Data Governance as a foundation for extracting maximum value from data

New European legislation such as the Data Act and AI Act sets stricter requirements for the use of data and artificial intelligence, requiring organizations to review their data policies. Data governance not only helps companies comply with these regulations, but also offers opportunities such as better decision-making, clear responsibilities, greater support for data-driven work, structure in approach and collaboration, resulting in better data quality and more effective use of technology.

But how do you ensure that the data you have available within your organization is used as effectively as possible? And that the data you make available is of high quality - after all, garbage in is garbage out - and also complies with set security and privacy requirements? The right attention and focus on data governance offers help.

In this article we will elaborate on the tools to embed data governance in the organization with the goal of making data actually that strategic commodity.

What is data governance?
Data governance is about developing and recalibrating a data strategy, designing & setting up a data management structure with clear data management functions and roles, and developing and recalibrating the policy and policy framework. Data governance is thus the foundation by which an organization structures and coordinates data initiatives across the organization. The goal of data governance is to ensure that data is consistent and reliable, delivering maximum value to the organization while being secure and compliant with laws and regulations.

It is also about developing and managing a portfolio of data management initiatives and guiding, coaching, training and supporting everyone who has a data management task or responsibility.

What do we see at organizations we come to?
We see a number of reasons that require thinking about data governance:

1. Organizations don't start using data overnight. Some teams or organizational units lead the way in creating value from data often in an exploratory form. We also welcome this as a catalyst for the rest of the organization. In order to move from this exploratory form to a manageable situation, however, as an organization you need to establish a vision and data strategy to rise above these exploratory initiatives.
2. Organizations see that the use of data happens within organizational silos and that data and also the understanding of the data is fragmented. A more integrated approach is then desirable, such as making certain data sources and data models available centrally for the entire organization instead of storing similar data and data models in different places, multiple times in different applications.
3. It is necessary to comply with laws and regulations. For example, for periodic reports to a regulator or being able to demonstrate that you are compliant as an organization. It requires a thorough administration of processes, data (quality) and clear ownership. Here you can think about recording how the data is created (tracking & consent) and who decides which colleague can use which data. In addition to legislators, good data governance helps build trust with customers, clients and future employees.
4. Avoiding business continuity risks. As the amount and complexity of available data increases, in addition to deterioration of substantive understanding of data, business continuity risks lurk. You can no longer lean on the knowledge in the heads of (a few) colleagues within the domain when data becomes an essential raw material for the value you deliver as an organization.
5. Attention, time and money are not inexhaustible. A need arises to get a handle on the various data initiatives and the effort and impact on the business objectives to deploy scarce resources appropriately. The business strategy is thus the basis for the data strategy, which in turn sets the course for structured data management.
6. For many organizations, the opportunities and, at the same time, the challenges presented by developments such as Artificial Intelligence are a trigger to put their data governance in order or to re-examine it. It is no longer just about the policy for internal data, but also the application of external data and the ease with which internal data ends up in external data technologies. In addition, having the right data and good data management often also drives a company's innovative power.

In short, enough reasons to give the proper establishment of data governance within the organization the right priority. But how do you start and what should you pay attention to?

How do you set up data governance effectively?
Of course, every company is unique, the circumstances and playing field are different as well as the level of maturity of the use of data. Nevertheless, we see many common denominators in the projects we have supervised.

1. Part of the existing: The most important thing we have learned in the projects on data governance is to make use of the existing organizational setup and not consider the rigging of a governance structure as a separate project. Delegate components to IT, Product Development, Compliance and Data & Analytics, for example. Make sure that these colleagues do explicitly accept their new task, because investing work in a function that has no sense, time, mind and priority for it has little to no negative impact.
2. Form and Vision: Define a data vision that aligns with the strategic vision of the organization(s). This will help at a later stage in making choices and priorities and forming a roadmap that supports the data strategy.
3. Link to strategic goals: Show how good data management contributes to business goals such as customer satisfaction, (product) innovation, compliance, cost savings or employee satisfaction.
4. Make it measurable and visible: Use KPIs such as data quality, data completeness, number of data breaches and compliance scores to measure progress.
5. Manage expectations: Let's start with the fact that data governance is not a gimmick or a side project, but a change in mindset, a process of continuous improvement and expansion. Both for managing expectations and getting the organization on board, it is important that this awareness is there when you want to get started. Urgency and sponsorship of this continuous process is thus critical to success.
6. Start small, scale smart: Start with a pilot in a specific domain and demonstrate the added value there. Use the insights and successes to scale up incrementally to other parts of the organization.
7. Consider what centralized, what decentralized: Provide central direction and decentralized implementation. Designate data owners and ambassadors, starting especially with the "coalition of the willing." Give departments the responsibility themselves, and facilitate with frameworks standards and the right tooling.
8. Share the successes: Work together here with marketing and internal communications, for example, to apply the right storytelling that everyone understands and where the added value is very concrete. This also triggers demand and creativity and contributes to support in other organizational units.
9. Secure awareness and training: Include it in e-learning modules depending on tasks, responsibilities and competencies. Organize workshops and/or lunch sessions to share practical examples and secure the use of tooling, for example.

The above focal points also ensure that data governance becomes a means to achieve the organization's strategic goals by embracing and securing a concrete and scalable way of data-driven and data-driven work in the organization. Setting up data governance properly secures structural grip.

Structural grip on data starts with governance
In short, data governance is not an end in itself, but a crucial foundation for an organization to actually extract value from data. At a time when data-driven work and the use of AI are becoming increasingly important, it is essential that data is reliable, findable, secure and compliant.

By approaching data governance not as a separate project, but rather by cleverly linking it to existing structures and processes, you increase the chances of sustainable anchoring in the organization. It requires clear ownership, employee involvement and a clear data strategy linked to organizational goals. Only then is data no longer seen as a byproduct of ICT, but as a strategic raw material for lasting value creation.

Together with my Mobilee colleagues, we like to think along with you how you can lay the foundation for maximum value from your data with data governance.

By H. van der Boog & R. Horree