Data is everywhere - in systems, reports and dashboards - but if it is not deployed in the right way, it remains just a collection of numbers. At housing associations, we see the ambition to work data-driven. But the question remains: how to use data as a strategic tool? And how do you deal with the growing flow of information, and how do you translate insights into concrete actions?

From ambition to approach

Leveraging data as a strategic tool requires more than technology. It requires vision, change in the organization and clear direction on data quality. That also means involvement at all levels, from the shop floor to the board.

For a successful approach from Data to Decisiveness in which you work effectively and successfully data-driven, it is necessary to pay attention to the managerial aspects (how are choices made and decisions made?). And to look critically at the organizational-cultural aspects.

Yet what this change means for the way you work, make decisions and collaborate is often underestimated.

In this blog, four recognizable phases will give you insight into where your organization is now and what it needs to move forward.

Phase 1: Data strategy and governance

The foundation: vision, ownership and organizational embedding of data-driven work.

In the first phase, it is recognized within the organization that data-driven work requires more than technology alone. This insight does not come naturally, but requires focused attention and awareness. Data-driven work requires a clear direction and clear frameworks. Therefore, an overarching data strategy is developed, describing what role data plays in achieving organizational goals.

At the same time, partly due to this awareness, there is a growing need to actively organize data governance: who is responsible for what, how is ownership invested and what rules apply to the use and interpretation of data? In the housing corporation sector, frameworks such as CORA and VERA offer guidance in this regard, but in practice their application often proves to be recalcitrant. Differences in maturity, fragmented source systems and unclear ownership underline the importance of a well embedded data strategy and governance.

Without a common basis, the chances of fragmentation are high, differences in interpretation arise, and efficiency remains elusive. This phase thus lays the foundation for further development and marks an important tipping point: the transition from ad hoc data use to structured and strategic data management.

Phase 2: Data architecture

The technical foundation: a future-proof and standardized data foundation.

As ambitions grow, so does the need for a cohesive technical structure that supports data-driven work. In this phase, you focus on setting up a future-proof data architecture that moves with changing ambitions. Often this means letting go of outdated customization and moving to a modular, scalable and easily linkable setup. Investments should be made in platforms and standards that not only meet current information needs, but also accommodate future innovations such as AI, advanced analytics and processing of unstructured data.

For housing associations, for example, this means weighing standard solutions versus customization, where practice shows that source systems are subject to changes that affect a data platform. This requires flexibility in architecture and a clear roadmap for BI development. The challenge lies in finding a balance between flexibility and manageability. This phase thus forms the technical backbone for sustainable data-driven development.

Phase 3: Implementation, adoption and acceptance.

The human side: change in thinking and doing requires guidance and support.

Technology and strategy are important, but the success of data-driven work depends on the people who work with it. This phase is therefore not only about implementing systems, but especially about embracing new ways of working. Employees must actually start using data in their daily work (adoption), and experience that this leads to better decisions and more effective action (acceptance).

For housing associations, this often means that different departments - from leasing to property management - need to start working together differently, sharing information and taking ownership of data use. This requires clear communication, practical support and room to learn. By investing in guidance, training and example behavior, confidence in data use grows and enthusiasm increases. The first visible results reinforce this process. Employees gradually develop the skills and insight to apply data in a practical and relevant way. Step by step, this creates a culture in which data-driven work is no longer seen as something extra, but as a natural part of daily practice.

Phase 4: Management and maintenance

The safeguard: structural embedding with attention to capacity, cost and continuity.

Once the first parts of the data chain are in place, the question arises as to how they can be structurally managed and further developed. This step revolves around organizing continuity: who manages the data sources and who is responsible for the quality of the data? Insight is gained into the capacity and competencies needed for this, both inside and outside the organization, and the structural costs involved.

By organizing this properly, the organization can eventually reap the benefits of data-driven work. Reliable data is structurally used to make better decisions, perform analyses and apply advanced technologies such as predictive models and AI within business processes. There is growing awareness that data-driven working is not a one-time effort, but an ongoing process that requires professional management. This phase is thus crucial for ensuring sustainability and scalability: without solid management, the data chain becomes fragmented and further growth of housing associations comes under pressure.