Data sits at the heart of every organisation and is relied upon in key areas such as operations, analysis, decision-making, reporting and planning. It is therefore vital that data is fit-for-purpose for its intended use - this is what we mean by data quality.
The quality of data in an organisation can be degraded due to multiple sets of client information across divisions, poor control over data acquisition and data transfers during mergers and acquisitions. This can potentially lead to reputational damage, poor decision making caused by incorrect information, wasted investment and errors in financial statements.
Improved data quality can provide faster and more accurate management information reports, greater customer insights and facilitate regulatory compliance so that organisations can confidently place higher reliance on their data accuracy.
Data quality is a key component of Deloitte’s Information Management framework. From identifying point-in-time data quality issues, through to the full development of a data quality sustainment cycle, our end-to-end approach is structured into three main concepts:
- Build programme foundation: including stakeholder sponsorship; data quality committees and their terms of reference; data definitions
- Enhance data quality: including data quality profiling; root-cause analysis and impact; risk qualification; prioritisation of remediation activities; roadmap design
- Data quality sustainment cycle: including process implementation and efficiency; awareness and training; continuous monitoring and reporting framework; upgrade and enhancements.
Deloitte’s Data Quality team helps organisations to address their data quality challenges working with the market-leading data quality technology providers to identify issues and implement solutions tailored to each organisation.