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The Data Quality Improvement Assessment Explained

What do the 4 stages of the maturity curve mean for your organisation?

Greg Taylor 3 minute read Data quality

In my previous blog, I talked about Experian's brand new Data Quality Improvement Assessment, why we developed it and how we hope organisations will benefit from using it. The assessment gauges an organisation's data quality strategy maturity and plots the results on our data quality maturity curve as well as offering useful reading and advice.

The simple and quick to complete Data Quality Improvement Assessment helps organisations understand their next steps so they can take appropriate action around how to move forward with their data initiatives.

Below I have outlined the 4 principal stages of the maturity curve, ranging from no real grasp of the importance of data quality to a fully governed and optimised data quality environment.

Stage 1: Unaware

What does this mean?

If you land here, your organisation has 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.

Stage 2: Reactive

What does this mean?

Your organisation is starting to react to data quality issues as they impact on business performance but you have yet to assign any data specific roles. You 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).

Stage 3: Proactive

What does this mean?

Your organisation has become more proactive with its data quality efforts. You've 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. Your organisation is 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 you have begun to utilise technology for data profiling and discovery to help realise the value of your data assets more clearly and have a more structured process for root cause analysis.

Stage 4: Optimised and Governed

What does this mean?

It's all very positive. Your organisation has developed a fully governed data quality environment and you 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. This would include well communicated and well documented rules, controls and processes for monitoring performance metrics closely. Your organisation takes a consolidated approach to technology investment, only partnering with vendors that can complement and/or integrate into your existing and established information management practices.

How would you score?

If you are interested to see where your organisation falls on our maturity curve, then take the Data Quality Improvement Assessment and start driving your data quality initiatives forward.

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