From Ambition to Practice: Why Data Governance and Data Management Make the Difference

Organizations today have more data than ever. Yet the question remains: how do you turn data into a reliable source of insight and a strategic steering tool? In this trilogy, we take you through the fundamentals of data governance and data management. Because only with clear frameworks, ownership, and structure can data not only reflect reality but also drive progress. 

We focus on administrators, policymakers, and professionals in government, semi-public institutions, and companies who are genuinely committed to working with data – not out of hype or obligation, but because it enables better steering, improvement, and innovation, and helps achieve societal goals within the limits of available resources and capacity. 

In three steps, we build from insight to application: 

1 Why are data governance and data management essential? 

2 How do you create control over and value from data? 

3 Where and when are data governance and data management necessary? 

Read along, get inspired, and discover how data can help your organization achieve both societal and strategic goals more effectively and efficiently. 

More and more organizations want to work data-driven and invest heavily in dashboards, algorithms, and data platforms. Yet many digital ambitions stall in execution. In practice, decision-making information is fragmented, definitions differ, and ownership of data is missing. Without a solid foundation – control, shared understanding, and clear responsibility – data becomes more a source of frustration than a driver of progress.

Organizations still often view data governance and data management as a technical or legal obligation, something for IT or compliance. But this image undervalues their strategic importance. 

Consider financial management: no organization leaves its budgets and expenditures completely unregulated. There are processes, frameworks, and responsible parties. The same should apply to data. Governance provides structure and direction: Who is responsible for what? How do we safeguard quality? And how do we use data in a responsible, goal-oriented way? 

Without such structure, fragmented data landscapes emerge with unclear ownership and inconsistent definitions. Dashboards are not traceable, data sources conflict, and no one knows who to turn to with simple questions such as: “Is this data correct?” or “Who ensures better data quality?” 

The core question is: how do you organize data so that it is usable, reliable, and available – not just for analysts, but for everyone in the organization who works with data?

At Highberg, we believe data governance and data management form the foundation of successful data-driven work. Not as an end in themselves, but as a means to truly harness the value of data. That’s why we advocate an approach that is: 

• Human-centered – we start with people. How do they work with data, what challenges do they face, and what do they need to succeed? 

• Maturity-driven – we assess where the organization stands in its data maturity and define a realistic growth path. 

• Focused – not everything needs to happen at once. We prioritize the data, processes, or departments where the greatest risks or opportunities lie. 

• Non-invasive – we build on what already exists. No one-size-fits-all blueprint, but an approach aligned with existing structures, roles, and processes. 

Our approach is aimed at embedding ownership, responsibility, and collaboration. We help set up roles such as data owner and data steward, develop practical frameworks, and strengthen data craftsmanship within the organization. 

We use internationally recognized models such as DAMA-DMBOK, FAIR, and the PBGL model, always tailoring them to the organization’s context, structure, and ambition. In this way, we bridge the gap from strategy to behavior, from policy to practice. 

This article is the first in a trilogy on data governance and data management. Here, we focused on the why: why are they essential for data-driven work? 

In the next article, we’ll zoom in on the how: how can organizations create control over their data and harness its value? We’ll distinguish between defensive and offensive data management and show how to balance the two for maximum impact.