Shared definitions
Teams work with documented definitions and agreed quality standards.
Make ownership, definitions and data quality part of your organisation’s day-to-day work.
Departments sometimes use different definitions and data for the same indicator. This leads to discussions about the figures and who is responsible. Data governance sets out ownership, definitions, access and data quality agreements.
An application only becomes meaningful when it fits a specific task and the information available for it.
The setup is built around the most important data and specific applications. That is why we define in advance what the solution should and should not do, how its output will be checked and who remains responsible.
Teams work with documented definitions and agreed quality standards.
It becomes clear who manages the data and who takes responsibility for improvements.
Dashboards and AI can rely on data that is fit for its intended use.
We start with the report or application that needs the data.
We map out sources, ownership and quality issues.
We document definitions and make quality measurable.
We address causes at source, during processing or in how the data is used.
We detect changes in quality or definitions.
This service is relevant for organisations with multiple data sources and reports. The exact setup depends on the process, the available data and the risks associated with the application.
We therefore start with a clearly defined application and only expand once the initial setup works in practice.
This service addresses the following challenge: data governance organises data ownership, definitions, access and quality standards. How it works in practice depends on the process and the data available.
The service can be relevant to organisations with multiple data sources and reports. A clear need and usable information matter more than the size of the organisation.
That depends on the application. We start with the question you need to address, then determine which data or sources are necessary and suitable.
No. For fixed, predictable tasks, conventional automation may be enough. We choose the simplest approach that addresses the challenge effectively and add complexity only when needed.
We define the scope of the application, check the results with users and put appropriate controls in place. Once it is in use, we monitor how it works and adjust the solution when the data, usage or context changes.
Want to explore where it makes sense to start with data governance? We’ll map out the challenge, the available information and the requirements together.