Key Takeaways

Here are key takeaways from the event contributed by Kevin Petrie and Wayne Eckerson in the final session. 

Keynote Takeaways:

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

Practitioner Takeaways:

Mike Ross, SVP of Technology, Blue Sky Specialty Pharmacy 

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

Anupam Nandwana, CEO P360

  • Data products can derive from MDM golden records
  • Key barriers: data volumes, product ownership, cross-functional processes for standardizing

Featured Presentation Takeaways:

Rick Hall

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

Anthony Deighton

  • data products are consumption-ready, high-quality, trustworthy, accessible, and able to help solve business problems
  • Clean, standardized, many attributes, with consistent views but also the ability to iterate
  • Data teams can’t keep up
  • Historical solutions are to throw people at the problem or lock it down – i.e., MDM or other controls prevent future data changes
  • AI can unlock and break the dilemma
  • Start with a MVDP
  • Nobody likes being on an MDM team…

Expert Panel:

  • Data governance cannot be purely centralized/top down (Rick)
  • Start with consumption rather than production (Anthony)
  • Data mesh philosophy reminds us that data product ownership should be as close to the source as possible (Rick)
  • You need both central and distributed elements – i.e., a federated model
  • Empower but govern edge data consumption and usage
  • Value metric focuses downstream – i.e., the output and what consumer does with it
  • Costs are processing, time, etc.