How to achieve data quality

However, while there is a growing consensus about the importance of quality data, the understanding of how to achieve it still lags behind – with organisations often focusing on short term fixes to bring their data ‘up to scratch’, but then failing to follow this up with 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. So why are so many organisations still shying away from a proper data governance structure when it makes such obvious business sense? 

It 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 doing the same for data governance. Without this structure to report and feed back into, those responsible for data quality will frequently be cast in the role of fire fighter.

What is a data governance framework? u2028While there is no one size fits all approach, there are certain elements of a data governance framework that can be applied across the board.

1) 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.

2) It is essential to have clearly defined and documented processes in place – setting out how things such as data quality reporting and data quality issue management should be handled.

3) Responsibility is key. Defining who is responsible for data (governance and quality) is at the corner stone of any data improvement.

How do you implement a successful data governance programme?

1. Analyse

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 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

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. It’s also useful to include these data cleansing rules in your data glossary for ease of future reference. 

Master data management: Make sure the processes, governance, policies, and standards used are well defined and communicated.

3. Take control

Defining data quality rules: 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, and help you to capitalise on the benefits that data quality can bring in a sustainable manner.

Janani Dumbleton is principal consultant of data quality propositions at Experian UK.

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