What is Data Governance
In our last blog we discussed how Data Governance is not the feared ugly duckling any more. More and more organisations are starting to realise the importance of Data Governance to derive value from their data and safeguard their organisations from any legislative implications. But what exactly is Data Governance? Why do we need to care about it? And what are some of the challenges that we have seen? In this blog we are going to answer those questions.
So to start, what exactly is Data Governance?
Some might say that it is just another fancy word for Information Technology but that is far from the truth. Data Governance in simple terms means the ‘governance’ of data. It is the assignment of roles, responsibilities, accountability and ownership to data assets. DAMA International defines Data Governance as the exercise of authority and control (planning, monitoring, and enforcement) over the management of data assets.
Some might argue that most organisations already have some form of governance structure in place so what is Data Governance doing differently? The sole purpose of Data Governance is to oversee data assets within the organisation. Governance for operations and employee’s health and safety (just to name a few) is not a part of a Data Governance framework. Having said that, Data Governance is essential in providing good quality, reliable, and valuable data securely for all data driven decision making throughout the enterprise. It ‘enables’ all Data Management and decision making initiatives in an organisation.
So what does an enterprise Data Governance framework set out to achieve?
A Data Governance framework will aim to define clear roles and responsibilities like Data Owners, Data Stewards, Data Custodians and Data Users. It will also define clear decision making pathways (through establishing groups, councils, working groups or similar structure) and how decision making flows between them. There are various Data Governance approaches that can be considered like Non-intrusive Data Governance, Federated Framework or the more traditional Centralised Data Governance. We are going to discuss these different approaches in detail in our upcoming blog.
We have talked about what Data Governance is and what it looks like in an organisation. Now the next question is ‘Why do we need Data Governance’?
The most common answer is to optimise data assets, get the best value from that data and enable good decision making and strategies. It is a great answer but there is a lot more to that. Data Governance is not just a ‘Good to Have’, it is a ‘Must Have’. With an increase in organisations pursuing to become data centric it is vital that Data Governance is prioritised. This is because good data governance builds the foundation for good data management throughout the lifecycle of data and lays the foundation for overseeing everything from Creation to Disposal of data assets.
Data Governance is also getting increasingly important with new regulations and the continuous modernisation of technology. Most industries now have data standards and expectations that need to be met as a legal requirement. Whether it is the duration of data storage or managing personal information, it is essential that proper policies and guidelines are in place for any organisation’s data and are executed effectively through different Data Governance roles. There is also an additional layer of AI Governance with the increasing use of AI and machine learning solutions embraced by the organisations now.
Now let’s talk about some of the challenges that we have observed while implementing Data Governance frameworks. Let’s break it down into the discovery and implementation phase.
During the discovery phase, from our experience assessing the current state of Data Governance in various organisations and the gaps to an ideal future state, we have observed some common trends and themes of Data Governance issues.
Data in silos:
One of the most common things is that data assets are managed in silos. This is especially true when the Data Governance maturity is low, each domain or team is making decisions or managing their data assets individually. There is not much interaction or coordination with the rest of the organisation which leads to duplication of effort and hampers the quality and value of data. Not to mention, the lack of an enterprise data view that would hinder good decision making and strategies that we mentioned earlier (at least, not holistically or enterprise wide).
Lack of sustainability:
We have also seen that most organisations have had some attempt at Data Governance in the past but do not follow it through and go back to the old ways pretty quickly. This is a result of treating Data Governance implementations as a project rather than an ongoing function in the organisation. While implementing Data Governance frameworks, it is important to have resources and people committed to the implementation and to take it further as “Business as Usual” once the implementation is successful.
Lack of literacy and awareness:
The other thing that we have observed is a low level of Data Governance literacy. When trying to implement Data Governance frameworks the most common question we get is Why are we doing this? We already have ICT. It has been observed that there is usually little awareness of the importance of Data Governance and how it is different from Data Management.
We discussed some of the challenges that we have seen but there are some simple things to mitigate these issues.
First of all it is important to choose the correct approach or style of the Data Governance framework and its implementations. Customisation is really important as every organisation is unique in their own culture, aspirations and vision (to name a few key ones). Not to mention, they can change over time.
- If an organisation is moving towards a Data Mesh approach and working to lift their Data Governance maturity then it is a good idea to use something like Federated Data Governance framework which is easily scalable and provides more autonomy to domains.
- For organisations where they have a lot of processes or working groups in place already and just require some structures, a non-intrusive Data Governance approach is the most ideal.
- Most traditional organisations or even government agencies tend to integrate their Data Governance with their Enterprise Governance. In that situation a more traditional, top-down Centralised framework is optimal.
Note that the aforementioned frameworks are not mutually exclusive and this list is not exhaustive. There are other framework options or a mix of these frameworks that might work better for some organisations. Given these options, it would be a disservice to force a framework (that may have worked well in another organisation) into your own. In our experience, not customising your framework will also create an unnecessary level of change friction that will make the framework ineffective and, ultimately, make you lose momentum.
Secure Buy-in and direction:
The next thing is to make sure that the implemented Data Governance framework is sustainable. It is important to drive momentum, which could mean obtaining and maintaining buy-ins from senior leaders, committing a dedicated Data Governance resource or team to the implementation journey and taking it all the way into ‘Business As Usual’ once the implementation is successful. A dedicated Data Governance resource or team should be involved across all relevant data groups (e.g. Data Governance Forum) within the enterprise, thus making it easy to act as a conduit between the various decision making layers.
Embed Change Management:
The last important thing to consider is that Data Governance implementations must be complemented with some form of Change Management activities. Simple things like Data Governance training sessions or awareness material can help overcome the confusion and lack of engagement by the stakeholders. These Change Management activities should be endorsed by the sponsor to get maximum impact.
It is safe to say that in the modern world, Data Governance is one of the most important aspects of Data within an organisation. With the increasing desire of organisations to become data centric and get the most out of their data assets Data Governance has now taken centre stage. We are seeing a huge surge in Data Governance projects and the mindset around Data Governance just being an add-on is changing rapidly.
In our next write up we are going to discuss the differences in Data Governance, AI Governance, Ethics and Privacy and also talk about different Data Governance framework approaches in detail. Watch this space.