As organizations realize that their data can be a true competitive advantage, they oftentimes struggle with making data more useful and reliable; this is one of the reasons data quality has become a hot topic for discussion in the business world. Any successful data quality program combines three main components: people, processes, and technology. Sometimes, the people factor can be the most troublesome because different groups (with different agendas) can struggle to find roles in data quality projects.
The trouble often starts with a basic disconnect between IT and business users. The IT function typically "owns" systems and applications within the enterprise, but the end user of the data is often a line-of-business employee who must make intelligent decisions from the information. Getting each of these groups on the "same page" is critical for implementing and maintaining any system, especially a CRM application.
Here are a few ways that companies can assemble teams to build better data:
Identify data stewards
Every team needs a leader; a data quality project is no different. Companies should look for a person who combines technical knowledge with business sense, and has the background to guide the team in identifying and resolving data quality problems.
Commonly known as a data steward, this person could be a seasoned IT manager who has worked with business users to design and deliver applications. Or, organizations could go with a technology-savvy business user who is familiar with the IT infrastructure. Regardless, he or she will be responsible for reviewing all aspects of the problem--from system requirements through business impact.
Assemble a cross-functional data quality team
The next step is to give the data steward a mix of expert-level resources from both IT and business functions. A CRM system might have a four- to five-person data quality team, depending on the size and scope of the project, made up of IT professionals assigned to maintain the system and manage data storage and access, as well as analysts, managers, or technical personnel from the business side to represent the business expectations of the data.
Moreover, organizations would do best to put an actual end user on the data quality team. With end-user involvement, the team can verify that any proposed procedures and business rules will not significantly hamper the work of the system's user base.
Collaborate to design business rules for data quality
Data quality projects involve more than just pieces of technology. The team should begin by outlining a set of "best practices" for all users to follow. These can include everything from deciding how a data element should be encoded (for example, phone number coded 999-999-9999 versus 9999999999), to assigning access rights required to add, update, or delete data.
Once documented, the team can embed these business processes within a data quality application. (Companies typically use data quality technology to automate the rules--in batch or in real-time--to ensure that data meets the business requirements on an ongoing basis.)
With these steps companies can bridge the gap between IT and business to create a more effective data quality program. Since both sides work to frame the data quality initiative, each has a stake in the success of the initiative.
About the Author
Tony Fisher is the president and general manager of DataFlux Corporation, the leading provider of end-to-end data management solutions that help companies analyze, improve, and control business-critical information. Tony can be reached at email@example.com. For more information on DataFlux, visit www.dataflux.com