Although almost everyone agrees that data is important to an organization's success, few see data as an organizational asset or a strategic imperative. But trustworthy data is critical to competitiveness, operational efficiency, risk reduction, and member and customer satisfaction.

As Debbie King, CEO of Association Analytics, and Christin Berry, senior director of business analytics at ASAE, noted in a presentation at last summer's ASAE Annual Meeting & Expo, association employees do not always connect the value of their data with the organization's bottom line or credibility. In fact, many routine processes—the ways associations handle e-commerce initiatives and data conversions, for example, and the use of multiple systems—result in poor data quality. Most important, an absence of consensus about data priorities and a lack of formal business rules or processes make effective data management almost impossible.

The impact of poor data quality can be costly, time-consuming, and frustrating, resulting in challenges like these:

• lack of confidence in data
• poor decisions based on inaccurate data
• expenditure of effort to reconcile differences among data sources
• time spent asking and answering questions
• delays in data distribution due to manual review

The technology research and consulting firm Gartner estimates that poor data quality costs an average organization $13.5 million a year, King and Berry noted.

An absence of consensus about data priorities and a lack of formal business rules or processes make effective data management almost impossible.

Cleanup and Prevention

It's important to dedicate time to audit and update your data. Data cleanup takes time, so to manage the process, be proactive: Develop a schedule and process for doing it. Here are some suggestions from King and Berry for tackling the challenge:

• Generate data integrity reports.
• Sort records to find duplicates.
• Filter to help determine missing or incorrect data.
• Review change over time to check for anomalies.
• Use realty-check scenarios to confirm data.

To prevent poor data quality, associations can take the following steps:

• Develop a formal data governance framework to oversee data quality.
• Develop data quality monitoring processes.
• Maintain a data dictionary: All staff should understand what each data field means and where it should be stored. Clear and consistent communication is vital for transparency, confidence, accountability, and consensus.
• Develop clear guidelines and business rules for data entry, management, usability, and security.
• Create a data-driven culture where everyone sees themselves as owning stock in good data.
• Build data quality into group and individual incentives.
• Link strategic priorities to data.

Complete, current, and accurate data is an essential and valuable asset that enables an organization to achieve its goals. Without a process to ensure its integrity, the organization's bottom line and competitive advantage are at risk. As King and Berry put it, "What gets measured gets done."