High quality data is needed to capture the full potential of the enterprise and deliver all the benefits of ERP systems, customer relationship management (CRM) and business intelligence (BI) initiatives, compliance and data warehousing solutions. Conversely, low quality data causes transactions, processes and projects to fail, leading to increased costs, additional risks, reduced confidence in enterprise data and potential business loss.
Whereby budget owning managers anecdotally understand that high quality data leads to improved process efficiency and increased confidence, there is often no understanding of :
- How to justify the cost or value of a data steward team
- The process or roles focused on managing data as an asset
- The business impact and business value generation of a data quality team
As a result, most organizations are struggling to build data quality initiatives and thus failing with the implementation and ongoing management of data quality processes.
By defining a framework of data quality dimensions, such as uniqueness, completeness, accuracy, non-obsolescence and consistency, data quality can be measured and monitored. Data quality can be linked to a business process by identifying and measuring the quality of data and its compliance with core business rules critical for that process to succeed.
The reasons organizations invest in data quality are to :
- Improve time to market
- Increase people trust in data
- Support enterprise flexibility
- Leverage from information as a competitive advantage
- Support agility and flawless execution
- Capture the full potential of the enterprise
- Reduce cost and rework through efficiency