Accelerating your data quality

Everyone gets data quality

It was pleasing to hear during the roundtable discussions that everyone is starting to get to grips with their data quality and begin to understand the steps they need to take to improve the maturity of their data quality strategy. The participants had also begun to contemplate how a data governance framework can support data quality initiatives but this area was far less developed. Our guest presenter, Nicola, explored how having a clear governance structure (i.e. clearly defined roles, processes and policies) for your data can help organisations to become far less reactive when tackling data quality.

Define terms that mean something to your business

When starting out on a data governance programme there are a few things that need to be defined. One of these is “the terms” that you will use. These terms cover job roles, data definitions and other areas but crucially must make sense to your organisation. If the people in your business can’t define your initiative or elements of it then they will struggle to support it. Nicola mentioned a range of terms and keywords during the roundtable, but that doesn’t mean that you have to have the same in your organisation. Choose job roles and glossary terms that are relevant to you and your business.

The three steps of a data governance framework

Creating a data governance framework needn’t be complex, and that’s why Nicola has clearly defined the three stages that make up the framework: policy, processes and roles and responsibilities.

You need a policy in place so that you can say, “in our organisation we manage our data properly by doing A,B and C.” If you don’t have a clear policy in place, then people forget and will quickly forget that there are rules surrounding data quality.

By giving people clear and informative processes, they understand exactly what they are meant to be doing, and this in turn, helps your data to be more consistent (and avoids people doing nothing!).

Finally, develop the right roles and responsibilities for your organisation ensuring to select senior “data owners” that can help drive buy-in and accountability. In large organisations, a data owner would be supported by a number of data stewards.

Although there isn’t a definite order that these steps should be taken, it is important to have a policy either agreed or drafted before you get started. We’ve seen many cases where people spend time on other parts of the framework, and then don’t receive business buy-in. It’s also useful to trial this on one area of the business that has a data pain, just to double check that it works.

Making data governance business as usual

It’s worth remembering that data governance should not be seen as a project or a one-off. Once the framework has been put together, and put into practice, make sure that this becomes business as usual. This will ensure that you are continuously being proactive in your approach to data quality and this is an invaluable way to be.

With that being said, once the framework is in place, don’t just forget about it. We suggest that you review your framework at least once a year, if not more. People change, the business’ priorities change, and therefore the framework needs to change and adapt.

Keep the business in the loop

Ensuring that data is centrally managed would be the ideal scenario, but in some cases this just isn’t possible. Therefore it’s vital that you keep the rest of the business in the loop about what you are doing, and what you are trying to achieve. Without this, errors usually occur because one team is trying to do “A” with the data, while another is trying to do “B”, often leading to a conflict of interests.