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Your Data Team Is Not a Vending Machine

    A lot of companies still treat their data team like a vending machine.


    You submit a request -> “Dashboard on Q2 Sales by Region”

    ....and wait for something to come out.


    People in orange and red outfits analyze data on large screens and servers. Grey background with cloud graphics. Mood is focused.

    Sometimes it’s quick. Sometimes it’s not. Sometimes it’s useful.


    But the assumption is the same: request in, answers out.


    ✅ Structured

    ✅ Trackable

    ✅ “Efficient”


    But here’s the thing: This model stifles trust, limits learning, and turns data work into an order-taking exercise.


    Because most data requests?

    They’re signals. Not specifications.


    They’re hints that something’s unclear. Something needs to be understood. Something’s not working as expected.


    If you treat them like finished requirements, you miss the opportunity to dig deeper.



    Why This Matters


    Great data work doesn’t happen in a ticket queue.


    It happens through conversation. It happens by partnering with the business to explore what the real need is.


    Request: “Can we get a report on top-performing stores?”

    Reality: The region manager is trying to figure out why one area’s sales have stalled — and whether it’s team structure, inventory, or customer mix.


    A ticket would have delivered a chart.

    A conversation leads to insight.



    Why the Vending Machine Model Persists


    So why does this still happen?

    • It creates structure.

    • It protects the data team’s time.

    • It feels efficient.


    And sometimes, it’s genuinely helpful for well-defined requests.


    But when it’s the default, it disconnects your team from the business and limits the impact of the work.


    You don’t need order takers.

    You need strategic partners.



    What to Do Instead


    Shift your data team from service model to product model.


    That means:

    • Inviting business users into the early stages of the work

    • Co-creating the solution, not just building what was asked for

    • Measuring success by usefulness, not delivery

    • Keeping feedback loops open from start to finish


    The business doesn’t just need data.

    They need clarity, confidence, and context.


    That doesn’t come from a vending machine.

    It comes from working together.



    At Fuse, we believe a great data strategy only matters if it leads to action.


    If you’re ready to move from planning to execution — and build solutions your team will actually use — let’s talk.


    When “Just Enough” Is the Right Answer

      When you’ve been around enterprise data long enough, you start to see a pattern.


      A small use case kicks off.

      A man stands on a path dividing two landscapes: one mechanical with gears, the other industrial. Orange highlights contrast with gray-blue tones.

      The goals are clear.

      The business is engaged.


      And then… the architecture conversations begin.


      Suddenly, a simple analytics request becomes a platform initiative.


      Multiple tools are in play.

      Workflows get abstracted.


      Foundations get planned before anyone checks if the ground is worth building on.



      We over-engineer because we want to be responsible.


      We want the work to scale.

      We want to “do it right the first time.”

      We want to avoid future rework.


      All reasonable instincts.


      But in trying to build something durable, we often delay the very thing that would make it successful:


      Showing early value.



      You’re not building a monument. You’re building momentum.


      When you're starting an initiative, keep the following in mind:


      1. Deliver just enough.

      2. Test it early.

      3. Improve it fast.


      That doesn’t mean we cut corners or ignore quality.

      It means we prioritize traction over perfection.


      The best data work isn’t built in one big push.

      It’s co-created, shaped in the real world, and refined over time.



      Final Thought


      Ask yourself:

      • What’s the smallest version of this that would still be useful?

      • What feedback could we collect within 2 weeks?

      • What can we leave out, for now, that won’t break trust?


      Because in most cases, just enough is enough to start.


      And starting is where all the real learning begins.



      At Fuse, we believe a great data strategy only matters if it leads to action.


      If you’re ready to move from planning to execution — and build solutions your team will actually use — let’s talk.


      Build vs. Buy in the Age of AI Co-Pilots

        A man stands on a path dividing two landscapes: one mechanical with gears, the other industrial. Orange highlights contrast with gray-blue tones.

        For years, the classic question in enterprise data has been:


        “Should we build our own solution or buy something off the shelf?”

        Now, with AI-assisted development tools the question is evolving. But the answer isn’t getting any easier.



        It’s easier than ever to build. But that’s not the whole story.


        There’s no doubt that co-pilots and assistants have lowered the barrier to custom development.

        • You can generate boilerplate code in seconds

        • You can scaffold pipelines, tests, and documentation faster than ever

        • You can go from idea to prototype in a day


        So it’s natural to ask:


        “If it’s this easy to build, why are we buying anything at all?”

        But here’s the thing:


        Just because it’s easier to build doesn’t mean it’s easier to maintain.


        Just because it’s fast to generate doesn’t mean it’s ready for enterprise use.


        And in most cases, the cost to create isn’t the cost that matters most.



        What looks like savings may be debt in disguise.


        Quick builds can hide long-term responsibilities. A working proof-of-concept doesn’t mean you’re ready to run it at scale.


        In a corporate environment, you’re not just building for yourself. You’re building for a team. And for the person who inherits it three quarters from now.


        That means you’re accountable for:

        • Permissions and access controls

        • Logging and observability

        • Documentation

        • Governance and data quality

        • Change management

        • Support


        All of these take time, tooling, and team capacity. And none of them are free.



        How we’re thinking about build vs. buy now


        At Fuse, we don’t ask, “Can we build this?” (You probably can. Especially now.)


        Instead, we ask:

        • Is this something we’re prepared to own?

        • Does it give us an advantage we couldn’t buy?

        • Will we still want this responsibility six or twelve months from now?


        If the answer to all three is yes, then build.


        If not, buy it. Integrate it. Focus your energy where it actually gives you leverage.



        Final thought


        AI is making it easier than ever to build. But in enterprise data, building is only the beginning.


        Owning something means maintaining it, evolving it, supporting it, and making sure it continues to deliver value.


        Choose wisely. Not based on what’s easy today, but based on what you’re willing to carry tomorrow.



        At Fuse, we believe a great data strategy only matters if it leads to action.


        If you’re ready to move from planning to execution — and build solutions your team will actually use — let’s talk.


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