What is the current level of data quality maturity within your organization?
In today's digital age, managing data quality is crucial for businesses to thrive. One framework that can help organizations quantify their data quality strategy maturity is the Experian Data Quality Global Research maturity curve. This model offers a step-by-step guide for organizations to progress from basic, reactive data management to sophisticated, proactive, and integrated practices.
The maturity curve consists of four stages:
- Unaware: At this stage, organizations have a limited understanding of data quality and its impact on their business.
- Reactive: In this stage, organizations react to data quality issues as they impact business performance but have yet to assign any data-specific roles.
- Proactive: Organizations at this stage become more proactive with their data quality efforts. They define roles, create charters, and take a more cohesive and unified approach to data management. They also utilize technology for data profiling and discovery to help realize the value of data assets more clearly and have a more structured process for root cause analysis.
- Optimized & Governed: In the final stage, data quality becomes 'business as usual' for organizations. They 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 responsible for the maintenance of the corporate-wide information management strategy.
Progressing along the curve means moving from reactive fixes to a proactive and predictive approach that ensures data is trustworthy, accessible, and fully leveraged to support AI and analytics initiatives. This evolution leads to improved business decisions, reduced operational risk, increased efficiency, and a more data-driven culture.
For instance, according to Experian research, 40% of business-critical data often remains "trapped in silos," limiting its value. Moving up the maturity curve implies breaking down these silos and enhancing data integration to unlock that value. Organizations at higher maturity levels also use AI-driven tools to automate quality assurance and create actionable insights across all data interactions.
In 2015, 54% of companies planned to prioritize and improve their existing data quality solution, while 64% focused on a new solution. As we move forward, understanding and advancing along the data quality strategy maturity curve can help organizations improve the value of their data assets and create actionable steps to enhance their overall business strategy.
For more insights on data management and its impact on business performance, be sure to read related articles such as "Is big data dead? The rise of smart data" and "A guide to data mining with Hadoop".
- Technology plays a significant role in data-and-cloud-computing strategies as organizations at the proactive stage of the maturity curve utilize it for data profiling and discovery, automating quality assurance, and creating actionable insights.
- In today's digital age, businesses can leverage data-and-cloud-computing technology to progress along the data quality strategy maturity curve, reducing operational risk, improving business decisions, and fostering a more data-driven culture.
