Data quality is a critical component of business assurance, the practice of reducing risk exposure and improving operational efficiency through business controls and compliance policies. Poor quality data exposes organisations to risk, jeopardises the performance of operational systems, and undermines the value of business intelligence systems on which organisations rely when making key decisions.
The emphasis in information systems throughout the last decade has been on the creation of applications that improve organisational productivity and efficiency and generate customer loyalty, for example, Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), Supple Chain Management (CRM), and Business Intelligence (BI).
Companies spend enormous resources on these technologies to create streamlined business processes from which they can make competitive gains. While these information management systems have the potential to improve efficiency and productivity, the data and transactions upon which these applications rest must be valid for this potential to be realised.
Inaccurate or incomplete data compromises the ability of organisations to make decisions and take action. Corporate managers who formulate business plans and strategies based on analysis derived from data warehouses and other enterprise applications are at risk unless a data quality program can ensure data validity at the most granular transaction level. Decisions based on faulty data can cause direct financial loss, undermine customer loyalty, and damage an organisation’s credibility.
The ongoing costs of poor data quality can make the costs of initial system engineering pale by comparison. Data quality experts estimate that bad data can cost a business as much as 10 to 20 percent of its total system implementation budgets.