{"id":2289,"date":"2025-09-08T14:04:41","date_gmt":"2025-09-08T14:04:41","guid":{"rendered":"https:\/\/solutionsreview.com\/thought-leaders\/?p=2289"},"modified":"2025-09-08T14:04:41","modified_gmt":"2025-09-08T14:04:41","slug":"from-data-products-to-product-thinking-why-data-teams-must-evolve","status":"publish","type":"post","link":"https:\/\/solutionsreview.com\/thought-leaders\/from-data-products-to-product-thinking-why-data-teams-must-evolve\/","title":{"rendered":"From Data Products to Product Thinking: Why Data Teams Must Evolve"},"content":{"rendered":"

In a recent panel discussion with Malcolm Hawker, Brian T. O\u2019Neill, and Mark Stouse, we tackled a hard truth: too many data teams are still confusing outputs<\/em>\u00a0with\u00a0outcomes<\/em>. I wanted to provide you with an overview of the conversation we had and how the panel played out. Thanks to all those who attended, and for those who didn’t, follow me, and you will be notified of our next panel.<\/p>\n

But, back to the panel. As I put it during the session:<\/p>\n

\u201cToo many data teams are still delivering outputs, not outcomes. A dashboard or a model is not a product, until it really makes a business impact and changes decisions and results.\u201d<\/em><\/p><\/blockquote>\n

Some folks out there may disagree with that, but we have seen, heard and some have experienced what happens when data teams don’t deliver value and don’t embed the products they build into the processes and DNA of the organization.<\/p>\n

The Core Challenges<\/strong><\/h3>\n
    \n
  • Outputs mistaken for value<\/strong>\u00a0Dashboards and models are produced in volume, but adoption remains dismal. Deliverables are often celebrated, even when no one uses them:\u00a0\u201cIf only 1 percent of dashboards are being used, then where is the value exchange? Adoption is the real test of a data product, not how many you\u2019ve built.\u201d Samir Sharma<\/em><\/li>\n
  • Inside-out vs. outside-in<\/strong>\u00a0As Mark Stouse warned, too many teams still build what they\u00a0think<\/em>\u00a0is valuable:\u00a0\u201cIf you build data products inside-out, like bringing your customer a dead lizard and saying \u2018look what I made for you\u2019 don\u2019t be surprised when they\u2019re not impressed.\u201d<\/em><\/li>\n
  • The cult of precision<\/strong>\u00a0Data science still chases academic-style accuracy instead of usable insights:\u00a0\u201cA 58% model shipped in two weeks can create more value than a 95% model delivered after seven months, because by then, the business has already moved on.\u201d Brian T. O\u2019Neill<\/em><\/li>\n
  • Lack of customer orientation<\/strong>\u00a0Malcolm Hawker shared a telling stat:\u00a0\u201cWhen I asked a room of 400 data professionals how many refer to their stakeholders as customers, only three hands went up.\u201d<\/em><\/li>\n<\/ul>\n

    Recommendations for Change<\/strong><\/h3>\n
      \n
    1. Be relentlessly customer-driven<\/strong>\u00a0As Malcolm put it:\u00a0\u201cStop putting data on a pedestal. Forget being data-driven. Start being customer-driven, if there are no customers, there is no data team.\u201d<\/em><\/li>\n
    2. Adopt product management methods<\/strong>\u00a0Brian stressed that the real game isn\u2019t in outputs but in outcomes:\u00a0\u201cMost data teams are stuck in the feature factory: shipping outputs, not outcomes. The real game is downstream, what changes because of the thing you built?\u201d<\/em><\/li>\n
    3. Measure success by business impact<\/strong>\u00a0I added:\u00a0\u201cThe feedback loop is missing. Data teams measure pipelines and quality, but not the business impact. That\u2019s the real metric of success.\u201d<\/em><\/li>\n
    4. Embrace imperfection and speed<\/strong>\u00a0A model doesn\u2019t need to be perfect; it needs to be useful. As Mark noted, CFOs don\u2019t want precision for its own sake, they want forward visibility that helps them act.<\/li>\n
    5. Unlearn old mindsets<\/strong>\u00a0Malcolm urged us to drop deterministic thinking:\u00a0\u201cThese binary ways of looking at the world don\u2019t work in a probabilistic, AI-driven world. We need to be comfortable with ambiguity.\u201d<\/em><\/li>\n<\/ol>\n

      The Mindset Shift<\/strong><\/h3>\n

      The key lesson? A \u201cdata product\u201d is not the end goal. The goal is to deliver outcomes that business stakeholders would\u00a0pay for<\/em>\u00a0in budget, in behavior change, or in trust.<\/p>\n

      \u201cWe need to elevate data teams out of just shipping stuff and into building outcomes that have a deeper, meaningful relationship with the business.\u201d Samir Sharma<\/em><\/p><\/blockquote>\n

      Data teams that embrace\u00a0product thinking<\/em>\u00a0will:<\/p>\n

        \n
      • See themselves as problem-solvers, not deliverable factories.<\/li>\n
      • Work iteratively with customers, not in isolation.<\/li>\n
      • Measure success by business impact, not vanity metrics.<\/li>\n<\/ul>\n

        Those that don\u2019t risk being sidelined. Those that do will become indispensable.<\/p>\n

        The future of data belongs to teams who think like product managers and embrace the product life-cycle mentality.<\/p>\n

        If your team isn\u2019t there yet, the time to start is now.<\/p>\n","protected":false},"excerpt":{"rendered":"

        In a recent panel discussion with Malcolm Hawker, Brian T. O\u2019Neill, and Mark Stouse, we tackled a hard truth: too many data teams are still confusing outputs\u00a0with\u00a0outcomes. I wanted to provide you with an overview of the conversation we had and how the panel played out. Thanks to all those who attended, and for those […]<\/p>\n","protected":false},"author":546,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[10],"tags":[],"acf":[],"yoast_head":"\nFrom Data Products to Product Thinking: Why Data Teams Must Evolve<\/title>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"From Data Products to Product Thinking: Why Data Teams Must Evolve\" \/>\n<meta property=\"og:description\" content=\"In a recent panel discussion with Malcolm Hawker, Brian T. O\u2019Neill, and Mark Stouse, we tackled a hard truth: too many data teams are still confusing outputs\u00a0with\u00a0outcomes. 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