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Poor Data Quality can Undermine Business Intelligence
Keeping data up to date and fresh is a global problem. How many times have you searched the internet to find the information in the top results to be out-dated? The same problem plagues data managed in enterprises.
A Data Warehousing Institute’s study estimated that $600 billion are lost each year in US businesses due to poor data quality. The impact of poor data quality are many and include data reprocessing and loss of customers due to dissatisfaction. Data quality can adversely affect the performance and efficiency of operational systems by undermining business intelligence. Data is a strategic asset and if not properly treated can easily become outdated, inconsistent, orphaned or duplicated.
Bad data can be the result of problems related to data entry or from just becoming stale. Often data quality problems are introduced when systems are converted or integrated (such as during mergers and acquisition).
Especially in the area of regulatory compliance, poor data quality can be a very costly proposition. A new study from AIM Software finds that data quality is the biggest problem for companies trying to comply with regulatory compliance initiatitives like Sarbanes-Oxley and Basel II. 69% of companies are implementing risk management solutions for regulatory reasons.
The AIM study of 1027 IT managers at financial services firms also found that 44% of companies are investing in “reference data management” systems over the next two years to improve data quality.
Reference Data Management refers to management of data that resides in “master” tables. It boils down to their not being a consistent business language in communicating data. Multiple applications and sources of data, many coming from different domains, are frequently not coordinated well among business, process and technology groups. There often is no single source of truth for reference data.
