The data life cycle describes the stages that your organisational data goes through, from initial capture through to the decisions ultimately made from its use.
There are many views on the life cycle of data, and each has its own context. It’s important to remember that data can take many forms, not just digital.
My perspective on the Data Life Cycle is to use data for the betterment of the organisation through the support of strategic decision makers.
These are the phases that every byte of data will go through:
A range of software systems allows you to capture data from every part of your organisation. Data can also be captured manually or automatically and takes the form of customer orders, staff timesheets, operational data, live feeds from connected technology, and so on.
Most data automatically categorised can be captured within the system. Categorisation also provides a context to the data and gives it meaning beyond the software system. This is what allows us to match up customer orders in one system with customer sales calls in another.
Data maintenance involves cleansing and enriching the data already captured or updating it to reflect changes in the real world. Again, much of this is automated, but a greater amount of manual work usually encountered in maintaining data due to the ad-hoc nature of it.
Data is the backbone of all reporting within an organisation. Besides, every report relies on the previous stages happening correctly. As a result, an error found in a prior phase usually requires a huge amount of work.
Business Intelligence is gaining traction in business. Especially where more organisations are extending their traditional reports with dashboards. These dashboards are live, interactive, and connected to data that has been QA’d.
The C-Suite and executives are required to make well informed decisions to ensure the strategic objective is on track. The more up to date information they have about the performance of their organisation, the more prepared they are to make the most appropriate decision.
Some Data Life Cycles include disposal as the final stage. I’d argue that you shouldn’t delete or archive anything and you don’t need to. It costs almost nothing to store petabytes of data so why not keep it. You never know when it may be useful.
Whatever industry you are in and regardless of the systems you use, your data will likely follow a data life cycle similar to this.
What will set you apart from the pack is digitising and automating as much the life cycle as practically possible.