Data Strategy
For many public sector organizations, working with data and analytics is not new. Many organizations have taken steps under the heading of information-driven or data-driven work. Often we see that organizations have established a data vision and strategy, in which the strategic principles for the use of data are elaborated. The challenge for many organizations lies in connecting these strategic principles to the data and analysis processes in operations. Many organizations struggle to scale up successful pilots, partly because data expertise is fragmented in the organization and because of limited data awareness and skills (“data literacy”) among employees in the primary processes.
Integral model for the data-driven organization
In many organizations there are valuable building blocks for the data-driven organization, but they do not succeed sufficiently in operationalizing them and connecting them into one whole. To help organizations take the next steps in the field of data-driven working, we use an integral model for data-driven working. We see data-driven working as a continuous process through which the organization brings together internal and external data, analyzes it and applies the resulting information to increase social impact. The potential for this is enormous, but it also presents organizations with challenges and a different way of working. After all, the use of data only has value if the new insights are also turned into meaningful action.
Data-driven work is more than IT
It is an interplay of technology, structure and people in which collecting, recording, processing, analyzing and managing data is central. This requires an integral approach to the organization where not only the technology is considered, but also the rest of the organization. Together with its clients, Highberg has developed a model that does justice to all these facets. Besides paying attention to technology, the model also explicitly considers organizational, cultural and control aspects. The model helps organizations to actually extract value from data, taking into account (legal) frameworks such as privacy and working with data in an ethically responsible manner.
Our approach to developing the data-driven organization
We use the data-driven organization model as a capstone in our approach. We proceed as follows:
We often start a project with a 0-measurement, in which we determine the maturity of the organization based on the data-driven organization model. We do this per pillar in the model. We carry out the baseline measurement as easily as possible, using various research methods (questionnaire, interviews, workshops). The baseline measurement results in a roadmap with projects that the organization must implement to take the next step in realizing the data-driven ambitions.
Before we proceed to implementation, we make a design that fits the ambitions of the organization. We look at what minimum structures, processes, people and technology must be present in the organization in order to realize the organization's ambitions (“minimal viable organization”).
“Change by doing.”
With the roadmap and design in hand, we start the implementation. Here, we start from the principle of “change by doing.” This means that we introduce new structures, processes, technologies, etc. by directly applying them in data projects. This immediately creates practical experience with the new concepts, making them more firmly rooted in the organization. After realizing the projects in the roadmap, we perform a follow-up measurement to determine where the organization stands. Depending on the ambitions of the organization, this results in a new roadmap and taking the next step.
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