The "So What" Test

I write weekly on different topics related to Data and AI. Feel free to subscribe to FAQ on Data newsletter and/or follow Fawad Qureshi on LinkedIn or FawadQureshi on X.

Two words can kill any project in data and analytics: “So what?”. I often use this as a litmus test for myself and while offering feedback to others. I often see teams present complex models, dashboards, and analytics platforms that sound impressive but don’t answer the most critical question: What problem does this solve? If the “So what?” response isn’t clear, you’re in trouble.

Show me the money

I wrote in the blog "There is no business department" about an experience from early in my career, which has always stuck with me. In a data warehousing training session, the instructor asked: “Why do companies build data warehouses?” We gave all the textbook answers—single source of truth, 360-degree view of the customer, eliminate silos—but we were wrong.

He simply said: “To make money and they fail when they fail to make money.”

Projects fail when they can’t make or save money. It’s that simple. I always remind people that the purpose of a data project is not to get an “academic orgasm” from a fancy model no one understands.

Clarity Sells

As I mentioned in A Confused Mind Never Buys, B2B marketers are often guilty of using consulted terms to describe their solutions. If you can’t communicate the value, customers will walk away. The same principle applies to your data projects. If you can’t answer “So what?” without using jargon, your project won’t survive.

Conclusion: Keep It Simple

Here’s how you can put this into practice:

1. Keep asking, "So what?": At every project stage, challenge yourself to justify the value. Even if it makes you sound like a broken record, keep asking, "So what?" until the answer becomes obvious.
2. Tie it to business outcomes: Align the project with specific metrics—revenue growth, cost savings, and operational efficiency. If you can’t link it to a measurable goal, rethink it.
3. Simplify your message: Strip away the technical jargon and focus on the real value your project brings. If your audience can’t understand it in one sentence, it’s too complicated.
4. Involve decision-makers early: Get feedback from the people who control the budget. Make sure they can see how the project will help them achieve their goals.

Before presenting your next data project, run it through the “So what?” filter until you can confidently show how it moves the needle. The simplicity of the message will stand out every time.

I write weekly on different topics related to Data and AI. Feel free to subscribe to FAQ on Data newsletter and/or follow Fawad Qureshi on LinkedIn or FawadQureshi on X.