10 Jul Do you know the 7 phases of the data life cycle?
The Data Life Cycle describes the stages that your organisational data goes through, from initial capture through to the decisions that are ultimately made from it’s use.
There are many views on the life cycle of data and each have their 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:
Data from every part of your organisation is captured in a range of software systems. Data can be captured manually or automatically and takes the form of customer orders, staff time sheets, operational data, live feeds from connected technology and so on.
Most data is automatically categorised within the system it’s captured in. Categorisation 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 is usually encountered in maintaining data due to the adhoc nature of it.
Data is the backbone of all reporting within an organisation. Every report relies on the previous stages happening correctly. A huge amount of manual work is required to correct an error found in a prior phase.
Business Intelligence is gaining traction in business and we are finding many 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 information they have about the performance of their organisation and the more up to date that information is, 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 life cycle similar to this.
What will set you apart from the pack is digitising and automating as much of the life cycle as practically possible.