Where is your organisation on the data quality maturity curve?

It is widely accepted that big data has the potential to transform performance, but, not surprisingly, businesses have adopted it into their strategy at varying speeds, as data, big or small, is not consistently seen as a strategic asset across all industries. On a positive note, from recent investigation, it seems that we are finally seeing more and more organisations looking into the quality of their data and thinking strategically about how it is managed.

According to the latest Experian Data Quality Global Research, in 2015, 54% of companies plan to prioritise and improve a data quality solution they have in place, while 64% will focus on a new solution. As these figures show, accurate data is becoming ever more important, and it being moved to the heart of business operations.

Due to the heightened significance being placed on data, it is important that organisations are able to quantify the maturity of their data quality strategy, in terms of how it is managed and used, and for that, businesses should look to use a maturity curve.

There are plenty of data quality strategy maturity curves in the market, just type it into a search engine and a number will come up, but there’s not a huge amount of context around how they work and what moving along the curve means to an organisation.

> See also: Is big data dead? The rise of smart data

To make this easier to quantify, Experian has broken this curve down into four stages on how advanced companies are with their data. The four stages are ‘Unaware’, ‘Reactive’, ‘Proactive’ and ‘Optimised’& Governed’, covering the full life cycle of a data quality strategy.

Here they are explained in more detail, ranging from no real grasp of the importance of data quality to a fully governed and optimised data quality environment:


This is the first point on the curve and relates to organisations that have a limited understanding of the concept of data quality and the impact it can have on the business. There is a sense of apathy towards the issue of data quality across the organisation, particularly at senior management levels.

Business users see data as 'good enough' and regularly introduce workarounds where information is often sub-standard and not fit for purpose. The organisation is unaware of the costs associated to workarounds, and so even more unclear of the potential value added to the business if these workarounds did not exist in the first place.


The next stage on the curve applies to organisations that are starting to react to data quality issues as they impact on business performance but have yet to assign any data specific roles.

They typically lack a coherent strategy when dealing with data at a corporate-wide level and use tactical point tools within departmental silos for issue resolution. It’s likely that the motives behind any investment in data quality will be in response to a compelling event that has caused the business significant short-term pain (e.g. breach in compliance).


This stage means organisations have become more proactive with in their data quality efforts. They have started to define roles and create charters that help to take a more cohesive and unified approach to data management. A better understanding of data processes has begun to break down departmental silos, allowing for collaboration and prioritisation between IT and business users.

Organisations here are also now likely to be considering the improvement of a broader range of data domains other than customer/party data (e.g. product/financial/location etc.) and have begun to utilise technology for data profiling and discovery to help realise the value of data assets more clearly and have a more structured process for root cause analysis.

Optimised and governed

The final stage means it’s all very positive, where optimised business practices around data mean data quality becomes 'business as usual'. Organisations here have developed a fully governed data quality environment and can clearly communicate the link between data quality and financial performance to the board. Data has a single owner or entity that is responsible for the maintenance of the corporate-wide information management strategy.

> See also: A guide to data mining with Hadoop

This would include well communicated and well documented rules, controls and processes for monitoring performance metrics closely. Here, organisations take a consolidated approach to technology investment, only partnering with vendors that can complement and/or integrate into your existing and established information management practices.

By looking over these stages, organisation can assess the maturity of their approach to data quality and see where they sit on the scale compared to others. In this burgeoning digitalised world, data will only continue to increase in terms of its value to your business. By acquiring a fuller understanding of where your organisation sits on the maturity scale you should be able to significantly improve the value of your data assets, creating actionable steps which will enhance the overall business strategy.

Sourced from Janani Dumbleton, principal consultant, Experian Data Quality UK

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Ben Rossi

Ben was Vitesse Media's editorial director, leading content creation and editorial strategy across all Vitesse products, including its market-leading B2B and consumer magazines, websites, research and...

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