5 Tips for Agile Data Governance
Agile Data Governance contains the same building blocks as traditional Data Governance. The key difference is approach. The more adaptive approach of Agile makes a big difference, both with effectiveness, and with how quickly we experience the benefits.
Agile used to be applied predominantly to software development. It’s now recognised that Agile methods have significant added value in a far wider set of business applications. Below are 5 practical tips for applying Agile methods to Data Governance.
1. Focus on frequent deliveries, each time delivering added value
Just as with software projects, short incremental deliveries can be applied to Data Governance efforts and offer various benefits, one of which is faster return on investment. Data Governance initiatives frequently used to experience business case hurdles when starting out. There are various reasons for this, one of which was that it was often an expensive ask. A far-reaching programme was scoped, involving organisational restructuring, new roles, extensive process remodelling and new policies, and results were typically only expected after the project had been running for a number of months.
Smaller, leaner business cases, ideally where the project quickly becomes self-funding, are generally far easier to get approval for than extensive programmes. If we are clear what business value we expect from each incremental data governance iteration, and if each iteration then delivers the expected return, we have a far more convincing business case for continuing with subsequent iterations. I’ve sometimes presented this as a “rolling business case” concept, where each new iteration is financed by the improvement gained in the previous iteration. This approach is particularly useful in organisations where budget availability or lack of understanding about the added value of Data Governance is blocking progress. Concrete returns are more difficult to dispute than optimistic projections.
As the value of Data Governance becomes more widely understood, it is more common for management to start with positive expectations. Providing faster returns which regularly add concrete value in the organisation helps keep both senior management and other stakeholders enthusiastic and engaged.
2. Ensure each iteration delivers usable products, so constant evaluation is possible
A key principle of Agile focuses on early and continuous delivery of value. Closely related to this is the concept of failing fast in Lean. Eric Ries popularised the term “Minimum Viable Product” (MVP). In his book “The Lean Startup” he explains how MVP can be used to implement efficient validated learning using the Build-Measure-Learn feedback loop to quickly discover when we’re on the right track to deliver value and when we need to change direction.
If we start rolling out Minimum Viable Products early in our Data Governance efforts, we will quickly be able to gather stakeholder feedback about what works and what doesn’t. A huge benefit of this approach is that we can use the feedback we receive to tweak or, where necessary, completely overhaul the Data Governance building blocks we’ve implemented. If particular roles, responsibilities, data governance technology solutions or process changes aren’t generating the improvements we expected, we can evaluate why and change course.
A second benefit of this approach is that our users are more engaged because they experience benefits early on. Rather than having to wait for concrete returns as we set up a wide-ranging programme, our users begin to experience the measurable benefits of cleaned up data, complete data sets, more accurate definitions and sharply defined context within a couple of iterations. This is hugely motivating, especially if users have already lived through multiple failed Data Governance programmes.
3. Support, trust and motivate the people involved
Data Governance efforts usually contain a large people component, whether it’s gently nudging users to perform new activities, encouraging them to be more careful with their data quality or getting them thinking about and agreeing on definitions.
One of the underpinning principles of Agile is collaborative working as a way to empower users, stating “projects are built around motivated individuals, who should be trusted”. As with all change programs, if people understand why new work methods or extra effort is being asked of them, they are more likely to go the extra mile. Similarly, if they believe certain returns are possible, they are more likely to display the personal motivation required to make Agile working, in this case Agile Data Governance, a success.
Stakeholders should be given clear input about what is required of them and why. They should be given both the mandate and the necessary time to carry out any additional tasks required of them. They should be trusted to perform the extra activities required and the benefits of doing so should be clearly communicated. And they should be roundly praised for all improvements this leads to. Agile Data Governance often asks users to put in extra effort. They are more likely to stay enthusiastic and committed if they understand the benefits their efforts bring and if they are recognised for their extra work.
4. Focus on simplicity
One of the core aspects of Agile working is minimizing waste. Agile principle number 10 states “Simplicity – the art of maximizing the work not done – is essential”.
Data Governance programmes used to be herculean undertakings where considerable time was spent developing new organisational structures, committees, policies and processes, often before any benefits were felt by the organisation. Current wisdom focuses more on scaling back activities to the minimum required to generate returns.
Rather than investing effort changing organisational structures as a prerequisite to nominating data stewards, data custodians and data governance managers, we are now more likely to see virtual teams created. Often these are staffed with existing roles who have been given additional Data Governance responsibilities. Only if users enjoy their new responsibilities and the virtual team set-up delivers the anticipated extra value, is it worthwhile to formalise the extra responsibilities and implement any required organisational changes.
Similarly, instead of drafting lots of new processes and policy changes which ultimately may not be necessary, users can try out various process and policy suggestions. Once the best options are clear, these can be implemented. Subsequent iterations can then be used to improve, extend and formalize the implementation where necessary. At no point in Agile Data Governance is theory put before practice, or bureaucracy before concrete returns.
5. Regularly reflect on how to become more effective
Last but not least, most Agile methodologies contain a significant amount of reflection. In Scrum, there is a retrospective at the end of every sprint, where we evaluate what has worked and what can be improved upon. Lean has the learning loop, among other tools. Regularly reflecting on and evaluating how things are going, which aspects work well and were we can improve can greatly improve our effectiveness.
If we are going to get the most out of our evaluations, it is very important that we are honest with ourselves and this is easier in a safe environment. If people are afraid to voice concerns because of negative feedback or because it may have a negative impact on the overall project, the reflections will be less effective.
Equally important is that the output is evaluated and where possible, applied so that improvements are made. If reviews take place and the results are then put in a cupboard, people quickly loose interest. The regularity of the reviews is also important. If our reviews are too infrequent, important learning moments will be missed.
Finally, while lessons learned are important improvement tools, we also need to focus on the positives and celebrate our successes. By promoting our good news stories, we not only help keep key stakeholders motivated, we also inspire the rest of the organisation to get on board with Agile Data Governance.
If you would like more information about how we approach Agile Data Governance in Free Frogs, we would be delighted to hear from you. Please mail us at firstname.lastname@example.org.