Clear ownership
Establish who is responsible for datasets, definitions, quality, access and data use.
Data governance
Professionals reviewing policy documents at a table
Clear agreements for responsible data use
Datalab helps public and social organisations develop practical data governance policy. Not policy for a drawer, but clear frameworks that enable your organisation to work with data responsibly and effectively.
Getting a grip on data takes more than technology
As more people work with data, new questions arise. Different departments use different definitions. Access to data has grown organically. New analysis methods and AI offer opportunities, but do not always fit within existing policy frameworks. Good data governance policy brings structure.
Consistent agreements
Prevent each department from deciding what data means, how long it is retained or what it may be used for.
Responsible use
Clarify in advance which forms of analysis and data use fit within the goals, values and responsibilities of the organisation.
Room for innovation
Clear frameworks help staff know more quickly what is possible and when additional considerations are needed.
Policy frameworks
From principles to practical data policy
Data governance is not just about privacy or information security. It concerns the full set of agreements through which an organisation maintains grip on its data and how it is used.
Which subjects belong in governance differs per organisation. Datalab helps with policy on ownership, definitions, data quality, access, data sharing, privacy, analysis, AI and documentation.
Case study: data governance for DUTCH
DUTCH – Digital United Training Concepts for Healthcare – is a collaborative programme involving Amsterdam UMC and LUMC, among others. The programme develops new ways to train and upskill healthcare professionals using digital learning tools and physical and virtual simulation.
Within such an innovative programme, many opportunities to analyse data arise. It is precisely then that it is important to agree in advance what may be done with that data.
Datalab supports DUTCH in developing policy for all forms of data analysis. We look not only at what is technically possible, but especially at the organisational and policy choices behind it.
- Which applications fit within the objectives of DUTCH?
- Which principles apply for responsible data use?
- Which roles and responsibilities are needed?
- Which additional considerations are needed for new forms of analytics and AI?
Value
What does data governance deliver?
Good governance policy gives staff clarity without unnecessarily restricting them.
Experience with organisations that want to work responsibly with data
Datalab works for public organisations, healthcare, education, retail, accountancy and other knowledge-intensive sectors.
They also trust Datalab
Approach
As light as possible, as robust as necessary
Data governance need not become another bureaucratic layer. A smaller foundation needs different rules and processes than a municipality, province or national partnership. It also matters whether data is used only for internal reports or for scientific research, public decision-making or AI.
That is why we do not start with a standard policy document. We first look at the organisation, existing policy, the data being used and the risks that actually apply. From there, we determine which agreements are needed.
Independent advice with technical knowledge
Datalab combines experience with data strategy and policy development with practical knowledge of data warehouses, BI, analytics, machine learning and AI. We are not tied to specific software vendors or platforms.
1.
Map the organisation and data use
We examine which data is used, for which purposes, which parties are involved and which agreements already exist.
2.
Determine principles
Together we set the principles that guide data use: around ownership, transparency, quality, access and responsible use of analytics and AI.
3.
Develop policy and responsibilities
We translate the principles into concrete roles, decision rules, procedures and policy agreements.
4.
Make policy workable
A policy document only has value if people can work with it. That is why we pay attention to practical implementation, exceptions, decision-making and periodic review.
Trade-off
Data strategy or data governance?
Data strategy and data governance are closely related, but answer a different question. A data strategy determines what your organisation wants to achieve with data and which steps are needed. Data governance determines under which agreements and responsibilities your organisation works with data.
Does your organisation still lack direction in its data use? Then it may be sensible to develop strategy and governance together. Are the ambitions already clear, but is policy, ownership or rules lacking? Then data governance can also be established independently.
Build further
Frequently asked questions
Questions about data governance
What is the difference between data governance and privacy policy?
Privacy policy specifically concerns the processing and protection of personal data. Data governance is broader and also covers topics such as ownership, data quality, definitions, access, documentation, analyses and decision-making.
Do we need to create policy before starting with data analysis?
Not necessarily. Often there is already significant data use before formal governance policy is developed. We use the existing practice to determine which agreements are needed. The aim is not to halt analyses, but to create clear frameworks for existing and future use.
How extensive should data governance policy be?
As light as possible and as robust as necessary. The scope depends on the sensitivity of data, the number of parties involved, the applications and the responsibilities of the organisation.
Can you also develop specific policy for analytics and AI?
Yes. We can develop organisation-wide data governance policy, but also look specifically at the responsible application of data analysis, algorithms, machine learning and generative AI. Read also about analytics & AI.
Can you assess existing policy?
Yes. We can assess existing data policy and governance frameworks on completeness, coherence and practical applicability. We then determine which parts are missing or need sharpening.
Next step
Develop data policy or sharpen existing governance?
We can help with both setting up new data governance policy and assessing and improving existing frameworks. We can focus on the full organisation or on a specific topic.
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