Data Lifecycle Literacy

A policy-based approach that manages the path of data throughout its lifecycle: from creation to deletion, while taking into consideration - where is data getting created, stored, used, tampered, and deleted, is as we know called Data lifecycle management.

While on one side Data lifecycle management policies help businesses manage the enormous size, variations, and velocity of incoming data, Data lifecycle management mistakes are far too common than we like to accept

So what are the most common data lifecycle management mistakes?

Data has become the lifeblood of most enterprises, both small and large, helping an enterprise add value, maintain competitive advantage and grow, yet there are some fundamental mistakes that most organisations commit on their Data Life cycle journeys: 

1. DETAILS BEFORE CONTEXT

Not prioritizing ‘why’ over ‘what’ in a Data management policy, since providing the context to data aspects that matter most, is required, before getting into the implementation details.

2. TREAT ALL DATA AS EQUAL

Not considering that different data types serve different purpose and hence need different storage, retention and deletion policies

3. IGNORE THE ‘HUMAN’

Failing to acknowledge that data loss from ‘human’ factor is a common occurrence and accommodating suitable backup and disaster recovery, within the overall policy

4. REGARD DELETION AS THE END GAME

Mistake of considering deletion as the absolute end point of a data, since data are often restored and brought back into the lifecycle

So, how to avoid committing such mistakes?

It’s true that Data can transform the modern enterprise and to attain this transformation, enterprises must develop a strong data strategy and an architecture that manages the lifecycle of data, without committing mistakes, as it finds its way through the enterprise.

While there are various ways to accomplish it, one particular approach is the CAD (Consult +Architect + Deliver) approach from Infovie, wherein the ‘Consult’ phase involves detailing the data lifecycle requirements of an enterprise and preparing a blueprint, followed by the ‘Architect’ phase of  drawing a Data lifecycle policy with mandates that are aligned with client ‘s milestones, and finally recording those milestones through effective knowledge transfer and efficient cut overs, at the ‘Deliver’ phase.

 Know more about Infovie and its offering -

 Write at: [email protected]

Or visit: www.infovie .com