Policies and tools make company-wide data quality possible
When a piece of low-quality data is fixed, are your employees making long-term changes in the system or merely adjusting the error for their own immediate purposes?
Unless companies have processes in place for workers to notify everyone who uses the data that there is a change or a mistake, those errors can proliferate and continue to cause problems for the entire company later on. This applies to client and business partner address management as well as other data-related issues, such as transactional histories, performance statistics and more details that may impact anything ranging from a marketing campaign to a major strategic decision.
Thomas C. Redman recently wrote a post for the Harvard Business Review blog network that explains the importance of having a company-wide system for locating errors in databases and regularly cleaning, validating and monitoring information with the use of data quality tools. Without policies and software that enable employees to make changes whenever they uncover a discrepancy, data users further down the line could be saddled with the consequences of bad information.
"People and departments must continue to seek out and correct errors," Redman writes. "They must also provide feedback and communicate requirements to their data sources, and be mindful of and create data to meet the 'next person's' requirements."
He tells the story of a businesswoman who is preparing a presentation and thinks a certain figure seems off. When her assistant sifts through the information and uncovers the error, he makes a correction to his boss' presentation but fails to update the raw data. Both the assistant and the executive realize that they will have to verify the data from the source, yet they do not discuss the importance of alerting the data creators of the errors.
Redman says there are two main points in the data lifecycle - its creation and its use. Its quality is determined during its application, but proactive data management requires connecting those two points, he says. This way, corporations will be able to "ensure that the moment of creation is designed and managed to create data correctly, so everything goes well at the moment of use," he adds.
Likewise, organizations also need to develop proactive programs for rooting out bad data before it negatively affects the business. Do not wait for someone to find errors or duplications, go hunting for them. Set up initiatives for correcting contact data quality and other kinds of information in order to avert data-related disasters and achieve greater efficiencies within the organization.
Unless companies have processes in place for workers to notify everyone who uses the data that there is a change or a mistake, those errors can proliferate and continue to cause problems for the entire company later on. This applies to client and business partner address management as well as other data-related issues, such as transactional histories, performance statistics and more details that may impact anything ranging from a marketing campaign to a major strategic decision.
Thomas C. Redman recently wrote a post for the Harvard Business Review blog network that explains the importance of having a company-wide system for locating errors in databases and regularly cleaning, validating and monitoring information with the use of data quality tools. Without policies and software that enable employees to make changes whenever they uncover a discrepancy, data users further down the line could be saddled with the consequences of bad information.
"People and departments must continue to seek out and correct errors," Redman writes. "They must also provide feedback and communicate requirements to their data sources, and be mindful of and create data to meet the 'next person's' requirements."
He tells the story of a businesswoman who is preparing a presentation and thinks a certain figure seems off. When her assistant sifts through the information and uncovers the error, he makes a correction to his boss' presentation but fails to update the raw data. Both the assistant and the executive realize that they will have to verify the data from the source, yet they do not discuss the importance of alerting the data creators of the errors.
Redman says there are two main points in the data lifecycle - its creation and its use. Its quality is determined during its application, but proactive data management requires connecting those two points, he says. This way, corporations will be able to "ensure that the moment of creation is designed and managed to create data correctly, so everything goes well at the moment of use," he adds.
Likewise, organizations also need to develop proactive programs for rooting out bad data before it negatively affects the business. Do not wait for someone to find errors or duplications, go hunting for them. Set up initiatives for correcting contact data quality and other kinds of information in order to avert data-related disasters and achieve greater efficiencies within the organization.