix, match<\/span><\/li>\n
  • Many data products will be virtual<\/span><\/li>\n
  • Data commerce, integration, catalogs, analytics, MDM vendors all moving into the data product platform space as well<\/span><\/li>\n
  • Data product manager is product manager, owner, and marketer<\/span><\/li>\n
  • Data product organization can be centralized or decentralized \u2013 pros\/cons to each<\/span><\/li>\n
  • Data product governance requires careful organizational structures and workflows, with review\/approval process, oversight, standards, etc.<\/span><\/li>\n<\/ul>\n

    Practitioner Takeaways:<\/b><\/h3>\n

    Mike Ross, SVP of Technology, Blue Sky Specialty Pharmacy\u00a0<\/b><\/p>\n

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    • Data products must be testable, usable, and loveable<\/span><\/li>\n
    • Product managers have product management experience. Paired with product counsel, which has senior leaders of company. They drive conversations with outside users<\/span><\/li>\n
    • Journey from query to Excel file to application that is continuously used<\/span><\/li>\n
    • They\u2019ve matured to go through the full lifecycle \u2013 even have retired a data product<\/span><\/li>\n<\/ul>\n

      Anupam Nandwana, CEO P360<\/b><\/p>\n

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      • Data products can derive from MDM golden records<\/span><\/li>\n
      • Key barriers: data volumes, product ownership, cross-functional processes for standardizing<\/span><\/li>\n<\/ul>\n

        Featured Presentation Takeaways:<\/b><\/h3>\n

        Rick Hall<\/b><\/p>\n

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        • More than half of data leaders cite issues with data quality, reusability. This contributes to huge turnover among CDAOs<\/span><\/li>\n
        • Four principles:<\/span><\/li>\n
        • Delivery of quality granular data products that might otherwise be overlooked<\/span><\/li>\n
        • Empowered business teams must move quickly<\/span><\/li>\n
        • Curate results to identify trusted content<\/span><\/li>\n
        • Foster collaboration and reuse<\/span><\/li>\n<\/ul>\n

          Anthony Deighton<\/b><\/p>\n

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          • data products are consumption-ready, high-quality, trustworthy, accessible, and able to help solve business problems<\/span><\/li>\n
          • Clean, standardized, many attributes, with consistent views but also the ability to iterate<\/span><\/li>\n
          • Data teams can\u2019t keep up<\/span><\/li>\n
          • Historical solutions are to throw people at the problem or lock it down \u2013 i.e., MDM or other controls prevent future data changes<\/span><\/li>\n
          • AI can unlock and break the dilemma<\/span><\/li>\n
          • Start with a MVDP<\/span><\/li>\n
          • Nobody likes being on an MDM team\u2026<\/span><\/li>\n<\/ul>\n

            Expert Panel:<\/b><\/h3>\n
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            • Data governance cannot be purely centralized\/top down (Rick)<\/span><\/li>\n
            • Start with consumption rather than production (Anthony)<\/span><\/li>\n
            • Data mesh philosophy reminds us that data product ownership should be as close to the source as possible (Rick)<\/span><\/li>\n
            • You need both central and distributed elements \u2013 i.e., a federated model<\/span><\/li>\n
            • Empower but govern edge data consumption and usage<\/span><\/li>\n
            • Value metric focuses downstream \u2013 i.e., the output and what consumer does with it<\/span><\/li>\n
            • Costs are processing, time, etc.<\/span><\/li>\n<\/ul>\n\t\t\t<\/div>\n\t\t\t\t\t\t
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              youtu.be\/NvAVHm51ssA<\/div>\n\t\t\t\t\t\t