Navigating the data management lifecycle: end-to-end info management

Building effective processes for managing data throughout its lifecycle can help maximize the value of information. But that’s easier said than done.

If information is the lifeblood of the modern enterprise, then many organizations are suffering from serious circulatory disorders. Corporate information often is siloed, uncatalogued and managed via a patchwork of different tools and policies, reducing its potential business value and leaving organizations vulnerable to a variety of data-related problems.

To help remedy that situation, analysts and consultants prescribe building an effective end-to-end data management process that encompasses the entire lifecycle of both structured and unstructured information – from data entry and document creation through data validation and integration to archiving for long-term storage and disposal of no-longer-needed data.

Michael Miller, director of the records and information management practice at Array Information Technology, a consulting and IT services firm in Greenbelt, Md., has worked previously in traditional information archives as well as records management positions. He approaches data management with the clarity that comes from working with tangible, physical documents and records.

“Where I come from regarding the data management lifecycle is making sure that there is a real lifecycle,” Miller said. “The focus needs to be on ensuring the authenticity, reliability, usability and integrity of the data you have and want to keep.” That also means information needs to be evaluated for usefulness on an ongoing basis, with the idea being that much of it will eventually become a candidate for deletion as it gets older and is used less and less frequently, he added.

But in many cases, the long-term usability of data isn’t planned for or even discussed during the system design and development process, said Miller, who also teaches at the University of Maryland and Drexel University. Nor is disposing of data: “I’ve run into many systems where there is really no way to delete data that is no longer needed, because that capability was never considered when the system was created,” he said. Likewise, system designs rarely include built-in capabilities for migrating data to other systems in the future, according to Miller.

In addition, it can be difficult to convince corporate and business executives of the importance of creating data management processes that provide a framework for managing information through all the phases of its existence across an enterprise, Miller warned. “Unless there has been a [data] disaster, people aren’t willing to invest the time and money to do it well,” he said.

The threat of new regulations and compliance enforcement actions might help focus organizational thinking about the value of a comprehensive data management lifecycle, Miller noted. He also pointed to instances in which companies have had to settle court cases – and take financial hits in the process – after being unable to locate electronic documents needed for legal discovery purposes in their systems.

Assessing the business benefits of a data management lifecycle strategy
Beyond playing on such fears, another potential approach for structuring an end-to-end data management strategy and securing funding for it is to look ahead three to five years and assess the broad business issues that will arise in an organization and how sound information management practices can help address them, said Kalyan Viswanathan, a U.S.-based consultant and global practice leader at Tata Consultancy Services Ltd., an outsourcing and IT services firm based in Mumbai, India.

Effective data management throughout all the stages of the data lifecycle can also help lay the groundwork for the development of more advanced systems as well as new business services, Viswanathan said.

Fred Hargrove, a practice leader at consulting and professional services firm MorganFranklin Corp. in McLean, Va., said some of the biggest obstacles to implementing an all-encompassing data management strategy within an organization include a lack of executive sponsorship, an unwillingness on the part of IT and the business to share responsibility for data management, and a lack of transparency about unsuccessful data management initiatives in the past.

And don’t forget about the basics of data management, said Jason Wisdom, president and principal of Wisdom Consulting Inc., a business and IT consulting firm in New York. For example, he recommended making it a priority to establish enterprise-wide data retention and deletion policies as soon as possible in order to put some discipline in place in those areas while other facets of an overall data or enterprise information management program are being considered.

Wisdom warned that the growing use of external cloud computing services could make some aspects of data management even more challenging for organizations as a whole and data managers in particular. Often with cloud services, he said, “there are extra backups, which a data administrator does not have access to or even knowledge about [but] which can store sensitive information.”

The bottom line, he added, is that you should “do a full sweep” of the systems and databases used by your organization to make sure that all information is being properly managed from the start of its lifecycle to the end.

Alan R. Earls is a Boston-area freelance writer focused on business and technology.

Dig Deeper on Data governance strategy