
Imagine you’re leading an analytics team in a large global enterprise. You sit somewhere between federated and centralized, a hybrid model where your region gets direction from global teams but owns its own investments locally. You’re supporting domains like sales, marketing, and supply chain. You’ve got lean squads, agile workflows, stakeholder intake, and the tools to build good products.
But here’s the challenge: you can’t always tell if what you built was worth it.
Sometimes, the wins are clear. A dashboard leads directly to cost savings. A new data product reveals efficiencies in the supply chain. Everyone sees the value.
But more often, the picture is fuzzier. Stakeholders want a tool or metric they think will drive impact, but when it launches, no one’s quite sure if it did. And now it’s your team’s job to answer the question: Was this worth the investment?
In these moments, the question of ROI gets political fast. What I mean by “political,” is how the work and the perception of the work operate in the business. Who’s asking the question? What expectations were set? How are you tracking impact, and who’s owning the outcome?
If you’re trying to lead with integrity and secure future investment, you can’t wait for every product to prove itself post-launch. You need a way to shape the story as you go. That’s where portfolio thinking becomes an ally and strategic advantage.
This blog builds on an earlier post in our ROI series that introduced strategic bets and portfolio thinking. Here, we focus on how analytics leaders are applying those same principles—plus lightweight A/B testing—to navigate messy, ambiguous value conversations and earn the political capital needed to keep momentum.

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How Savvy Leaders Manage the Slippage Between Insight and Impact
When you’re serving multiple business domains—sales, marketing, supply chain—it’s inevitable that some of your analytics products will gain more traction than others. Some leaders know exactly what to do with the insights you provide. Others don’t. And that’s where marrying your portfolio approach to political savviness can be a true advantage.
To be clear, this is not a about pointing fingers. It’s about tacking the human hurdles to analytics adoption and recognizing this truth: the business’s ability to act on your work varies widely. One domain leader might take your enhanced forecast and drive measurable savings. Another might nod politely and keep doing things the old way.
So what do you do? If you’re smart, you rebalance.
You quietly prioritize the teams and leaders most likely to deliver value. Not because you’re playing favorites, but because you’re managing the ROI of your team’s time. In a world where analytics and AI demand likely exceed your resources, how you prioritize your team’s time and communicate value to the business should be a central pillar to your strategy.
Where A/B Testing Comes In
When product launches blur the line between “nice dashboard” and “real business driver,” lightweight A/B testing can help you clarify the difference. The trick? Don’t aim too high.
At the executive level, comparison is hard. You can’t give one VP a report and withhold it from another just to measure impact. But at the front lines—among managers, reps, and analysts—there’s room to experiment.
One team we’ve worked with put this into action by rolling out a reporting enhancement to one group of reps but not another. The report itself wasn’t revolutionary—just a smarter version of a standard forecast, using basic AI to smooth out anomalies and make trends more obvious. But when usage and outcomes were tracked across groups, the impact became clear. The reps with the enhanced report performed better.
It didn’t require a randomized trial. Just a controlled rollout, some disciplined measurement, and the courage to let the numbers speak.
If your current process looks more like build, then validate with SMEs, then release to all, you’re missing the window to test and learn. Instead of assuming the tool is valuable because one person liked it, give yourself the space to measure actual business lift. Then tell that story.
Make the Portfolio Visible and Build Trust
Even the best-intentioned product teams can get stuck in tough tradeoffs. When a squad commits to a “home run” use case—an ambitious, resource-intensive build that could take six to twelve months—it’s not just a matter of delivery risk. It’s a political bet.
Because while one stakeholder is cheering for the moonshot, others are quietly losing patience. They’re getting nothing. And from where they sit, it looks like your team is off chasing unicorns while their needs go unmet. Even if they intellectually understand the tradeoff, they start to disengage—and sometimes, to go rogue.
In this client’s case, several stakeholder groups responded by spinning up their own teams. These teams weren’t trying to undermine the central analytics function. They just wanted to get something done. And when they succeeded with a pilot or proof of concept, they came back asking for help “industrializing” it. That kind of fragmentation creates hidden costs and blurred lines of ownership.
This is why transparency matters. Transparency in delivery timelines and the logic of the portfolio itself.
Who’s getting what? Why? What bets are being made, and what evidence backs them? When everyone understands the decision-making process—even if they don’t always like the outcome—you retain trust. Without it, you get duplication, confusion, and resentment.
Use Your Network to Align and Amplify
One way to stay ahead of these dynamics is to formalize your internal D&A network. For instance, we’ve observed clients pull together all the key players, from central and adjacent analytics teams to data engineering leads, and even business leaders with analytics functions in their domain. The goal wasn’t to create a new layer of governance. It was to create visibility.
In these forums, teams share work, compare roadmaps, and coordinate rollout plans. They help each other avoid duplication and identify what’s ready to be scaled. They also create a clearer path for stakeholder ideas to evolve from pilot to production.
That last step is critical. A forum like this gives you the organizational infrastructure to test value early, measure impact, and decide what’s worth full-scale investment.
That’s how portfolio thinking becomes real, where there’s a working agreement among stakeholders with shared interest in what gets prioritized and why.
Final Thought: Be Strategic with Your Scarcity
In analytics, for most of our clients, the question isn’t whether you have enough demand. It’s how you manage it. No team has the resources to say yes to everything. The most effective leaders don’t just triage requests. Instead, they shape expectations, structure their portfolio, and communicate tradeoffs in ways that build credibility, not conflict.
That means being politically savvy without being political. Not every stakeholder is equally skilled at translating data into business value. Not every executive understands what goes into scaling a product. Your job is to help them see what’s possible—and what’s worth it—by creating mechanisms that reward good bets and test assumptions early.
A/B testing is one of those mechanisms. It’s a tool that gives you evidence to show what’s working, where the impact resonates most, and why a certain approach deserves investment. And critically, it builds a case for the ideas that might otherwise get buried under louder requests.
So when the next big ask comes in, ask how you’ll prove it was worth building. That’s how you move from delivery team to strategic partner. And that’s how you start turning pockets of demand into a sustainable, scalable, value-driven portfolio.

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