-- “If you want credit for outcomes, you have to help create them - not just report on them.” --
Why This Matters
Many data teams are obsessed with dashboards, pipelines, and ticket throughput. And yet, they’re frustrated when they don’t get credit for business impact.
Here’s the problem: Dashboards, pipelines, and ticket throughput are Outputs. And Outputs don’t equal outcomes. And outcomes are what matter to the business.
Data teams complain that they “empower decision-making,” but companies don't give them "credit" for outcomes like increased revenue, profit, or customer retention.
Let’s be real: that line of thinking is a form of victim mentality. It assumes the world should just understand the value of data.
It doesn’t.
Time to Take Control
As a data leader, I don’t want to be at the mercy of business perception. I want to shape it.
That means taking ownership of impact. Taking responsibility for outcomes. And building a systematic way to prove our influence on business results.
And if you want proof that this is possible, just look at marketing.
For decades, marketing teams struggled to get credit for conversions. Sales teams closed the deals, so they got the glory. But marketing didn’t whine. They responded by creating attribution modeling - a whole discipline aimed at proving their contribution to sales.
It worked. Today, marketing attribution is everywhere. Entire platforms exist to model, optimize, and prove marketing impact.
Here’s the ironic part:
Marketers built data systems to prove their value. Yet most data teams haven’t done this for themselves.
That has to change.
From Outputs to Outcomes: 5 Ways Data Teams Create Business Value
Data work is valuable - but only if you connect it to outcomes. Every high-performing data team defines their work around one (or more) of these five value drivers:
- Increase revenue or market share
→ e.g. build and sell a data product that directly generates MRR
- Reduce costs
→ e.g. automate a task to save X hours per month, at Y$ per hour
- Strengthen brand or reputation
→ e.g. ensure 100% GDPR compliance based on a checklist
- Improve relationships with important organizational stakeholders
→ e.g. deliver accurate investor reports with a 90% satisfaction rate
- Increase organizational knowledge or capability
→ e.g. support smarter decisions with predictive analytics, enabling marketing budget reallocation
Most data teams focus on category #5 - and that’s okay.
But too often, they stop at reporting knowledge instead of influencing decisions that lead to measurable business outcomes.
Let’s fix that with a concrete example.
Case Study: Turning Data Into Profit (Literally)
In 2024, I worked with a large omnichannel retailer - shoes and fashion, sold via e-commerce and physical stores.
Problem:
Their marketing team was optimizing ad spend based on ROAS (Return on Ad Spend). But that metric was flawed - it used revenue before returns.
In retail, returns can be up to 70% - and they vary drastically between product categories. Optimizing to top-line revenue meant they were over-investing in campaigns that looked profitable on paper but weren’t once returns were factored in.
Step 1: Build the Attribution Foundation
We created a marketing attribution data mart:
- Calculated revenue after returns, per category and campaign
- Predicted return rates using customer behavior, product type, seasonality etc
- Integrated data across digital and offline channels
Step 2: Redefine the Target Metric
We shifted their marketing metric from ROAS to POAS (Profit on Ad Spend). This included:
→ Revenue after returns
→ Margins on products sold
→ Cost of returns
→ Differentiating between new vs. existing customers
Step 3: Make the Business Case Before You Build
Here’s where it gets powerful:
Before starting the project, we modelled out the expected impact. A conservative assumption for improved marketing attribution is a gradual uplift in POAS from 1% to 10% over 24 months.
You can use your company’s actual marketing spend to estimate the potential profit uplift. For significant $100K+ monthly ad spends, even modest improvements translate into significant gains.
Then compare this projected uplift to the cost of building the data mart. In most cases, this kind of initiative conservatively yields a 5X+ ROI - making it an easy sell for leadership.
And once the project is underway, you can measure and refine actual uplift, using the data infrastructure now in place.
Step 4: Connect to Action
We sent predicted customer lifetime values (CLV) and not Revenue before Returns into Google Ads.
That allowed the marketing team to automatically optimize ad spend in real time.
One more thing: We didn't go directly from optimizing on Revenue before Returns to predicted CLVs. We iterated and shipped the smallest thing - moving from Revenue before Returns to Revenue after Returns first.
Bottom Line
If you want to be seen as a strategic partner in your company, you can’t just build dashboards or improve ETL speeds. You have to own your contribution to real business outcomes and prove it.
Here’s what to remember:
- Don’t accept the victim mindset. Influence the outcome.
- Anchor your work in one of the five core value drivers.
- Tie each project to measurable change in revenue, cost, or risk mitigation.
- Iterate. Start with what you can measure today. Then evolve.
You are a data team. That means you're built to create systems of evidence. It's time to use that skill to prove your own value.
Stop counting dashboards. Start counting impact.
See you next week.
Sebastian
P.S.: My data strategy masterclass "From Dashboard Factory to Strategic Partner" has helped hundreds of data experts from 40+ countries to create outsized business impact and prove it.
