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Ways To Automate Data Management With Machine Learning

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Title: Ways To Automate Data Management With Machine Learning


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Ways To Automate Data Management With Machine
Learning
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Automatic And Versatile Cataloging Of Data
Elements
Enterprises are creating huge volumes of
information at breakneck speed. Their databases
contain assets belonging to varied types. This
makes it essential for businesses to have an
organizing system which helps them in storing
their elements efficiently. Data cataloging is an
essential exercise that most organizations use to
sort and categorize their assets. A catalog
contains all the vital details about each element
present in their digital ecosystem. The
information helps in defining each item in a
clear manner and helps users locate the desired
element easily. An asset can be sorted based on a
domain, source, lineage, compliance requirement,
or any other metric chosen by the organization. A
system will need to run through multiple data
sets, columns, sources, etc. to get a clear
understanding of an element and define its
metadata correctly.
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Establish Data Provenance And Record Lineage Of
Assets Information is constantly transformed
at enterprises during various processes and it is
easy to lose track of its origins. The situation
gets complicated with the increasing use of
analytics. In order to get more in-depth
knowledge, businesses are encouraging the
independent use of analytics solutions at all
levels. This gives rise to a unique situation
wherein small data silos are being created where
individual users are storing elements required by
them. These items undergo a transformation and
may not be understood by another stakeholder. It
becomes essential for corporations to establish
the provenance of their assets and efficiently
record the lineage of each item. It will be
difficult for human resources and even
traditional digital systems to create a
consolidated view quickly by going through
various versions. However, machine learning
algorithms can help automate data management by
running through large datasets quickly.
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Data Governance Approach
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Enable Efficient Metadata Management
Organizations are sitting on a huge pile of
versatile data. Even small businesses store
information belonging to various aspects of their
enterprise like clients, vendors, employees,
sales, marketing, etc. On top of that, all the
data is present in a variety of file formats not
restricted to documents only. There can be
spreadsheets, videos, images, and a host of other
file types present at any given moment. Data
management involves providing a consistent
definition of the elements irrespective of their
nature and storage composition, across the
organization. This helps users in easily locating
an item for executing their assigned task.
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6
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