Building a business intelligence MVP essentially comes down to four steps.

This is the strategy I used at IBM, and I’ve since applied it at both smaller companies and large financial institutions. When you’re facing overwhelming data, competing business priorities, and pressure to deliver insights—this framework keeps you focused on what matters.

Step 1: Focus on the Most Impactful Business Question

When building a BI solution, there’s going to be a lot of noise. Lots of data. Competing priorities.

Your job? Get laser-focused on the business question that will drive the most impact.

You might start with 10 different business questions, but you need to narrow it down ruthlessly. Pick the one question that, when answered, will move the business forward most significantly.

The key: Make it actionable. Don’t just look at data—turn it into information that drives decisions. Always ask “So what?” If you can’t answer that, the question isn’t focused enough.

Step 2: Build a Prototype First

I’ve been on both sides of the coin:

  • Companies drowning in data who don’t know where to start
  • Companies with critical questions but no clear path to the data

That’s why I always build a prototype before the MVP.

Why prototype first?

  • Validate you have the data you need
  • Test if your approach answers the business question
  • Avoid investing resources in the wrong solution

How to build the prototype:

  1. Get sample data from spreadsheets, APIs, or source systems
  2. Transform the data using tools you already have (Excel, Google Sheets)
  3. Create calculations that answer your business question
  4. Build key visualizations that tell the story
  5. Assemble a dashboard feel with high-level summaries and detailed reports

At this stage, you’re not investing in tools. You’re validating your approach with what you have on hand.

A real example: I’ve used Excel with Power Query and Power Pivot to build complete prototypes. When it’s time to move to a proper BI tool, I simply replicate what I’ve already proven works.

Step 3: Validate with Stakeholders

Your prototype might be beautiful, but if it doesn’t meet business needs, it’s useless.

Before building the MVP, I take my prototype to stakeholders:

  • Business owners
  • Executives
  • End-users who will make decisions with this data

This validation phase accomplishes three things:

  1. Confirms data sources are identified and accessible
  2. Validates calculations and transformations answer the right questions
  3. Ensures visualizations and summary levels meet user needs

If something’s missing, now is the time to go back to data sources and figure out what additional data you need. Refine the system before you invest in building it out.

This collaborative approach ensures buy-in and maximizes ROI when you move to the next step.

Step 4: Build the Scalable MVP

Now you’re ready to get technical.

At this stage, you know:

  • Where your data sources are
  • What calculations are needed
  • What visualizations work
  • What level of summary stakeholders need

Building the MVP:

  1. Connect data sources to your business intelligence tool
  2. Replicate your prototype functionality in the BI platform
  3. Focus on core features users need:
    • Different views and data displays
    • Filters and sorting capabilities
    • Export features for tabular data
    • Access controls for data security

The key difference from prototype: Your MVP must be scalable. The prototype breaks when you refresh data or the transformations fail. Your MVP needs to handle:

  • Data volume growth
  • Fast refresh cycles
  • Complex visualizations
  • Multiple concurrent users

Tool selection matters. Pick a tool you’ll stick with long-term. You’re not building a new dashboard every time—you’re building a foundation you’ll expand on.

The MVP Philosophy

Remember one core idea: MVPs are about getting feedback as fast as possible while delivering business value upfront.

You’re not building perfection. You’re building something that:

  • Answers the critical business question
  • Scales as needs grow
  • Delivers value immediately
  • Can be expanded over time

Not Ready for an MVP Yet?

For companies not ready to build an MVP, there are still ways to automate your data and reporting. Check out The Four Levels of Data Automation to find the right starting point for your organization.

The Bottom Line

MVPs aren’t just for tech startups. They’re the secret weapon for delivering data-driven value at any scale—whether you’re at a Fortune 500 company or a growing business.

Start focused. Prototype fast. Validate early. Build scalable.

Ready to build an MVP that transforms your data strategy? Get in touch to discuss how we can create scalable BI solutions for your organization.