Magazine article CRM Magazine

Ensuring Data Quality for Business Intelligence: Creating Strategies around Data for Reliable Analytics

Magazine article CRM Magazine

Ensuring Data Quality for Business Intelligence: Creating Strategies around Data for Reliable Analytics

Article excerpt

EXECUTIVE SUMMARY

In order to differentiate and get ahead in today's market, businesses are relying on more intelligence for key business functions. With this shift, organizations are prioritizing data for the year ahead.

Organizations use a host of different sources for intelligence, from internal systems to third party data providers. However, businesses need to ensure accuracy before making business decisions based on data analysis. Without correct information, businesses will operate on poor intelligence, potentially wasting resources and damaging key customer relationships.

To ensure that business intelligence provides the desired benefits to an organization, individuals need to first understand the current data quality landscape and then learn how organizations can improve data quality throughout the CRM.

THE DATA QUALITY LANDSCAPE

To better understand the data quality landscape, Experian QAS surveyed over 800 global business professionals with roles related to data quality. Key industries included finance, government, manufacturing, retail, education, and more.

The study found that most organizations are motivated to keep data accurate. The main reasons cited by organizations for maintaining data accuracy were to increase efficiency, enhance customer satisfaction and enable more effective business decisions.

The research confirmed the point made above--many organizations are maintaining data to enable more informed business decisions. This motivation increased five percent over a similar study conducted in 2011.

Another growing trend related to business intelligence is single customer view. 37 percent of organizations surveyed have a contact data quality strategy to support a single customer view. This strategy was especially important to data management and IT professionals.

Even with these acknowledged strong motivations, most organizations still have data accuracy issues. 94 percent of respondents suspect that their customer and prospect data might be inaccurate in some way.

The main cause of these issues is human error, cited by 65 percent of organizations participating in the research. Other causes clearly lag behind this front runner, but included a lack of internal manual resources, an inadequate data strategy and insufficient budget. …

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