Managing the Data Lifecycle

In business, we rely on applications, services, and data to improve the efficiency and effectiveness of the organization. One of the side-effects of having a sophisticated capability is thinking we now have the capacity to analyze all the data that enters our IT system; the problem that surfaces consistently  is losing track of key aspects of the data and what it reveals about our organization. The actual insight provided by this information comes from having a complete view of the data from its creation to its retirement. This data lifecycle should be our focus, since it will help us better understand its actual value and impact on operations, as well as how it can be used to ensure the organization can grow and prosper.

The Data Lifecycle

There are three main areas to understand with respect to the lifecycle of data once it is created:

  • Maintenance
  • Entitlement
  • Retirement

Each area is important to the support of operational activities, which ultimately contribute to positive business outcomes. Understanding the entire data lifecycle can provide valuable insight into how to run the organization better.


Maintenance of data is a vast area that demands many resources. It involves understanding what should be remediated versus what could be eliminated.

The key to any data maintenance plan is a data quality improvement strategy. This is required across the entire organization in order to leverage all available data source types. The more frequently these maintenance activities are performed, the more likely it is that organizations will be able to leverage data for beneficial purposes.


Entitlement is a different type of effort. It focuses on making sure the right people have access to the right data.

Only the appropriate or authorized people will fully understand the meaning of data they view or consume, since these individuals will have innate knowledge about the context of the data and be able to interpret what it means. For example, someone with a strong security background might detect data modification patterns that suggest a possible breach, or a systemic issue about how the data is being maintained. Data at the discrete or elemental level might not convey this message unless individuals with knowledge of the entire lifecycle are able to understand it and translate it, so others can take appropriate actions.


Lastly is the retirement of data, which in some cases is the most vital step.

When we recognize that data or a data source is no longer adding value to the organization, it must be disposed of or eliminated from further consideration to avoid negatively skewing or influencing decisions. In addition, the timely retirement of data sources provides valuable information about the data itself, as well as the usefulness and longevity of the associated data sources. It could also be the reason why decisions based on this type of data may not have matched expectations.

As everyone in IT knows, there is no shortage of data and the tools to interpret it, but it is critical to have a systematic process to manage the data lifecycle for it to be fully leveraged. Without such tools and processes, data will just become more noise in our environment and prevent us from reaching successful outcomes. Outcomes will only begin to change when we look at data in a more comprehensive and holistic manner that factors in the entire lifecycle; only then will we have all the information needed to help the organization make the best decisions possible.

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