Organization's Data Quality Maturity Level: Where Does It Stand?
In the realm of data management, understanding the importance of data quality is paramount. Experian has broken down the data quality strategy maturity curve into four distinct stages: 'Unaware', 'Reactive', 'Proactive', and 'Optimized & Governed'.
The 'Unaware' stage refers to organizations with a limited understanding of data quality and its impact on the business. Business users in this stage often see data as 'good enough', and regularly introduce workarounds, often with sub-standard and unfit-for-purpose information. The costs associated with these workarounds are often not recognized, making the potential value added to the business unclear.
Organizations in the 'Reactive' stage react to data quality issues rather than proactively addressing them. It is in this stage that the importance of data quality begins to be recognized, but efforts to improve it are still ad-hoc and reactive.
As organizations progress to the 'Proactive' stage, they begin to break down departmental silos, allowing for collaboration and prioritization between IT and business users. Organizations in this stage have started to define roles and create charters for a more cohesive and unified approach to data management. They have also begun to utilize technology for data profiling and discovery, helping them realize the value of their data assets more clearly. Organizations in the 'Proactive' stage are likely to consider the improvement of a broader range of data domains other than customer/party data (e.g. product/financial/location etc.).
In the 'Proactive' stage, organizations also have a more structured process for root cause analysis. This stage marks a significant improvement in the value of data assets and creates actionable steps to enhance the overall business strategy.
The pinnacle of the data quality strategy maturity curve is the 'Optimized and Governed' stage. Organizations in this stage 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. In this stage, organizations take a consolidated approach to technology investment, only partnering with vendors that can complement and/or integrate into their existing information management practices.
In 2015, 54% of companies planned to prioritize and improve their existing data quality solution, with 64% focusing on implementing a new data quality solution. These statistics highlight the growing recognition of the importance of data quality and the strides being made towards maturity on the data quality strategy maturity curve.
Accurate data is becoming increasingly important and is being moved to the heart of business operations. By assessing their maturity on the data quality strategy maturity curve, organizations can significantly improve the value of their data assets and create actionable steps to enhance their overall business strategy.
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