“Data really powers everything that we do.” – Jeff Weiner

Hence the life of data is crucial. And the series of phases it goes through during its lifetime highlights the true meaning of data to a brand or entity. 

This is when Data Lifecycle Management comes into the picture! 

Data Lifecycle Management structures the steps followed by the data of a company or brand to optimize it to it’s best during its lifetime. 

But, before that, it is essential to understand the entire journey of data and its phases of the lifecycle.

Data Capture

Data

Maintenance

Data Usage

Data lifecycle is the series of stages

through which a particular data goes

through from its capture or initial

generation to its archival or

deletion at the end of its life.

WHAT IS

DATA LIFECYCLE?

During its lifetime

a data goes

through seven

phases

The f rst and foremost

experience of data is to

pass within the f rewalls

of a brand. Data cap–

ture is an act of creat–

ing data values.

Common examples are

data acquisition or data

entry.

1

As the data has been

captured, it encoun–

ters data mainte-

nance. Data mainte-

nance supplies data

to the points where

data synthesis and

data usage occurs.

Next is data usage where it

reaches a point, and data is

used as support of a brand. It

is the application of data as

information to tasks while the

business needs to run and

manage itself.

2

It is the f nal stage of

data, where it’s life

actually ends. P urg–

ing is the process of

the removal of

unwanted datasets

from an enterprise.

Data Purging

After many rounds of

usage and publica-

tion, data comes near

to its end, where a

brand might want to

archive the data. The

archival of data leads

to the storage of data

in case it is needed in

the future.

Data Archival

W hile making the most of the

data, it might also go outside of

the company. This is known as

data publication, where the

data is sent out to a location

outside the brand.

Data Publication

3

4

7

6

5

It is the compound of analytics

that uses modeling such as risk

modeling, actuarial modeling,

and investment modeling. This

is where the creation of data

values takes place by utilizing

other data as input.

Data Synthesis

When data goes through all the phases of the data lifecycle every day, brands gain various significant insights that would let them make many strategic decisions. Data lifecycle management leads the brands towards the digitization of their business, which allows them to control costs, leverage their data for competitive advantage, and accelerate time to value. 

Thus, a capable data lifecycle management could help you in multiple business operations daily. So, having a robust data lifecycle management software would guarantee the performance of your data and let you gain meaningful insights from it.