How to Answer "What Has Your Team Delivered?" Without Listing Dashboards
The Data Leader's Reset: From Reporting to Decisions (Part 3 of 3)
A few years ago, My boss’s boss’s boss set up a quarterly one on one with me. I was surprised, a little scared and thought I was probably about to get fired. Fortunately, that wasn’t the case – it was just a check in with a new leader – and I learned a lot from those conversations.
I went into the first meeting with a list of the things my team had done and was working on and rattled it off while he listened politely. When I’d finished my long list of what we had done over the last few months, he asked a very simple question… “What are we doing differently?”
I – ever ready with an answer – went back through the list again, talking about the data we were sharing, who it was going to and… He gently stopped me and explained he was really asking how the work we were doing was impacting the business.
I was telling a story with deliverables I should have been telling with outcomes.
The last two weeks covered Phase 1 of a data leader reset (understanding what’s happening, getting backing) and Phase 2 (building decision products, retiring the graveyard). Phase 3 helps you tell the story of the impact you’re having and build a sustainable system so you don’t slip in the future.
This is the third of three posts summarizing a longer guide in the Penguin Analytics store.
Here’s Phase 3.
Build the impact story while you can still remember it
Most data leaders – including me – find “show me the value” uncomfortable. You’ve been asked before, and the answer has always felt thin or inflated. The reset gives you something more honest.
You’re not calculating a precise ROI. You’re building a credible narrative around three kinds of evidence.
The first is usage. For each decision product, track who looks at it, when, and how often. Are the decision-makers checking it before the meeting it supports? Is usage clustered around the decision cadence or scattered randomly? Usage that lines up with decision timing is strong evidence that the product is being used for its intended purpose.
The second is process. What changed in how the team spends its time. How many dashboards retired. How many duplicate metrics consolidated. How much manual reporting was eliminated. These are tangible numbers, and they hold up to questions in a way that “data-driven culture” claims don’t.
The third is narratives reinforcing how it’s used for decisions. Three to five concrete stories where analytics influenced a real call. Not “we built a dashboard.” Something more like “the pricing team used the segment margin view to tighten Mid-Market discount bands in Q3, with projected margin recovery of two hundred thousand dollars.” The story doesn’t need to be precise to the dollar. It needs to be specific enough that the stakeholder would nod and confirm it happened.
Package these into a short update for your manager and, where appropriate, for stakeholders and leaders. A one-page summary or a couple of slides with a few bullets and a few stories is usually enough.
Also be honest about what you haven’t solved. A credible impact story presents the wins, identifies the gaps and a plan for fixing them. A leader who only reports good news is a leader nobody trusts.
Turn the soft filter into a real system
The soft filter you ran in Phase 1 worked as a temporary measure. Now you need to expand that into something durable.
Once a week, review new requests with the team. Classify each as decision-linked, informational, or noise. Decision-linked work gets scoped and scheduled. Informational requests get a lighter touch or a redirect to self-serve. Noise gets a clear no with a reason.
Give your team the language to handle the common cases without escalating to you. “We can support that, here’s what we need to scope it.” “That’s available in self-serve, here’s who can walk you through it.” “That doesn’t fit our current priorities, bring it back when it’s tied to a decision.”
When you defer or decline, write it down. A simple log of “we chose not to do X because Y” protects you when someone later claims they weren’t heard. It also creates a visible record that your team makes deliberate choices, rather than being overwhelmed.
The intake system needs to be simple enough that your team runs it without you and doesn’t skip steps. If every decision requires your personal judgment, you’ve built a bottleneck. If every request requires an hour meeting to scope it and 1,001 intake questions, you’ve built a different kind of bottleneck.
Teach your team the new model
The reset only sticks if your analysts internalize it. You need them to think decisions before dashboards every time a new request shows up.
Reinforce the intake question in your 1:1s. The first few times feel awkward. After that it becomes habit. Review framing in your work reviews, not just SQL. A technically perfect analysis with no decision framing is half-finished work.
For each decision product, name an analyst who owns it end-to-end. The relationship with the decision-maker, the data quality, the delivery, the iteration. Ownership means they present it, defend it, and improve it. It also means they get the credit when it works.
Celebrate the right things in front of the team. When someone’s work changes a decision, make that visible in team meetings, in your update to your manager, in Slack. When someone launches a new dashboard, ask what decision it serves. The signal you send about what counts as success will shape behavior faster than any process document.
Teach the organization too
Your stakeholders also need to learn the new model. Some will adapt quickly. Others will keep sending “can you just build me a dashboard?” requests for months.
Run short enablement conversations with your key stakeholders. Cover what changed, how to request support, and what to expect. The goal is to make expectations clear enough that the common interactions go smoothly without you stepping in.
Some stakeholders won’t get it until they see a concrete example. The decisions you supported in Phase 2 give you those examples. When you can say “for the pricing meeting, we used to do X, now we do Y, and the team spends less time looking at dashboards and more time talking about trade-offs,” you’re making the change real.
What should feel different by Day 90
If the reset has worked, a few things are noticeably different:
You spend less time in meetings answering questions about numbers and more time talking about the business.
Your team talks about decisions and outcomes more than dashboards and tickets.
Stakeholders bring you into conversations earlier, when decisions are still forming, instead of at the end when they need a chart.
Your dashboard surface is smaller but more used. The things you maintain are the things that matter.
You can explain what analytics delivered this quarter in three sentences that a non-technical executive would understand and believe.
You still have open problems. Some stakeholders will still default to “just build me a dashboard.” Some analysts will need more time to internalize the new model. The self-serve layer probably still needs work. That’s expected. The measure of the reset is not whether everything is fixed, but whether the team, the stakeholders, and the system you’ve built are pointed in a better direction and moving under their own power.
A little assignment
Write a three-sentence answer to the question “what has analytics delivered this quarter?”
Not what it produced. What it delivered. Decisions changed, outcomes shifted, processes improved.
If you can’t get to three sentences, you don’t have an impact story yet. That’s useful information. It tells you what to spend the next thirty days building.
If you can, save it. That’s the foundation of how you talk about your team’s work for the next year.
This concludes the three-part series on running a 90-day reset for a data team. The full guide includes worked impact-summary examples, sample language for the harder conversations, and four scenario walkthroughs from real resets. Available in the Penguin Analytics store.