How to build a data strategy that actually works: distilled insights from our podcast appearance

Introduction

Data strategy is no longer just a nice-to-have for businesses—it’s a necessity. Large, complex organizations need a clear and actionable strategy to stay competitive in today’s market. But building and executing a data strategy that delivers real value is a challenge. So, where should you begin, and how do you align it with your broader business goals?

In our work at FELD M, we’ve seen firsthand the growing importance of data strategy across industries. Companies of all sizes are now realizing that without a strong foundation, their efforts to leverage AI, analytics, and other advanced technologies will fall short. The issue is simple: you can’t build shiny use cases without a solid strategy to support them.

As AI matures, the initial excitement fades, and businesses are left facing the hard work of creating a strategy that can deliver sustained results. The good news is, with the right data strategy in place, organizations can unlock real value from their data—enabling them to scale, innovate, and stay ahead of the competition.

In the latest episode of UNF#CK YOUR DATA with Christian Kruger, our experts Dr. Julius Kayser and Dr. Isabelle Kes shared their insights on why data strategy remains the quiet hero behind every successful AI and analytics initiative.

If you’d prefer to dive deeper, you can listen to the full podcast episode here.

Strategy and execution must go hand in hand

“It doesn’t have to be an either/or. Strategy and execution can work together like a dynamic duo.”

— Julius

A common misconception when it comes to data strategy is the idea that you must choose between strategy and execution. Some believe that strategy is a big, upfront effort that needs to be done first, followed by execution. Others rush to execution without thinking through a solid strategy. The truth is, strategy and execution must work together.

In my (Julius's) experience, strategy is most effective when it’s directly tied to actionable steps. It’s about setting clear goals, identifying use cases, and then moving quickly to bring those ideas to life. The mistake many organizations make is overcomplicating strategy, thinking it has to be exhaustive and rigid. But in reality, it’s much more effective to start small, iterate, and adjust the strategy as you go. Strategy without execution is just a document—execution without strategy lacks focus.

At FELD M, we’ve worked with leading companies, including SBB, to help align their data strategy with operational goals, turning high-level strategies into successful, measurable outcomes. This hands-on approach has proven to be effective in large-scale projects where both strategy and execution are critical.

Data strategy is no longer optional

“Digital transformation has shown companies that data is not just useful — it’s essential, and it needs intentional direction.”

— Isabelle

At FELD M, we often work with organizations that still treat data as a secondary asset. They collect data, store it, but fail to use it strategically. The problem with this approach is that data, when properly managed, can be a competitive advantage—and without a strategy, it’s just noise.

Data isn’t just useful—it’s critical. As I (Julius) pointed out, “Data is the oil of the 21st century,” and in today’s environment, businesses that don’t treat it as such risk falling behind. But many organizations don’t realize the importance of a strong data strategy until it’s too late.

“Often the importance of data strategy only hits people when their AI doesn’t deliver because the data behind it wasn’t ready.”

— Julius

This is where things get tricky. Businesses might rush into AI or analytics projects without first laying the groundwork with a solid data strategy. This leads to disappointing results. The key takeaway here is clear: data strategy is not optional. It needs to be intentional and aligned with broader business objectives.

For example, we helped Jobcloud, a leading job platform, strengthen their data governance and strategy to optimize hiring processes, enabling them to make data-driven decisions that lead to long-term business growth. This is the kind of transformation that a solid data strategy can enable, ensuring that AI initiatives deliver real value.

Don’t overplan — start with real use cases

“What matters is getting into doing — getting started with real use cases that tie back to business goals.”

— Julius

One of the most frequent challenges companies face is overplanning. It’s tempting to get stuck in the details of a comprehensive strategy, spending months or even years refining a plan that never gets executed. However, the best way to build a data strategy is to start small and test the waters with real use cases.

In my (Julius’s) experience, it’s essential to move quickly from strategy to action. Start with use cases that directly support business goals and provide measurable outcomes. These early use cases will give your team valuable insights into what’s working and what needs to be adjusted.

Executives often worry about getting a perfect strategy, but the reality is that imperfection breeds learning. By starting small and iterating based on results, you can continuously improve and scale your data strategy over time.

A great example of this approach is our work with Bexio, where we helped the company focus on practical use cases to streamline operations, making the transition from strategy to execution smoother and more impactful.

People are the linchpin of any data strategy

“If you don’t bring your people along, and give them the right processes and enablement, even the best technology won’t help you.”

— Isabelle

Data strategy isn’t just about technology—it’s about the people who will be executing it. No matter how sophisticated your tools or models are, your data strategy will fall short if your team isn’t aligned, skilled, and motivated to use data effectively.

At FELD M, we emphasize the importance of the human factor in any data strategy. It’s not just about building a great data infrastructure or implementing cutting-edge technologies—it’s about ensuring that the people who are responsible for executing the strategy have the right skills, the right tools, and the right mindset.

As I (Isabelle) often say, “Technology is only as good as the people who use it.” Without empowering your team, providing them with the right roles and responsibilities, and fostering collaboration across departments, your data strategy won’t be able to deliver its full potential.

This approach is key to the work we’ve done with media companies. From European public broadcasters to online news publishers, it's clear that cross-functional collaboration consistently leads to more innovative use of data, resulting in more effective business strategies and outcomes.

Measure success early and often

“What very, very often doesn’t work out is sustainably measuring success.”

— Isabelle

Measuring success is an area where many data strategies falter. Without a clear way to assess whether a data project is working or not, it’s easy to waste time and resources on initiatives that don’t align with business goals.

In my (Isabelle's) experience, early measurement is essential. Before you start implementing any use cases, you need to define how you will measure success. This will ensure that your efforts are aligned with business outcomes and that any necessary adjustments can be made early in the process.

It’s not enough to just check off tasks on a to-do list—you need to track progress and outcomes. By defining success criteria upfront, you’ll have a much clearer view of whether a project is driving value, or if it’s time to pivot.

Align your data strategy with organizational goals – and organizational maturity

“You can’t build a data strategy in a vacuum. It needs to be grounded in the company's overarching business strategy.”

— Julius

A successful data strategy isn’t a one-size-fits-all solution. It needs to be tailored to your organization’s goals. This is where many companies go wrong: they try to implement an advanced strategy when they’re not ready for one.

As I (Julius) pointed out, “Data strategy needs to be grounded in your company’s overarching business strategy.” At FELD M, we use a maturity assessment tool to evaluate where organizations currently stand and identify the steps needed to move forward. It’s a pragmatic tool that helps businesses understand their readiness for data strategy and avoid the common mistake of jumping into complex initiatives too soon.

By assessing your organization’s data maturity, you can ensure that your strategy is realistic and scalable, and not something you have to abandon halfway through because it’s too ambitious.

Getting started: practical steps for leaders

If you’re ready to turn your data strategy from a concept into a reality, here are some practical steps to get you started:

1. Align your data strategy with business goals: Make sure your data strategy is tightly connected to your company’s larger objectives.

2. Focus on use cases: Start with clear, business-aligned use cases that provide measurable value.

3. Empower your people: Invest in your team—skills, tools, and a collaborative mindset are essential for success.

4. Measure success early and often: Set clear KPIs and track progress throughout the execution process.

5. Assess your maturity: Use a maturity framework to evaluate where you are and where you need to go.

By following these steps, you can create a data strategy that’s actionable, scalable, and aligned with your broader business goals.

Want a deeper dive?

For those who want to explore these concepts further, we’ve recently hosted a webinar that walks through the FELD M data strategy framework in detail. In this session, we dive deeper into how to develop a data strategy that’s not just theoretical, but one that creates real business value.

👉 Watch our on-demand webinar: FELD M's Data Strategy Framework Explained
👉 Learn more about our Maturity Assessment Tool (includes practical guidance and a free toolkit)

Conclusion

Data strategy isn’t just about the technology—it’s about creating a framework that connects your business goals to actionable steps, while engaging the right people and measuring success along the way. By following the iterative process outlined above, you’ll ensure your strategy remains agile and aligned with your company’s needs.

At FELD M, we’re using these insights to continuously improve our own data strategy. We hope you find these takeaways useful as you embark on your own journey to build a data strategy that delivers measurable, sustainable value.

About the authors

• Dr. Isabelle Kes: Isabelle has been successfully advising clients from various industries on data strategy, digital strategy, and in complex analytics & insights projects for over 10 years. She has been part of the FELD M team for 8 years. After studying business administration in Münster, she completed her doctorate on the topic of personalized advertising at the Chair of Service Management at the Technical University of Braunschweig. In addition to her work for FELD M, she teaches and researches at the University of Applied Sciences in Munich and the IHK Nord Westfalen on the topics of data strategy, data-driven companies, and organization.
• Dr. Julius Kayser: Julius’ expertise combines deep technological understanding with strong business acumen. With over 12 years of experience working with world-class leaders across product management, development, and implementation of data-driven solutions, as well as management and strategy consulting, he is ideally positioned to advise clients on data-driven decision-making. He also brings experience leading teams in fast-paced environments. Julius holds a PhD in physics, specializing in quantum information and quantum computing.