“Your data team closed 500 tickets last quarter? That’s great. But what did it actually do for your business?”

This provocative question often leads to uncomfortable silence among data folks.

Why? Because most data teams are stuck measuring their success through outputs instead of outcomes. They’re tracking the number of dashboards created, analyses completed, and stakeholder requests served – but missing the bigger picture of actual business impact.

I was no different for many years.

Let’s change that conversation.

The Output Trap

Ask a data leader about their team’s performance, and you’ll hear metrics like:

• Number of dashboards built
• Analysis requests completed
• Tickets closed per sprint

While these metrics can serve as leading indicators, they share a fatal flaw: they measure activity, not impact. It’s like judging a restaurant by how many ingredients it uses rather than the quality of its meals.

OKRs for the data team - not so easy…

I faced this problem for many years during my 17 years in the data space: Every 3 months, when we’d discuss new goals or OKRs (objectives and key results) for the next quarter - I struggled.

I always thought: “Sales and marketing have it so easy.” Their impact on the business is so direct: Sign more customers, increase conversion rates, reduce customer acquisition costs.

Easy peasy lemon-squeezy. 🍋

Determining OKRs for the data team - not so much.

The Value Creation Framework

Over time, I developed this framework to make “business value” created by the data team more measurable.

It really helps to prevent sleepless nights ahead of these OKR meetings.

Business value boils down to one thing: evidence-able positive effects on company performance.

For data teams, there are five concrete paths to creating this value:

1 | Increase in a company’s revenue, growth, or market share

• Building data products that customers pay for, for example:
• Creating analytics suites sold as subscriptions
• Developing proprietary algorithms licensed to clients

Note that I’m talking about direct revenue generation here. Not indirectly helping business teams make more $$$ - that comes later

2 | Decrease in a company’s cost

→ Automating manual reporting processes

→ Implementing LLM-powered content generation

→ Saving man hours in customer service through AI automation

3 | Enhancement of a company’s reputation or brand

• Achieving and maintaining compliance standards
• Building trust through data transparency
• Supporting sustainability initiatives with data

4| Strengthening of relationships between critical organizational stakeholders → Making investors happy with punctual, high-quality reporting

→ Creating seamless data sharing with critical vendors or banks

→ Building trust through reliable, timely insights

5 | Increase in organizational knowledge and capability of achieving organizational goals

• Reduce Churn by developing churn prevention models
• Reducing time-to-insight for analysts
• Ensuring critical report availability SLAs are met
• Increase marketing ROI via marketing budget allocation analytics

As opposed to the first point, these are indirect ways how a data team can make a company more $$$.

Matching Goals to Maturity

Here’s where many data teams stumble: they try to run before they can walk.

Your team’s objectives must align with your organization’s data maturity stage.

I like to use my Control Agility Framework for that.