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Understanding Corporate Life Cycle and Data

Data Lifecycle Management, also called as DLM can be defined as the set of processes used to administer the data of the company from its definition to its withdrawal. It is a series of steps that the corporates apply in order to create, maintain and access their data.

Steps in Corporate Data Lifecycle Management

Corporate Data Lifecycle Management is conducted in a series of steps. There are essentially six steps:

Creation or collection of data

This is about creation of the data. For instance, in a bank, the data could be customer usage data which is created over time by the usage of the net banking and mobile banking systems by the customers. Another example could be of flight ticket booking data which is created as and when the customers book more and more tickets via the airline’s online ticket booking portal. The created data is stored in large data centered that are powered by hundreds or even thousands of servers. With the progress in the cloud support from providers like Amazon AWS, Microsoft Azure, etc, more and more organizations are now moving to clouds. This helps in reducing the costs significantly because now, corporates don’t have to maintain their own infrastructure.

Processing data

The stored data is processed, often in order to convert it into a format that could be understood by analytics systems. For instance, banks may want to run a data-cleaning algorithm on their data. This generally involves filling on unavailable data. For instance, if the age value is missing for some bank account, it may be filled by the median value of age so as to make a good guess.

Data analysis

Data analysis involves extraction of meaningful information from the data. For instance, analyzing a bank’s data may provide useful insights about consumer behavior. As an example, an analysis may show that there are certain regions where loan defaults are highest. This helps the bank in building stricter guidelines before giving out loans in that particular area. Talking from a tech perspective, data analysis algorithms involve various technologies like hadoop, map-reduce, parallel computing, etc.

Preservation of data

Data storage involves storing the data securely in data-centers so as to keep a copy of it. This is required in order to maintain the logs of the organization. As mentioned above, large cloud systems are used by organizations in order to store the data.

Access to data (giving access to data or data retrieval)

This involves development of a secured, access controlled interface to access the data. This is generally achieved by building scalable web applications around the database layer which provide the users an application level access to data in a controlled manner using several APIs (Application Programming Interfaces).

Reusing data

Past data may be very well needed in order to retrieve past information. This leads to reusing of old data. Further, the organization may want to get insights from past data in order to build certain data models based on Machine Learning and Artificial Intelligence. This also requires reuse of data.


Using the above steps of corporate data lifecycle management, the corporates administer their data. This is important since data is important for the corporates for any decision making process, which in turn can affect their business.

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