What is Big Data Analytics | How Big Data Analytics are Implemented
Big Data Analytics
A. Big Data
Analytics: What it is
Big data analytics is the process
of examining large and varied data sets to uncover hidden patterns, unknown
correlations, market trends, customer preferences and other useful information
that can help organizations make more-informed enhanced insight and business
decisions.
There
are four different types of Big data analytics tools:
Ø Descriptive Analytics: These tools create
simple reports and visualizations that show what occurred at a particular point
in time or over a period of time. These are the least advanced analytics tools.
Ø Diagnostic Analytics: Diagnostic tools are
more advanced than descriptive reporting tools. It is allow analysts to dive
deep into the data and determine root causes for a given situation.
Ø Predictive Analytics: Predictive analytics
tools use highly advanced algorithms to forecast what might happen next. Often
these tools make use of artificial intelligence and machine learning
technology.
Ø Prescriptive Analytics: Prescriptive
analytics tell organizations what they should do in order to achieve a desired
result. These tools require very advanced machine learning capabilities, and
few solutions on the market today offer true prescriptive capabilities.
B. How Big Data
Analytics is implemented presently across various industries:
Primary goal for most of the
enterprises is enhance various business benefits through big data analytics,
including new revenue opportunities, more effective marketing, better customer
service, improved operational efficiency and competitive advantages.
Key industries using big data
analytics:
Industries
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Application
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Banking and
Securities
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Stock Exchange
Authorities or Banking Authorities are using big data analytics to ensure
that no illegal trading happens by monitoring the stock market/finance market.
Big
banks, hedge funds and other financial institutions uses big data for trade
analytics used in high frequency trading, pre-trade decision-support
analytics, sentiment measurement, Predictive Analytics etc.
This
industry also heavily relies on big data for risk analytics including;
anti-money laundering, demand enterprise risk management, "Know Your
Customer", and fraud mitigation.
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Communications
and Media
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Since
consumers expect rich media on-demand in different formats and in a variety
of devices, some big data challenges in the communications, media and
entertainment industry include:
·
Collecting, analyzing, and utilizing
consumer insights
·
Leveraging mobile and social media content
·
Understanding patterns of real-time, media
content usage
Organizations
in this industry simultaneously analyze customer data along with behavioural
data to create detailed customer profiles that can be used to:
·
Create content for different target
audiences
·
Recommend content on demand
·
Measure content performance
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Healthcare
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In
healthcare, large amounts of medical data have become available in various
healthcare organizations (providers, pharmaceuticals etc.). This data could
be an enabling resource for deriving insights for improving care delivery and
reducing waste. The enormity and complexity of these datasets present great
challenges in analyses and subsequent applications to a practical clinical
environment.
Using
public health data for faster responses to individual health problems and
identify the global spread of new virus. Health Ministries of different
countries incorporate big data analytic tools to make proper use of data
collected after Census and surveys.
|
Education
|
Big data
in education used to measure teacher’s effectiveness to ensure a good
experience for both students and teachers. Teacher’s performance can be
fine-tuned and measured against student numbers, subject matter, student
demographics, student aspirations, behavioural classification and several
other variables.
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Manufacturing
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To
increase productivity by using big data to enhance supply chain management.
Manufacturing companies use these analytical tools to ensure that are
allocating the resources of production in an optimum manner which yields the
maximum benefit.
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Insurance
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For everything from developing new products to
handling claims through predictive analytics. Insurance companies use
business big data to keep a track of the scheme of policy which is the most
in demand and is generating the most revenue.
When it comes to claims management, predictive
analytics from big data has been used to offer faster service since massive
amounts of data can be analyzed especially in the underwriting stage. Fraud
detection has also been enhanced.
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Government
|
The public sector generates a huge amount of data
in transactions, employment, education, manufacturing, and agriculture, to
name a few. Big data analytics applications can significantly help the
government to achieve efficiencies, combat fraud, bring transparency, foster
the economy, and spike productivity and growth.
Using big data analytics the security agencies and
police can analyze the disparate sources and respond to crime, attacks, and other
such situations in the country.
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Retail and
Whole sale trade
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Big data from customer loyalty data, POS, store
inventory, local demographics data continues to be gathered by retail and
wholesale stores. Retail and wholesale traders can utilize big data for
analytics and for other uses including:
·
Optimized staffing through data from
shopping patterns, local events, and so on
·
Reduced fraud
·
Timely analysis of inventory
·
Customer loyalty
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Transportation
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For better route planning, traffic monitoring and
management, and logistics. This is mainly incorporated by governments to
avoid congestion of traffic in a single place.
Applications of big data analytics by governments,
private organizations and individuals include:
·
Governments use of big data: traffic control,
route planning, intelligent transport systems, congestion management (by
predicting traffic conditions)
·
Private sector use of big data in
transport: revenue management, technological enhancements, logistics and for
competitive advantage (by consolidating shipments and optimizing freight
movement)
·
Individual use of big data includes:
route planning to save on fuel and time, for travel arrangements in tourism
etc.
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Energy and
Utilities
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By introducing smart meters to reduce electrical
leakages and help users to manage their energy usage. Load dispatch centers
are using big data analysis to monitor the load patterns and discern the
differences between the trends of energy consumption based on different
parameters and as a way to incorporate daylight savings.
In utility companies the use of big data also
allows for better asset and workforce management which is useful for
recognizing errors and correcting them as soon as possible before complete
failure is experienced.
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