3 April 2017
Advanced analytics. Real time reporting. Big data. Smart data. Data virtualisation. From data centres to centres of data. Just a few of the concepts and phrases we have been hearing so much lately. However, what is the point of these concepts if you have no idea whether or not you can trust the source or the data supporting them? That trust can be arranged. In this article we will examine the concept of Data Governance and are happy to share five things to think about that we have come across in recent times in this field.
Many organisations are increasingly using data as the basis or starting point for their decisions. Marketing runs customer analyses, Finance creates insights into the results and the Risk department thinks it is in control of the processes. All examples of things that are driven by trusting underlying data. Therefore we can see data as the foundations beneath operating processes, reports and analyses in an organisation. Considering that conclusion, it is odd that many organisations still pay barely any attention to the interpretation and organisation of these foundations. Or to the concept of Data Governance.
And what do we mean by Data Governance? This is how we define it: “Data governance refers to all the control measures in place to guarantee the availability, usability, integrity and security of data within an organisation.”
In order to correctly implement Data Governance, we use a Data Governance Framework. In addition to the design and organisation of processes and procedures, the framework also incorporates the actual execution of the designed policy. An important starting point for this is that the designed framework helps to achieve the data strategy within the business strategy.
We often see that Data Governance is confused with Data Management. However, we do see Data Management as one of the (essential) components of Data Governance. To identify this interrelationship, it is a good idea to look at the model from the DAMA (Data Management Association International): the Data Management Body of Knowledge (DMBoK).
This model makes it easy to see what a binding and directing role Data Governance has and what common ground it has with other areas of interest such as the various components of data management. And no matter how important this directing role is, it can’t be carried out correctly without specific interpretation or at least attention to the various sub-areas. In other words, as we often say in response to what the relationship is between Data Governance and Data Management: “Data governance is to data management what accounting rules are to financial administration.”
1. Don’t let regulations be the starting point
We often find that Data Governance is only being designed because the regulating/supervising body has set certain requirements. For example, it suddenly becomes important to be able to identify what certain information in a report (whether or not it is a statutory one) is based on. Introducing Data Governance at that stage is often too late. Beyond the fact that you should always be raising question marks about the sources of reports and their trustworthiness anyway, introducing Data Governance is not a stopgap solution to gain some “quick” insight. Therefore our advice is this: organise Data Governance anyway, the internal organisation is entitled to it too and will benefit from it!
2. Data Governance is not an “IT Field Day”
We often see that Data Governance is initiated by the IT organisation. After all, data is often seen as a technical thing and therefore part of IT. We disagree. As we see it, Data Governance is information management across the highest levels of the organisation and, just like information management, is not, or not only, to do with IT. We recommend arranging Data Governance around a neutral department between IT and the business (including staff departments such as Finance, Risk, HR). Examples could be an Information Management department or Compliance.
3. Start with Governance; interpretation from IT will follow later
People often seek to address the absence of good Data Governance by providing tooling. We don’t believe in that. Start by making sure there are well-described processes and clear tasks, authorities and responsibilities. Choose a fast way to set metadata in reporting repositories, business glossaries and data dictionaries. But do ensure it can be imported later when tooling comes into play. And in terms of tooling, also consider integration with applications you already use. In our experience, the reporting software already in use is a great place to make the link with a business glossary or data dictionary. Either way, this approach means that the value of the Data Governance process is already proven, regardless of the software.
4. Constant commitment is key
Without support from all levels of management, the implementation of Data Governance will run into deep water. Especially when the benefits are not directly visible, it requires huge powers of persuasion to get the people who will have to do the work (i.e. start documenting things or documenting them better) into a cooperative position. That’s why we believe the viability of Data Governance depends on securing the management’s commitment. It has to become a recurring topic for the management, and rolled out in the organisation, before Data Governance can succeed.
5. Think big, start small
The implementation of Data Governance can be a monumental and gripping process. But if you start small – but with the bigger picture in mind – it is possible to set the right speed and learn from the various steps. In other words: you can’t eat an elephant whole, you have to cut it into pieces. As you complete each individual piece, you can show what has been achieved; the business glossary for the marketing department, an overview of reports for Finance; all the little pieces of the puzzle that show it is more than worthwhile.
Data Governance might sound complex, vast, expensive and perhaps even unnecessary. But don’t we always want to be able to say with certainty that we trust as much as possible the data based on which we create reports, derive insights and take decisions? And certainly don’t we want to understand what source the data comes from? So make it small, show that with a little effort you can take the first steps in no time and the results can move the entire organisation forward.
Click here to read more about our pragmatic approach.
Do you want to know more about this subject? Please contact Kees Groenewoud using the details below
17 July 2018
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