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Phases of Information Lifecycle Management

Information, like the business, goes through various phases in its lifecycle. Since
information is required at every stage of the business, on a daily basis, it
becomes imperative to understand each phase of data lifecycle. For simplicity,
we can divide Information Lifecycle Management into 7 phases starting from

data acquisition to data removal. Yes, it is as important to remove the data once
it is of no use as it is to acquire it. Let us explore these phases in detail:

1. Capturing Data

Data enters the business through data capture. It could be data that’s acquired
from reliable outside resources, data entry and data reception. A business can
collect data from various external sources for research and analysis. This
information is used for comparisons and for predictions. The data generated by
the business has to be input diligently to be processed by the ERP or Business
Intelligence Software. Typically, large volumes of information are generated by
organization on a daily basis in various forms. Information also comes from
various devices, such as mobile phones and IoT enabled devices. IoT is an
outstanding source of reliable data for businesses.

Every business has its own ways of capturing information. It determines the
information that needs to be captured and then determines the methods in
which it can be accomplished. The banking sector inputs the information and
also pulls it from devices such as smartphones, POS swiping machines, ATMs etc.
The telecom sector collects user information from its networks. In Governance, a
lot of information is collected from outside resources apart from those being
input at the offices.

2. Preserving Data

The data that’s captured by the business needs to be stored diligently. It is quite
challenging to store the varied types of bulk information or Big Data that the
business acquires, generates and receives from devices all have to be preserved
to be used later for processing and publishing. Data should be ideally stored in a
categorized way for easier access. Where and how data is being preserved is
important as it determines the accessibility and time to access. Data can be
stored in tiers or in parallel servers to provide faster access. Once the data is
stored, it can then be grouped to ease access.

Where data is stored, it has to be secured. Data security is one of the most
important features to consider from this phase onwards till it is discarded.
Security and privacy of information is essential for businesses to succeed and
sustain. Particularly in the banking, healthcare, telecom and governance sectors,
data security is utmost important.

3. Grouping Data

Data synthesis or grouping data is a comparatively new phase in information
lifecycle. Grouping data lets you quick access to compiled information such as
totals, average, means etc. Many important metrics are formed and stored as
group information which makes further analysis and processing much easier and
faster.

Grouping data is essential when dealing with large volumes of information. When
you have businesses in multiple locations that generate bulk information on a
daily basis, it becomes very difficult for the Head office to handle all this bulk
information. For example, take the telecom, banking, insurance, retail chains andgovernance sectors. There’s always bulk data that’s generated which is grouped
at the end of the day or periodically for convenience.

4. Processing Data

Data that’s collected, categorized, stored and grouped are used to process it to
make it useful. The employee attendance data that’s collected on a daily basis is
used to process payroll. The call details for every customer in the telecom field is
used to analyse the usage and to form better marketing strategies. The banking
industry uses the transaction data and processes it regularly to understand the
transaction patterns and to track the money flow.

Many advanced technologies such as Artificial Intelligence, Business Intelligence,
Enterprise Resource Processing, Intelligent Automation, Robotics, Machine
Learning, Virtual Reality, Artificial Reality etc are used to process information
depending upon the business requirements. Sometimes, the simplest form of
processing such as document management system or database systems also
help processing information. Many businesses use legacy software for data
processing.

5. Publishing Data

The information that’s collected, stored, grouped and processed is used for
publishing as reports to management and public. Every business publishes its
information to its stakeholders including employees, vendors and investors. The
financial stability, external communication and other details are published on
various mediums to reach the right audience at regular intervals.

Many tools are used by businesses for publishing and reporting information. Data
sharing is also a part of publishing as the aim of publishing data is to make it
available for the intended audience. Businesses publishing the financial
statements, offering market insights etc are popular forms of data publishing
that we see on a daily basis.

6. Archiving Data

Data archival is another important aspect of information lifecycle management.
When there’s bulk information being handled on a daily basis or regularly, it
makes storage and processing highly expensive. It slows down data processing
and publishing. To counter this, data is regularly checked and archived.
Archiving is done by creating data subsets. Every set of information archived is
represented by its subset. Archiving stores the information that’s not
immediately used separately from the active data storage environment. This
makes the active data directory more space and makes processing much faster
as lesser information is involved in processing. Data archiving makes data
storage and retrieval more efficient.

7. Removing Data

Data has to be periodically checked and removed when obsolete. Certain
classified information has to be immediately removed from the main data
storage and stored secured in a separate environment specially maintained for
that. Data tends to get obsolete over time. Such obsolete data becomes an
overhead to data storage and processing and hence is carefully sorted and
removed periodically from the data server.

Data entering the business and leaving it may happen over many months or years. Sometimes, a part of the old information is grouped and stored and the base information is removed. This makes sure that the consolidated information is used where required. Data goes through a range of transformations during its
lifecycle in every business. Even though the phases may be named differently,
business information lifecycle management is essential for the business to make
the most of the information it captures and generates.

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