Business Intelligence Blog from arcplan
14May/121

Poor Data Quality – Part I: The Consequences

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Data Quality - Garbage in, Garbage out?We’ve been thinking a lot about the various ways organizations can improve their existing business intelligence applications. Many of arcplan’s customers have been with us 5-10 or more years and are continuously improving their BI along the way. Some of the initiatives we frequently hear about are related to data quality improvement, but this may be an anomaly. According to Ventana Research’s recent study, less than half of organizations surveyed have taken on some kind of information management initiative, like data quality or data integration improvements, in the last 2 years due to budget restrictions or lack of employees with the right skills.

I’d argue that data quality initiatives should be a “top 5″ priority for organizations in 2012. Why? Because of stories like this: A friend recently told me about a meeting at his company where the regional sales managers were giving their summaries of pipeline opportunities. During one of the updates, a director interjected that he didn’t see the favorable developments mentioned in Salesforce, their CRM system. Based on the information that was present in the system, the director figured that the quarter would be an average one. However, the updates from the sales manager would really swing the potential outcome of the quarter in a positive way. Now I bet that director had to make some decisions that were compromised by the (lack of) current information in the CRM system. He might have started strategizing about how to re-engage with the (assumed) stagnant prospects, started working with marketing on a nurturing campaign, asked the telesales team to reach out…any number of things could have happened based off of the faulty information available to him.

Unfortunately, many organizations have to contend with poor data quality which ultimately results in poor decision-making. After all, decisions are no better than the data on which they’re based. Reliable, relevant, and complete data (as opposed to the incomplete data set available to the director in my example above) supports organizational efficiency and is a cornerstone of sound decision-making. So what are some of the consequences of sub-par data quality?

1) Mistrust. Poor data quality often breeds mistrust among internal departments. I read a great example from 1998 (if you can believe it) that could have been written yesterday:

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