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Tips to make a good start with data management in your organization


Bas van Gils

31 augustus 2020

This is the third and last posting in a series on data management. In the first post, I discussed Why data management is easier than it seems. Three main reasons are: (1) you probably have several components in place already, (2) you can start small and keep improving continuously, and (3) there are many good practices available, for example in the DMBOK or in my book Data management: a gentle introduction. In the second posting, I discussed Why data management is harder than it seems. Key reasons that were briefly discussed are: (a) the ‘data = IT’ myth, (b) the ‘we have systems for that’ myth, (c) the silo-culture, and (d) the fact that data touches everything. In this post, I will pick up the ball and discuss how to make a good start with data management in your organization.


The following diagram summarizes how we tackle the challenge of building/ improving the data management capability at an organization:


The diagram shows that there is a “slow cycle” and a “fast cycle”. Let’s start with the slow cycle, which represents the top-down approach where a vision for data management is translated to an actionable roadmap. We use our REALIZE approach for this, which iterates between the following disciplines: 


  • Strategy elaboration & stakeholder mobilization: try to understand what the overall strategy of the organization is, what the role of data is in it, which data matters the most, and what that means for data management. Also try to mobilize stakeholders to join in on the effort.
  • Operating model & business blueprint: figure out and visualize how the pieces of the data management puzzle (grip on data / value creation with data) fit together for your organization. This is about the big-ticket decisions: what needs to be centralized, what can be decentralized? Where is the locus of control? Where do we need to be strict with governance, and where do we allow more freedom?
  • Architecture & design: flesh out the big picture view (business blueprint) in more detail, and analyze the detailed impact on data management capabilities such as governance, metadata, quality, etc.
  • Value- and portfolio management: translate the identified gaps to an actionable change portfolio and roadmap. The results from the “fast cycles” (i.e. the experiments) are also a major input for the data management roadmap.
  • Execute the change: realize sustainable data management solutions, with sufficient grip on data and a focus on creating value from data assets


Typically, this is not a linear process, but requires several iterations. The whole initiative must be managed (governance) and professionals doing the work should be supported (training, resources, time). 

Note that there is a strong link with the fast cycles of experiments – partially visualized with the grey arrows. In my earlier posts, I already (briefly) discussed the complexity of data management as a result of data touching everything: people, processes, systems, products and services. No two organizations/ situations are the same, and therefore a pure “big decision up front” approach is doomed to fail. In my experience, experiments and pilots help to find out what works well for your specific organization. These pilots should be short, to the point, and with measurable success criteria. Examples of pilots that you could start with are:


  • Improve data integration between two departments/ systems by harmonizing data definitions and data quality requirements. This pilot is successful if the number of errors in data is reduced significantly and less time/ effort needs to be invested in correcting the data.
  • Build a new dashboard or report with a cross-functional team in an agile manner. This pilot is successful if the end product is realized faster and with higher quality than what is normal for your organization.
  • Experiment with advanced data platforms (e.g. virtualization, self-service BI) to do new types of analysis. For example, try to make a data-driven decision on a good mix between ‘work from home’ and ‘work in the office’ in the post-Covid era. The pilot is successful if we get a good answer and we make the (subjective) assessment that the new platform realizes capabilities that the organization needs to move forward.


The list is sheer endless, and very much depends on your local situation. A brainstorm with stakeholders can go a long way. Of course, we are happy to help/ facilitate such a discussion. 

Please drop me a note if you are interested/ would like to know more. Our data management offering is extensive and ranges from training and coaching, to workshops and consultancy services. We look forward to hearing from you and working with you.