How to Make Data Work for What’s Next

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The push to adopt AI is forcing a lot of organizations to take a harder look at their data. Leaders want to take advantage of new tools and technologies, but they’re starting to see that their current data isn’t set up for what’s coming next. It’s not just about the quality of the data; it’s also about purpose. Most of the data they’re working with was set up for reporting or compliance, not to support real-time insights and decisions or future growth.

To move forward, there needs to be a shift in focus–from making the most of the data on hand to defining what’s needed to reach tomorrow’s goals. What should be measured? What information actually matters? And how can data show up in ways that support the decisions people are making every day? Here’s how to make data work for where your organization is headed.

Start with the End in Mind

Too often, companies begin by auditing the data they already have. A better question is, “What outcome are we trying to drive?” Whether it’s scaling operations, improving retention, or guiding smarter investments, the path forward starts with understanding where you want to go.

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Once the goal is clear, the next step is to decide what actually needs to be measured. What information will help track progress? What needle changes do we need to see to know if we’re making progress in the right direction, and which data sets does it come from? What’s missing? It’s not about having everything—it’s about having just enough of the right data to support the decisions that matter. Measure only the signals you’d bet your bonus on.

Find the Minimum Viable Data

Not everything needs to be measured. The goal is to curate the data, pulling in what’s most useful rather than everything that’s available. Focus on what’s going to help people make decisions in real time. Some metrics help you look ahead, while others explain what already happened. A good mix can be helpful, but only if it still aligns with the outcome you’re tracking.

This shift can feel unfamiliar. Many teams are used to starting from their existing systems–what’s already tracked, what can be pulled from a dashboard–and working backward. But that often leads to noise or gaps. Managing too much data isn’t just overwhelming; it’s also costly. Teams spend time storing, maintaining, and cleaning data that often doesn’t lead to better decisions.

The better move is to begin with the problem: What decision are we trying to inform? What would we need to know to make it with confidence? That’s how you get to the minimum viable data that’s actually useful.

Build Trust

Trust in data doesn’t come from having a perfect dashboard. It comes from seeing numbers that match what’s happening on the ground and satisfy intuition. When the data reflects what teams already suspected, it reinforces that they’re working with something they can rely on.

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Trust is built in small moments. When early reports reflect what people expect based on their lived experience, they begin to rely on the system. Over time, this creates space to introduce new insights and expand usage, but it starts with alignment and small wins.

That means cutting down on noise and being clear about definitions. If one report shows 25% attrition and another shows five people left a 50-person team, people are going to start asking questions. Standardizing how metrics are calculated (and making those choices visible) helps reduce confusion and builds confidence over time.

A stronger data culture isn’t just about systems. It’s about building skills and helping people see how their work connects to outcomes. When data reinforces what people already know and shows up in context—visually, interactively, and on time—it becomes a tool they trust, use, and want to leverage.

Lay a Solid Foundation

A sturdy data foundation starts with accountability: Name an owner for every critical dataset to safeguard purpose and quality. Build boringly repeatable pipelines—raw to trusted—using version-controlled, automated steps so breaks get caught early.

Establish a shared language through glossaries and metric contracts to ensure that “customer” or “churn” means the same thing everywhere. Choose tools that centralize logic yet allow many teams to explore one source of truth, avoiding dashboard silos.

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Finally, wire in observability and feedback loops—latency, failures, user confidence—so the system self-reports issues before trust erodes. With these pieces locked in, analytics and AI projects have bedrock to stand on instead of shifting sand

Iterate and Improve

The most useful data strategies evolve. Metrics that were useful six months ago may no longer reflect current priorities. Teams should build in regular checkpoints to reassess what’s being measured and why.

This is where governance becomes an enabler, not just a checkpoint. It gives teams the structure to revisit decisions, update or retire outdated metrics, and introduce new ones. Creating time and process for this reflection is what helps organizations stay focused on what really moves the needle.

Start by identifying what’s working. If one team is consistently outperforming others, dig into why. Then look for ways to replicate what’s working instead of starting over.

Moving from Data Overwhelm to Data Insights

You don’t have to solve everything at once. Choose one priority, define the outcome, and figure out the minimum data needed to support it. Get the structure right and show what’s possible. Then iterate from there.

Data alone isn’t the solution. But when it’s designed around real goals, and when people trust what they see, it becomes a tool for real progress.

Take the first step toward making data work for what’s next. Identify one decision your team struggles with this week and map the minimum viable data required—then schedule a governance review within 30 days.

About the author: María Sara Roberts is a director at Propeller, where she leads digital transformation initiatives and oversees the firm’s data and business insights service line. With a focus on aligning business goals with actionable, insight-driven strategies, she helps organizations harness data to drive sustainable growth and operational efficiency. María Sara has partnered with organizations across technology, retail, hospitality, e-commerce, and the nonprofit sector, bringing a versatile perspective shaped by experience in engineering, marketing, operations, and training. Her expertise spans AI enablement, strategic planning, and analytics, and she’s known for her ability to bridge strategy with execution. With a foundation in consumer behavior, market research, and tech-enabled innovation, she approaches complex challenges with a holistic and data-informed approach and designs solutions that are built to scale and evolve. Maria Sara holds a master’s degree in data analytics and business intelligence from INCAE Business School and a bachelor’s degree in business management and entrepreneurship from Babson College.