Data maturity assessment – prerequisite for robust data governance

In my experience working with clients on data initiatives, one observation comes up repeatedly in conversations with leadership teams. Many organizations consider themselves data-driven. They made significant investments in cloud data platforms, data engineering teams and deployed numerous dashboards to production.

However, implementing modern data stack is only the tip of the iceberg; still, data is often spread across multiple systems, discrepancies in business terms and KPIs do not allow consistent analysis, and responsibility for data is not defined. All of this does not allow to leverage data assets for decision making.

What can be done to avoid such pitfall? Before implementing governance structures or launching major data initiatives, it is important to understand the current state of data management in an organization. This is where data maturity assessment comes in.

How to approach a data maturity assessment

Contrary to popular belief, maturity assessment is not a technology audit. It does not focus strictly on which BI tools or data platforms an organization uses. Primarily, it evaluates how the data is used and managed. At BitPeak, we typically structure this analysis using a framework inspired by DAMA-DMBOK, which provides a comprehensive view of data management disciplines. We look at several key areas, such as data governance (processes, roles and responsibilities), data architecture, quality management and metadata.

A meaningful assessment needs to capture perspectives from across the organization. The process begins with stakeholder identification. We use the organizational structure to ensure broad representation across departments.

The next step is collecting insights at scale. Surveys help build a broad picture of how data is used across the organization. They allow employees in different roles to share their perspectives on topics such as data availability, data quality, and decision-making processes.

In most of our projects, I have observed a “snowball effect”, where, upon learning about the surveys, new departments/employees request to be involved so that they can also share their view. They are eager to provide inputs that prove very valuable in the assessment. Such a survey includes both quantitative and qualitative measures and must be properly designed to minimize bias and uncertainty in the answers.

Once this broad view is established, we move on to conducting workshops with key stakeholders. These sessions allow us to focus on critical domains and processes and to explore the survey inputs in more detail. Workshops help to better understand the context and reveal the underlying causes behind data challenges.

An often overlooked yet key aspect of the assessment is ensuring alignment with organizational strategy and business goals. Data initiatives cannot succeed in isolation; they must support the strategic priorities and key business processes. The organization’s strategy is one of the important inputs when performing the assessment.

Discovering challenges

In my cooperation with different organizations, I observe similar patterns. One of the most frequent issues is unclear data ownership. Data is widely used, but there is no formal responsibility for maintaining definitions, ensuring quality, or documenting datasets.

Another challenge is data availability. Even when relevant data exists, employees often struggle to access it or understand where it comes from – sometimes even spending weeks trying to obtain a dataset that is already used in the company (yes, it is a true story)!

We also frequently encounter inconsistent business terminology and KPIs. Different teams use different definitions for the same metrics, making cross-departmental and strategic reporting error-prone and time-consuming.

Identifying existing strengths

An assessment not only highlights gaps and challenges. There are always local initiatives and good practices already in place, waiting to be discovered. These might include data dictionaries, informal ownership roles, internal standards, or solutions developed by specific teams. Identifying these elements is important because they can often be expanded and standardized across the organization rather than built from scratch. Unfortunately, these types of initiatives lack scalability and are rarely known or accessible to a wider group of employees.

From diagnosis to action

The main value of a data maturity assessment lies in what it enables.

First, the assessment helps determine priority areas for pilot initiatives, focusing governance efforts where data has the greatest impact on business processes.

Second, it helps identify candidates for data owners and data stewards – individuals who already play a key role in managing data within their domains.

Finally, it provides input for the evaluation and recommendations on data management tools, ensuring that technology decisions are aligned with organizational needs.

Ultimately, a data maturity assessment is much more than the diagnosis of the current state. It is an important input for defining where the organization wants to be in terms of data management – and how it can realistically get there.

I increasingly notice that both in Poland and abroad, organizations have better awareness and see the benefits of a more structured approach to their data. If you are still trying to navigate your way in this complex landscape, I encourage you to see how a data maturity assessment conducted by BitPeak helped LOT Polish Airlines in building and implementing a comprehensive Data Governance Strategy.

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