Business Intelligence Blog from arcplan

Poor Data Quality – Part 3: Strategies to Combat It


82% of top performers implement a process for continuous data quality improvement.

–  Gleanster Deep Dive: How Top Performers Improve Data Quality for Better Business Intelligence, January 2011

In recent posts, we’ve explored the consequences of poor data quality and also evaluated who should be responsible for maintaining good data within an organization. We’ve seen that there’s no quick fix for subpar data quality; rather, ensuring superior data requires a well-orchestrated team effort.  A 2011 Gleanster benchmark report revealed that top performing companies understand that maintaining data quality is an ongoing discipline requiring constant attention. Organizations successful in the pursuit of better data have implemented strategies such as these to continuously improve their data:

1. Have a policy in place and take ownership
Organizations may hire a data quality manager as a dedicated resource and the first line of defense against bad data. The data quality manager governs data processes by ensuring that reliable information is loaded into the data warehouse and is responsible for data processes such as migration, manipulation and analysis. Additionally, some BI systems like arcplan allow authorized users to write back data directly to the data source. In this case it is the responsibility of that user to enter accurate information and not corrupt the system with erroneous data.

2. Enforce data quality at the source system
“Garbage in, garbage out” is the phrase to keep in mind. Making particular information mandatory in the source system is one way to go about maintaining data quality…

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