By Doug Mairet- VP Data Solutions, eimagine

“We’ve got three years and five million dollars to build this data warehouse. What should we do with it once it’s done?”

If you’ve ever heard a variation of this question in your organization, you’re not alone. I’ve seen it time and again – massive data infrastructure projects that put the cart so far before the horse that the horse has gone off to start a new life as a rodeo star.

The Traditional Approach: Build It and They Will…Scratch Their Heads

The traditional approach to data projects typically follows this well-worn path:

1. IT decides the company needs a modern data warehouse
2. Months (or years) are spent integrating every system and importing all available data
3. A beautiful, comprehensive data warehouse is built
4. Everyone stands around awkwardly asking, “So… what business problems does this solve exactly?”
5. A few dashboards are hastily created that nobody really requested
6. Executives wonder why their multi-million dollar investment isn’t delivering value

It’s like building an enormous mansion with 50 rooms before asking how many people will live there or what they like to do. Sure, it’s impressive, but do you really need that third billiards room if nobody in your family plays pool?

Flipping the Script: Decision-First Data

What if we approached data projects the way we approach other business investments – by starting with the end in mind?

Here’s the radical idea: Start with the decisions your business needs to make, then build only the data infrastructure required to support those decisions.
Novel concept, right? (I’m only being slightly sarcastic here.)
This approach transforms your data strategy from technology-driven to decision-driven, and the benefits are enormous:

• Faster time to value – When you build only what you need to support specific decisions, you can deliver results in weeks instead of years
• Higher ROI – Your investment goes directly toward solving real business problems
• Greater adoption – When data products directly address business needs, people actually use them
• More focused scope – Building for specific decisions prevents feature creep and unnecessary complexity

How to Implement a Decision-First Approach

Here’s how to put this approach into practice:

1. Partner with business stakeholders to identify critical decisions
Start by asking business leaders: “What decisions keep you up at night? What would you decide differently if you had better information?” These conversations reveal the true value opportunities.

Look for stakeholders who are enthusiastic early adopters. These champions will help demonstrate value and build momentum across the organization.

2. Define the data needed for each decision
Once you’ve identified key decisions, work backward to determine what data you need.
Ask questions like:

• What metrics would inform this decision?
• What level of detail is required?
• How frequently does the data need to be updated?
• What historical context is necessary?

Just as important as the data itself is defining what actually triggers action. Work with decision-makers to establish:

• Thresholds that demand attention – Is it when inventory drops below 20%? When customer churn exceeds 5%? When manufacturing defects rise above 0.1%? Setting clear thresholds transforms data from interesting to actionable.
• Trend patterns that signal opportunity or risk – Sometimes it’s not about absolute numbers but the direction and velocity of change. A 3% decline might be noise, but three consecutive months of decline might require intervention.
• Specific actions tied to data conditions – For each threshold or trend, map out exactly what the decision-maker should consider doing. “When X happens, consider options A, B, or C based on these additional factors.” This transforms your data from merely informative to truly prescriptive.

3. Source and integrate only the required data
Now – and only now – should you start thinking about data architecture. Build just enough infrastructure to support your targeted decisions. This might mean:

• Integrating only the systems that contain relevant data
• Designing data models optimized for specific analytical needs
• Setting up pipelines that refresh at the right cadence (not everything needs real-time updates!)

4. Deliver insights quickly and iterate
With your focused approach, you can deliver decision-supporting insights much faster than with a monolithic data warehouse project. This creates a virtuous cycle:

1. Quick wins build credibility
2. Early adopters become advocates
3. More stakeholders identify decision use cases
4. Your data capabilities grow organically to meet real needs

A Real-World Example
Our sales team wasn’t getting the kind of insights they needed from our CRM system. We worked with the Sales Executive to determine what decisions he wanted to make (he wanted to see sales forecasts with a probability model applied for each salesperson so that he could decide where to invest his time to help accelerate certain sales).

We showed him a few visualization options and determined which data was needed from our CRM to support the forecasting models. We built a pipeline and a basic data architecture and spun up a dashboard within a few weeks.

Later, he was showing the dashboard off to other executives and our team received several calls from those other executives – they all clamored for dashboards of their own!

This is the magic of the decision-first approach: when people see data directly addressing their real business needs, adoption isn’t just possible – it’s inevitable. And instead of pushing data tools onto reluctant users, you suddenly have executives knocking on your door, eager for their own decision support.

The Bottom Line
Data for data’s sake is a recipe for expensive disappointment. Data in service of better decisions is transformative.

By starting with the decisions that drive your business and working backward to the data required, you’ll build more valuable solutions, deliver them faster, and create enthusiastic adoption throughout your organization.

The next time someone says, “We need a data warehouse,” ask them: “What decisions will it help us make?” Their answer – or lack thereof – will tell you everything you need to know about whether you’re on the right track.

For more information on how eimagine can help your organization make Decision-First Data Solutions, please contact Doug Mairet- VP Data Solutions

dmairet@eimagine.com