So You Want To Do Data Governance?

For the word “governance” we can substitute the word quality because that is the fundamental aim of data governance. “Why should I look at data quality?” Well I guess that it’s obvious what happens if your business has bad quality data … goods being despatched to wrong addresses, customers receiving goods that don’t match the publicised descriptions and so on.

However, there are other more subtle effects of poor data quality – missing the opportunity to upsell to a customer because you can’t accurately identify the product categories that they purchase, not being able to negotiate purchasing discounts because the supplier is duplicated so many times that you can’t say what your total spend is, losing web sales because your inaccurate sizing data makes you look bad on comparison sites.

I guess we could also ask – “Why should I look at formalising my governance?” – because it’s likely that there are already some people in the organisation who are checking data quality as part of their regular job. For example the accountants are probably ensuring that postings are made to the correct ledger codes, your accounts payable department is ensuring that invoices are sent and matching payments are received.

Much of your operational data is already part of an active management process but to a large extent their interest is in quantities and values. The areas that get less quality checking are the reference data (or master data) that drive many of your business processes. Data Governance aims to put in place formal management responsibilities for the quality of this data.

One of the changes in attitude that is driven by data governance is to move away from a reactive approach to quality into a more proactive approach. Often poor data quality is only found when a business process fails – when a delivery can’t be made or when your IT system stops working – and there are few instances where that is the best way to find problems.

It is also common when disasters occur through poor data quality that nobody can be found to take responsibility! Data Governance ensures that somebody is clearly responsible – not just for fixing the disasters but also for reducing the likelihood of one occurring.

How do I get started?

Many proponents of Data Governance have fixed models which have been proven to work in previous engagements. The issue is that many of these fixed solutions disregard your organisational capabilities, the amount of resource availability or your budget. The right organisation is one that fits the needs of governance but also fits the ability of your organisation to execute and sustain it.

Here is a structured approach to building a tailored data governance organisation:

  • Build a clear vision – ensure you have a clear vision and scope for your data governance initiative so that you can ensure that your organisation is able to fulfil it
  • Define Standards – each Standard should have a business rationale as to why it exists, defined benefits that can be achieved from having the Standard, definitions of what level of quality should be achieved to realise the benefit (not always 100%) and metrics that will show that the benefits are being realised
  • Design a Data Governance organisation – that is suitable for managing the Standards we have defined. This includes the roles and responsibilities for those governing, the internal governance processes that will be used to manage activities (such as the change management for Standards) and changes to any external process that affect the organisation’s ability to govern (such as the IT project management process)
  • Engage your “Data Owner” – to own your Standards and to build the “Data Quality Roadmap”
  • Build a Data Quality Roadmap – which documents your current quality level, measures this against the requirement defined in our Standard and proposes actions to bridge the gap and/or maintain good quality
  • Populate the remaining data governance roles – engage resources for the data governance roles which are needed to operate the on-going compliance measurements and to manage the activities identified in the Data Quality Roadmap.

How can I make sure that my Data Governance organisation succeeds?

One of the keys to a successful data governance organisation is “authority”. When building your data governance organisation a key question is – “When someone in the organisation refuses to comply with our standards what can we do about it?”

Where there is no authority you usually find the growth of “local” standards and the proliferation of complex interfaces to manage the transition between areas of the business with differing standards. As the number of standards increases you eventually come to the point where there is no standard at all.

The types of businesses that often have issues such as this are those that have grown through acquisition but have kept management of these subsidiaries at arm’s length. Conversely the most successful data governance initiatives are in the pharmaceutical industries where compliance to standards is enforced by external agencies.

Money is another key issue for data governance. The budget needed to start such an initiative is often managed successfully by the project team but you also need to consider the on-going funding required by such an initiative. Data Governance requires continued operational funding for the roles defined in the organisation. It also requires access to funds for data quality improvement projects which may be identified over a period of time by the regular compliance monitoring.

And finally …

Data Governance is not complex in principle but its application can be become both complicated and very political. It is something that benefits from having expert guidance to design but it also requires local knowledge of the organisation and its peculiarities to build something that works in your situation and delivers real benefits.

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Stuart Murdoch is a Managing Consultant at Stibo Systems and has worked in IT for 39 years. For the last 13 years he has specialised in MDM Strategies, Data Governance, Organisational Change Management and Data Migration. He is now responsible for Stibo’s internal methodologies.