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Navigating the Data Governance Disconnect

Connecting Policies with Practice

I often work with organisations that are kicking off a new data governance project or journey. These initiatives usually start for a variety of reasons – a change in leadership, a shift in strategy or the emergence of regulatory pressures. A challenge I commonly encounter at the beginning of these engagements is the perception that data governance is just about creating policies and standards (e.g. documentation).

In my initial meetings, the conversations often centre around past data governance efforts, which tended to be heavy on documentation but light on impact. This history typically sets a tone of scepticism among stakeholders. They've witnessed previous data governance projects that generated a lot of excitement but delivered very little tangible value. Often these were initiated under regulatory or compliance pressures and lost momentum once the immediate issue was resolved.

From my experience, this issue, which I call the 'data governance disconnect', is a real problem. Data governance efforts are often defined in isolation, focusing on creating policies, standards, and writing definitions in a data dictionary, yet lacking real integration with the broader data teams and the ecosystem of the organisation. This disconnect from engineers, analysts and the business is where many governance initiatives fail or at least fall short of delivering their long-term transformative impact.

During a recent 'lunch and learn' session with one of my clients, I drew a parallel to illustrate this point. Imagine building a house where you hire an architect and a surveyor to create detailed plans and specifications. If these plans aren’t effectively communicated to the builder, and they aren’t taught how to use them or understand their importance, the outcome is likely a poorly constructed house. This renders the time and money spent on planning wasteful.

Similarly, in data governance, the creation of documents, roles, and processes becomes futile if they are not integrated with the teams responsible for actually moving and managing data around the organisation. In essence, successful data management requires the merging, or rather the inherent connection, of data governance and data delivery. They are one and the same discipline. It's not enough to set standards and define roles. All of this must be intertwined with the technology, engineering and business teams. To avoid the pitfalls of the data governance disconnect, it’s crucial to view data governance as part of the same whole unified data capability your organisation has.

Of course, it would be negligent of me to highlight this "data governance disconnect" problem without offering some suggestions as to how you can avoid this and sharing the techniques that I have found to work.

  • Emphasise the 'why' and 'how' over the 'what': The 'what' is often outlined in policies and standards. But these come to life only when teams understand why they matter and how to implement them. Instead of just focusing on what the organisation should do, you must describe why it's important and how it can be achieved.

  • Focus on the consumption rather than creation of artefacts: When building data governance artefacts like data catalogues, business glossaries, and data lineage, prioritise how they will be used and what use cases they'll support. Investing in these solutions is only worthwhile if they are being used and people are consuming the artefacts. The value comes when the information is consumed not when it is created. So often, I see organisations that have invested vast sums of money in building out data catalogues, but the only people who are logging in and using these tools are the people who have been tasked with putting the information into these tools and writing the definitions.

  • Integrate governance with delivery: Data governance processes and artefact development should happen as part of data delivery. For instance, when building a new dashboard, that's the opportune moment to define the metrics, to record them in a catalogue, and to address data quality issues. This ensures the governance-related activities are relevant. They occur alongside the delivery process, and the business sees value. It's tangible in terms of a high-quality dashboard that can be managed and maintained. If you try to do governance separately or retrospectively then it's very difficult to demonstrate that value.

  • Position data governance as an enabler: The role of data governance is to enable your teams to better manage their data. Developing policies and standards is just the beginning of this. The real work lies in training and empowering your business to understand, implement, and adhere to these guidelines. Consider developing policies and standards as the foundation. It constitutes maybe 20% of the work. The remaining 80% should focus on enabling your business to effectively implement and align with these policies and standards, which is all about communication and training.

Thank you for reading this article. If you found value in it, or if we share similar interests, then I'd be delighted to connect on LinkedIn. If you would like to discuss how Decaf Data can support you with training and coaching then please reach out via our contact page.

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