Business Intelligence Blog from arcplan
12Jun/120

Poor Data Quality – Part 3: Strategies to Combat It

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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. Marketers using Salesforce.com, for instance, may customize data fields so that when adding a new contact's information, it is mandatory to enter certain fields such as the person's surname, company name, and country. This way, the marketing team can count on information they truly need for customizing emails and running reports being present in the system. Good data practices will save you trouble down the line, so source data needs to be clean and consistent.

3. Use technology
The aforementioned Gleanster report also revealed that in addition to input from business users, top performing companies use data cleaning tools to catch entry errors, false records and duplication. A company may choose to implement a Master Data Management (MDM) system or an Enterprise Resource Planning (ERP) system to manage company data. These systems require rigorous planning before going live and they uphold strict standards for maintaining quality data throughout the company. Extract, Transform and Load (ETL) processes are another way to clean up data on the fly. There is no short supply of these user-friendly tools which can be used to cleanse and update your data in bulk.

Getting back on track with good data is worth the effort and investment for your business. The cost of preventing bad decisions through better data management is lower than the cost of failure by neglecting it. Stay tuned til next time when we’ll take a closer look at how technology can help address your company's data quality needs.

Additional resources:

Monique Morgan

About Monique Morgan

I'm the Marketing Project Manager at arcplan and work out of our Wayne, PA office. I'm originally from Jamaica but came to the US for college and I've been here ever since. I'm the voice behind a lot of arcplan's videos on YouTube - check them out here: http://www.youtube.com/arcplan.
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