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. There's a reason management insists on all customer and prospect information being stored in company systems. The account manager (or his/her predecessor) will need to refer to this information to keep track of accounts and identify up-selling and cross-selling opportunities. If the information is bad or missing, this can lead to poor decision-making or even offending the customer ("shouldn't my vendor know the details of my account?!"). Account managers should also be responsible for periodically checking in with customers to determine who the correct point of contact is. Has someone moved on to another job? Has someone been promoted? It’s manual work that falls under the umbrella of data quality.
The finance director that makes budgeting decisions and allocates resources for each department, manages daily cash requirements and keeps track of customer payments is responsible for the accuracy of these records in order to make decisions about the financial management of the company. The finance team should also be concerned with CRM data if financial systems are linked to the company's CRM. Who looks bad when a customer appears delinquent because an invoice was sent to the wrong address?
Anyone responsible for data entry is ultimately responsible for data quality. Gartner analyst Ted Friedman says that most data quality issues are the result of human error. To mitigate this, companies should thoroughly train employees to understand the ripple effects of bad data and appoint a data steward on the team to oversee data entry and handle disputes.
Author and analyst Wayne Eckerson was quoted last week at a conference saying "The good thing about bad data is it provides a lifetime employment opportunity." Sad but true. What organization can say with certainty that their data is 100% clean?
Next time, we'll take a look at steps your organization – no matter how large or small – can take to keep up with data quality management.