How to define an impactful data initiative

At Fuse Data, we use a four-part process to move from strategy to action:

Define clearly so you can deliver with confidence

1. Define – What are we solving, why does it matter, and how will we know if it worked?

2. Align – How does this fit with other priorities? What should we do first?

3. Design – What will the solution look like? How do we make it usable and trusted?

4. Deliver – Build, validate, and iterate with the business fully in the loop.

This post focuses on the first stage: Define.

It’s where we turn raw feedback into a clear, shared understanding of the opportunity and the goals that we're building toward.

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Define: What are we solving, and why does it matter?

Discovery gives you raw material, but raw insight doesn’t move anything on its own. You need to shape it.

Once you've run a few stakeholder interviews or working sessions, you’re usually left with a big wall of sticky notes. Requests, frustrations, goals, ideas — all valid, but not all equally important. And definitely not a roadmap.

This is where a lot of teams stall. The “we heard you” stage. And then… nothing.

So how do you take that raw input and turn it into something the business can actually say yes to?

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Step 1: Pattern what you’re hearing

Start by organizing what came out of discovery using simple UX research frameworks like:

• Affinity mapping: Group similar notes and observations to reveal recurring themes

• Jobs-to-be-Done: Reframe what users are asking for in terms of the job they’re trying to accomplish

• Pain-Gain mapping: Capture where the biggest friction lives — and where solving it would deliver real value

This isn’t just for clarity. It gives you language the business recognizes, and a more honest view of what matters most.

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Step 2: Frame candidate opportunities

Now take your themes and begin to shape them into initiative candidates.

For each one, ask:

1. What is the specific problem we’re solving?

2. Who is the audience, and what do they need?

3. What will success look like for them?

4. How might we enable or accelerate that outcome?

Use framing tools like:

• How Might We… statements

• Problem/Opportunity/Impact frameworks

• Or even just a simple one-liner: “We believe that [doing X] for [this group] will result in [this outcome].” This is actually my favourite and its harder than you think to get a group to agree on one sentence!!

The goal here isn’t to list everything you heard. It’s to focus.

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Step 3: Prioritize (gently)

You don’t need to create a rigid prioritization matrix, but a light-touch framework helps.

We often use a modified RICE model:

• Reach: How many people or decisions will this affect?

• Impact: How much could this improve trust, efficiency, or clarity?

• Confidence: How clear is the need? Can we deliver this?

• Effort: What will it take to get something usable in front of people?

This helps separate high-value, high-urgency initiatives from interesting-but-not-now ideas.

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Step 4: Shape into an initiative brief

At Fuse, we create a one-page initiative brief that includes:

1. The core problem or opportunity

2. Who it’s for

3. What success looks like

4. Initial solution direction (if known)

5. Risks or assumptions

6. What we’ll validate or prototype first

7. What's the RACI for our build and who owns the solution once its built

It’s not formal documentation. It’s a working agreement between data and the business.

And it creates alignment before we go anywhere near the technology stack.

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Final Thought

Discovery without direction leads to drift. But when you shape insight into a clear, co-created initiative, things start to move.

And importantly: the business feels heard.

That’s how you build trust. That’s how you make strategy real.

Next week, we’ll move to step two: Align. This is where we look across the opportunities surfaced and decide what to prioritize and when to act.

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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.

👉 Book a strategy call

👉 Read more on the Fuse blog

👉 Follow Fuse Data on LinkedIn

👉 Follow Dave on LinkedIn

Your Data Stack Shouldn’t Come First

It’s easy to get excited about technology....

but in order to have a successful data function, your data stack shouldn’t come first.

.... maybe one more micro service

The modern data stack is sleek, powerful, and constantly evolving. New technologies

promise better pipelines, faster queries, smoother integrations. So it’s no surprise that many data programs begin by architecting the stack — building infrastructure before anything else.

But that’s often where things go off the rails.

We’ve seen companies sink months into designing scalable, elegant architectures... only to realize too late that they haven’t solved a real problem for the business.

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If you’re not solving a problem, you’re building a monument.

The most common misstep we see is starting with platforms instead of people.

That usually sounds like:

"We’re rolling out Snowflake and dbt."

"We’re building a data lake with reverse ETL."

"We’re implementing a modern BI tool so people can self-serve."

Those are all fine components — but they’re not a strategy.

If no one trusts the data, or knows how to use the tool, or feels like their job got easier... it doesn’t matter how modern your stack is.

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Start with the pain. Design with the people. Then build.

The build is better with business buy-in

That’s why we use a structured discovery process built around ten practical questions.

These questions are designed to surface business context, reveal pain points, and expose hidden opportunities before we write a single line of code.

This process does more than gather information — it builds trust. It gives business partners a voice early in the process, which makes them more likely to engage later. By the time we start prototyping, we’re not guessing. We’re designing around real needs that have been heard, understood, and prioritized.

#	Questions	How your partner should prepare
1	What is your role within the organization and what are your responsibilities?	Org chart, role description, how it’s evolved, and how you interact with data (e.g., viewer, creator, analyst)
2	What are your goals and what does success look like for you and your team?	Departmental/corporate objectives and how they're measured (KPIs, cadence)
3	How does data support your team today?	Examples of reports or tools used in your day-to-day
4	What needs to change to better position you for success?	Known pain points, blockers, and examples from other roles
5	What changes are happening that may support or hinder your data journey?	Info on org changes, upcoming projects, or process overhauls
6	Who do you rely on most when it comes to getting data?	A map of who helps with data requests and what that process looks like
7	What’s a recent decision your team made — and how confident were you in the data that supported it?	Real-world examples of decision moments and the supporting data
8	How do new team members learn how to access and use data today?	Onboarding materials or anecdotes — or the lack thereof
9	If we could solve one thing for you in the next 30 days, what would it be?	Your team’s most pressing data pain point — the one that would make the biggest difference quickly
10	What happens if we do nothing?	Honest view of the cost or consequence of leaving data capabilities as-is

Now, you don't need to make it through all of these questions verbatim. The are simply meant as conversation starters. Often times I only make it through half of these before the flood gates open and the feedback starts flowing.

Once we’ve defined what success looks like for actual users, we define our initiatives, prioritize and go back to get feedback. Only then do we start thinking about the underlying tech — and even then, we ask: what’s the lightest way to support this?

That’s where delivery gets fast, focused, and actually useful.

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The stack is there to serve the strategy, not to define it.

Before you build anything, ask: 

Who is this for?

What will this help them do better?f

Because a scalable, elegant data stack that doesn’t solve a business problem… isn’t scalable or elegant at all.

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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.

👉 Book a strategy call

👉 Read more on the Fuse blog

👉 Follow Fuse Data on LinkedIn

👉 Follow Dave on LinkedIn

Before You Engage the Business in your Data Strategy, Start With Yourself

The instinct to act is strong.

Once you’ve bought into the idea of building a people-first data program, it’s tempting to jump straight into conversations with the business. After all, the point is to uncover their needs and design around them. That’s the right direction.

Look inward before looking outward.

But here’s something we’ve learned:

It’s hard to listen well if you haven’t taken stock of where you’re standing.

If you walk into discovery sessions unclear on your own gaps, strengths, and assumptions, it’s easy to hear what you want to hear. It’s even easier to frame every request through a technical lens rather than a human one.

That’s why we always recommend a short moment of internal reflection before you start discovery.

Not an audit. Not a strategy deck. Just a quick check-in.

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Self-awareness makes you a better partner.

The best data leaders we work with aren’t just great at systems thinking. They’re self-aware. They know what they bring to the table, and where they need to adapt.

That’s why we created a 5-minute Data Maturity Self-Assessment.

It’s designed to help you:

How do you measure up to your peers?

• Understand your current level of maturity across a few core areas

  
  

• Get clear on where you’re strong, and where you might need to shift your approach

  
  

• Prepare for more open, constructive conversations with the business

This isn’t a scorecard. It’s a mindset reset.

Take 5 minutes to center yourself before you step into someone else’s world.

👉 Take the Self-Assessment

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Why this matters

In our next post, we’ll share the 10 discovery questions we use to guide business-facing conversations.

These questions are powerful. But they’re even more effective when you come in ready to hear what’s really being said.

Because your job in those sessions isn’t to fix. It’s to understand. And that starts with checking your own lens first.

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At Fuse Data, 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.

👉 Book a strategy call

👉 Read more on the Fuse blog