Janani Dumbleton, Principal Consultant, Data Quality Propositions, Thought Leadership at Experian Data Quality UK

More and more organisations are starting to understand how critical quality data is in achieving their strategic objectives, not to mention ensuring customer satisfaction.    Accurate, complete data that is consistent with legal requirements and business rules can have a profound impact on a business’ long-term success.

However, while there is a growing consensus about the importance of quality data, as yet the understanding of how to achieve it still lags behind – with organisations often focusing on short term fixes or projects to bring their data ‘up to scratch’, but then failing to follow this up with the appropriate governance to maintain that new found quality. This makes it only a matter of time before another data quality drive is needed.

Data governance, the process of planning, monitoring and enforcing the management of data assets, is a key component of long-term data quality (and therefore, long-term organisational success). It helps to ensure that data is captured accurately and that this accuracy is maintained, no matter how long it is stored for, to avoid the expense of repeated data quality initiatives.

So why are so many organisations still shying away from a proper data governance structure when it makes such obvious business sense? It all comes down to responsibility – without a single responsible person (or department) driving for a proper governance programme, it will invariably flounder. While some organisations are starting to create Data Quality roles and departments, there are few with a similar set up for Data Governance, and without this structure to report and feed back into, those responsible for data quality will frequently be cast in the role of fire fighter.

Janani Dumbleton

Just what is a data governance framework?
While there is no one size fits all approach (there rarely is in business), there are certain elements of a data governance framework that can be applied across the board.

1) Policy: A robust policy stating that your company requires proper data governance is integral in achieving the wider support needed for any initiative on a long-term basis (rather than it occasionally being ‘flavour of the month’).

2) Processes: Just as with any successful data quality programme, it is essential to have clearly defined and documented processes (and associated deliverables) in place – setting out how things such as data quality reporting and data quality issue management should be handled.

3) Responsibility: As mentioned earlier, responsibility is key. Defining who is responsible for data (governance and quality) is at the corner stone of any data improvement.

In short, data governance is about analysing, improving, and controlling data and establishing processes to investigate and act on data quality issues.

How do you implement a successful data governance programme?

1) Analyse: The first step is to take stock and look at what you’ve got. This can be broken down into two main elements:

• Data profiling: This process of gathering and examining information about existing data is often viewed as a pure data quality activity. But when shared with those responsible for the data this can give advanced business expertise and insight to the results – bringing wider benefits to the organisation as a whole.
• Reviewing and approving data definitions: To truly understand and manage your data it must be defined (for example, in a data dictionary or glossary), and then held where it is readily accessible by the users.

2) Improve: Once you know what you are working with you can set about improving it. This means:

• Collaborating: We all know that an organisation and IT should work together to deliver for the whole business. However, a lack of collaboration where data governance is concerned will prevent you from getting off the starting blocks all together. Take time to ensure you all understand the wider objectives, are in agreement as to how to achieve them, and, crucially – who is responsible for what.
• Reviewing and approving business rules for data cleansing. After undertaking your analysis you will need to garner input from your stakeholders to agree the rules by which the data will be cleansed (of course, with clear responsibilities assigned, deciding who to involve should be fairly simple). It’s also useful to include these data cleansing rules in your data glossary for ease of future reference.
• Master Data Management: Making sure that you are highly focused on how your Master Data Management is deployed is essential. That means make sure the processes, governance, policies, standards used are well defined and communicated.

3) Take control: This is where you have the opportunity to really make a tangible difference in how governance works within your organisation. The key elements being:

• Defining data quality rules: This is where a data governance framework comes in to its own and starts to deliver results. This pro-active process will enable you to report on the status of your data quality at any point in time – not only serving as a monitoring system, but also providing an early warning of any potential issues (before they get too big – and expensive!)
• Data quality reporting: Only after data quality rules are defined will you be able to instigate a process for reporting on how the data measures up against those rules.
• Monitoring and acting on data quality reports: Here is where the chain comes back full circle to the initial establishment of a policy on data governance. With this, and the associated processes, in place you can take steps to ensure that those that need to take the necessary action, do so.

Taking these steps, and embedding them within your organisation will help to ensure that data quality and governance become entwined in a symbiotic relationship. This will help to deliver long-term benefits for the organisation as a whole, avoiding the repeated expense of starting data quality improvement initiatives from scratch, and helping you to capitalise on the benefits that data quality can bring in a sustainable manner.