No need to huff and puff

It is absolutely crucial to link the data management life cycle with the information governance framework, because the data management life cycle is the means by which the information governance framework is applied on a daily basis.

Part of developing an information governance framework is integrating the data management life cycle into identified, existing processes, particularly the software development life cycle. It encourages seeing beyond the scope of individual systems or processes to divulge how data will impact those systems or processes.

It is a fundamental aspect of uniting the chief information officer's thinking with that of other C-level executives to align operations with strategy. I think of it as a thought model that guides business and IT to ask the right questions about how data is being managed at different stages.

The data management life cycle similarly requires the information governance framework be clearly defined, because it supplies answers to questions such as: what policies should be applied to data; how should data be classified; how should data be secured; and what standards must be applied to different activities, such as storage or modelling?

The information governance framework is like the architectural blueprint of a house: it is the building plan that demonstrates what the finished product will resemble and what can reasonably be expected of its function. The information governance office or organisation then breathes life into the plan by constructing the building and furnishing it to make it fit for purpose.

The information governance framework defines the policies, practices, and procedures required to govern data properly. A common problem I encounter in the field is that, although many companies have this in place, the framework has commonly been developed with no regard to life cycle, so the two are disparate. Separately, they make perfect sense, yet in combination, they become counter-productive. It is crucial to highlight the relationships that bind their co-existence, such as the doors and passages logically linking the rooms of a house, so the furniture may serve the overall objectives of its occupants.

The data management life cycle defines the stages data must undergo throughout its useful existence. Many stages are common to all data. Continuing the house analogy, data life cycles may be equated to the lives of people who occupy houses. All humans have common life cycle traits. Humans are born, they live, and they die. Thereafter, the traits become more individualistic.

Some people get the measles, learn culture, go to school, age to become teenagers and adults, and perform rituals based on religion, culture and other beliefs. People end up with myriad traits that, although many individual traits are shared with other people, collectively these specific traits make up individuals.

The data management life cycle defines the stages data must undergo throughout its useful existence.

There are common features and properties for every person, for every group of people, and for every increasingly narrowly focused group of people, until drilling right down to the individual. The needs of large swathes of the human population can be satisfied through standardised offerings, and then the offerings can be customised when drilling down into ever more granular groups.

Data life cycle management is best achieved through a similar approach. The generic model offers the broad basis of what every business needs, and then customises it to suit its specific requirements. Businesses may all rely on data to function; many have databases, some warehouses, but these are all different technologies, linked in different ways, using different processes.

Issues such as legislative framework will define each company's needs with an impact on their data quality, data security and data retention policies, procedures and processes; the technologies they employ to help them; and the people they employ to achieve their goals. They will have their own operational considerations, market factors and customer behaviours for which they can cater by tailoring their data life cycle management policies and procedures. They will have their own environments populated by their own existing data stores, combinations of paper-based and digital information, process-based information and metadata.

But, uniting the information governance framework with data life cycle management is crucial to consistent and persistent application throughout. It helps to cope with growing data volumes, varying data aging speeds, and achieving good quality analytics. It can be the difference between dying, surviving or thriving - just like The Three Little Pigs who made their houses of straw, wood and stone.