What is the current level of data quality maturity within your organization?
The world of data is evolving, and so is the importance of managing it effectively. One of the key frameworks for understanding this evolution is the Experian Data Quality Global Research maturity curve for data quality strategy. This model outlines the progressive stages an organization moves through to improve its data quality management, from the least mature ("Unaware") to the most mature ("Optimised and Governed").
The 'Unaware' Stage
In the 'Unaware' stage, organizations have little or no recognition of data quality issues or the strategic importance of data governance. Data quality problems are often identified only when operational disruptions occur. At this stage, businesses may view data as 'good enough' and introduce workarounds, often with sub-standard information that is not fit for purpose.
Building Awareness and Reactive Response
As organizations begin to recognize the existence and impact of data quality issues, they start reacting to problems as they arise, often in an ad hoc or manual way. This is the 'Reactive' stage, where businesses respond to data quality issues rather than proactively addressing them.
Defining Processes and Roles
The next step is to establish formal data quality processes, roles, and responsibilities. This includes setting data standards, policies, and initiating basic data profiling and cleansing efforts. Organizations at this stage start to break down departmental silos, allowing for collaboration and prioritization between IT and business users.
Managed Stage
Data quality efforts become systematic and measured, with automated tools to monitor and manage data quality. Data governance structures are formed to oversee standards compliance and data stewardship. Organizations in this 'Managed' stage have a better understanding of data processes and are starting to define roles and create charters for a more cohesive approach to data management.
Optimised and Governed
At the highest maturity level, data quality is integrated into the organization’s strategy and operations. Continuous improvement practices are established, supported by advanced analytics, automation, and strong governance to ensure data integrity and business value. Robust data governance frameworks support compliance, risk management, and proactive data stewardship. Organizations in the 'Optimised and Governed' stage have a single owner or entity responsible for the maintenance of the corporate-wide information management strategy and can clearly communicate the link between data quality and financial performance to the board.
To practically move up this maturity curve, an organization typically needs to increase leadership awareness and commitment to data quality as a strategic asset, implement data governance policies and assign clear stewardship roles, invest in data quality tools for profiling, cleansing, and monitoring, embed data quality practices into business processes and decision-making, and continuously measure data quality KPIs and improve practices based on insights.
By assessing their maturity on the data quality maturity curve, organizations can improve the value of their data assets and create actionable steps to enhance their overall business strategy. 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.
As data continues to increase in value to businesses in the burgeoning digitalized world, it is crucial for organizations to understand and navigate this maturity curve to ensure they are making the most of their data assets. For more specific details about the Experian Data Quality Global Research maturity curve stages and transitions, consulting Experian’s official data quality research and whitepapers would provide the definitive framework.
Technology plays a crucial role in the progression through the data quality maturity curve, as automated tools are instrumental in the 'Managed' stage for monitoring and managing data quality.
Data-and-cloud-computing solutions can facilitate the continuous improvement practices at the highest maturity level, 'Optimised and Governed', by enabling advanced analytics, automation, and effective governance for data integrity and business value.
