We’re almost half way through the year and by now more than half of us have forgotten or become complacent about our New Year’s resolutions. But it’s never too late to get back on track! The same is true about data quality management; it’s never too late to restore order to your company’s data and combat the consequences of poor data quality. Data quality management should be an ongoing process since bad data affects business intelligence systems and ultimately the decisions based off of BI. It’s a big job and someone has to take responsibility for it. Who should that be?
“Data quality is not solely an IT issue…success depends mostly on involvement from the business side…Business professionals must ‘own’ the data they use.”
– Gleanster Deep Dive: How Top Performers Improve Data Quality for Better Business Intelligence, January 2011
The knee-jerk reaction to the question of who should be held accountable for maintaining data quality is “the data steward,” “the data quality manager,” or any variation of that role. But who is the data steward? I believe that each organization should have several data stewards and that they should be the content owners or really, the people who most care about data quality. Here are a few examples:
The marketing director who scrubs the CRM system to ensure that lead information is correct often wears the hat of data quality manager. Data quality is important to marketers because good data (email addresses, mailing addresses, and other segmentation fields like revenue and industry) is necessary to avoid fail points in communication and to ensure that the target audience receives your message. With a 2011 Experian QAS research report revealing that 90% of organizations believe as much as 25% of their departmental budgets were wasted during the last year as a result of inaccurate contact data, you can bet that your marketing team has a CRM data clean-up project in the works. Sometimes that means using an appending service to fix bad email addresses and sometimes that means manual research and data entry, but there is true ROI for marketing data quality initiatives.
The account manager who oversees a territory and enters sales and account information in the CRM system is also responsible for data quality…
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: