The term “life cycle” is used extensively when talking about information management but none of the existing definitions deal effectively with the death phase or even recognize that it exists. When you create a new BI report, do you also establish a way to track usage so that you know when it is no longer needed? When you implement a new IT system, do you define the data retention and deletion policies and procedures for its dataset? Does your company routinely evaluate applications and repositories for inactive data that is not adding business value? If you answered NO to any of these questions, you are not adequately managing the full life-cycle.

It’s time we expanded the notion of information life-cycle to include its eventual demise. My view of a more complete life-cycle model for information management consists of six stages each of which presents unique challenges and requires unique methods and tools.

1. Ideation & Design: Conceiving new information is easy, but the hard part is making it consistent with other relevant information at multiple levels in the hierarchy including the operational level, functional domain, enterprise level “one version of the truth”, and industry supply chain. Modern methods include canonical models (which include industry standards) and information architecture as cornerstone practices.
2. Manufacture & Implement: This stage is probably the most mature since it has received a great deal of the attention over the years in an effort to reign in project costs and to improve success rates of complex initiatives.  Modern methods include lean and agile methodologies in conjunction with factory-style mass-customization manufacturing techniques.
3. Maintain & Grow: There was little focus on this stage until about 10 years ago when we saw the emergence of competency centers; permanent teams within an enterprise focused on optimizing and sustaining information management as volumes exploded and as technology continued to evolve. Modern methods include master data management and metadata management to ensure that data remains relevant and is effectively governed as its use expands.
4. Acquire & Divest: This stage also has been with us for years, but until recently has been treated strictly on a project basis.  Leading edge practices view application (and data) acquisitions, consolidations and segmentation as permanent and ongoing processes that can also benefit from factory-style repeatable methods.
5. Retire & Protect: Stage 5 is an emerging capability as organizations struggle with the issues of archiving legacy data that is no longer active but must be protected and retained for legal reasons or because it contains valuable intellectual capital. Leading edge organizations are establishing formal governance processes and roles in order to sustain data for 10 years or more after it is no longer needed for operational activities (the longest format requirement I’ve come across in a commercial business so far is a 75 year retention policy). Furthermore, new storage technologies and archival software solutions present opportunities for a dramatic reduction (up to 95% with Informatica ILM) in the cost of storing corporate information.
6. Destroy & Recycle: Most enterprises have some form of data retention policy for unstructured information and paper documents, but are only recently beginning to tackle the issues for structured data. The least understood aspect of data destruction is how to recycle and reuse key concepts and ideas which are embedded in legacy data.

Curiously, the software industry is about 60 years old which is about when many people begin winding down their careers and move into retirement. Up until now stages 5 and 6 haven’t been a major concern for organizations since we have all been to busy giving birth to new information and growing rapidly. But now that we’ve passed middle-age and find our systems bloated with unused and inconsistent data, and the volumes becoming unmanageable, it’s time to seriously tackle retirement and death – which is after all is part of life.