The 10 Habits of Data-Savvy Managers

By John McCormick  |  Posted 2007-09-26 Email Print this article Print
 
 
 
 
 
 
 

A data-quality expert offers some advice, for those to whom data and dollars are one and the same.

Poor data quality is an ill that continues to plague organizations - with an estimated 25% of the data in the information stores of big businesses inaccurate, incomplete or duplicated.

Tom Redman, president of Navesink, a data-quality consultancy, says that improving data quality is as much about business practices and policies as it is about technology.

Redman has been helping companies improve data quality for more than a decade, and has found that organizations that manage data well have 10 habits that others would be wise to imitate. In an interview, Redman said that the companies that excel at data quality:

1. Focus on their most important customers. Companies need to prioritize their data quality efforts and this is a great way to do it. For instance, many companies decide that their new customers are the most important. They know that sending out a first bill or statement with misspelling or inaccuracies can send a customer into the arms of the competition. So they'll target this area for immediate improvement.

2. Manage processes. Companies need to create clearly defined business processes, laying out how data is passed around the business. One way is to simply adhere to the business processes outlined by their enterprise resource planning tools, although many companies customize those workflows for their operations.

3. Work closely with external data suppliers. A problem for many companies that aggregate data, such as data brokers, is that they have to rely on data from their suppliers, and the quality can be spotty. The problem stems from the fact that companies often don't spell out their expectations, or measure the performance of their data partners. Simply establishing clear benchmarks will eliminate most data quality issues. "That will usually get you 95% of the way home," Redman said.

4. Measure at the source. Businesses need to take stock of the data as it is entered into a database or repository. By periodically pulling out data sets and looking for errors, companies can spot weaknesses in their data collection and aggregation efforts.

5. Control at all levels. Data quality experts put in controls that stop bad data from getting in, but they don't stop there. For example, they make sure that any errors that may fall through the cracks are picked up and prevented from contaminating other data stores. Companies can build business rules into their systems to check data--such as a person's name that erroneously contains a number—as it passes from one department to the next.

6. Have a knack for continuous improvement. Companies need to constantly review and tweak their data handling processes. "Companies that get in the habit of improving, improve all the time," he says.

7. Set and achieve aggressive targets. The key to continuous improvement is to set aggressive targets. Data experts establish goals, such as halving the number of data errors found in a process. Then, once that goal is achieved, they again set out to cut the number of mistakes by half.

8. Formalize management accountability. Some companies make I.T. responsible for data. Others put the business unit in charge. Redman says the key to success here is to make those who create the data responsible for its accuracy.

9. Establish broad, senior leadership. Successful companies don't have one person leading their data quality efforts—they have managers across the organization committed to the drive. This is especially important in larger organizations, where data is constantly moving around.

10. Recognize that the hard issues are soft. These are the political issues that need to be handled—such as what data is required from employees. Getting some people, such as salespeople, to completely and accurately enter all their contacts and call reports into a customer relationship management may take some convincing. Doing so, however, has big advantages.



 
 
 
 
 
 
 
 
 
 

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