Big Data Analytics in Business

This article discusses the benefits and challenges of using big data analytics in business. It covers topics such as customer acquisition and retention, marketing insights, risk management, and various techniques for analyzing big data. The article also provides references for further reading.

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Big data refers to a field which provide systematic
extraction of information from a number of data sets
that are too large and complex to be dealt with
traditional ways of data processing. It is often seen that
the data that have a number of cases a rose with great
statistical power and high complexity a leading towards
false discovery rate. Big data helps in including the
challenge of capturing data, storing data, analysing data,
sharing data, transferring data, updating data,
visualizing data and soon.
These are all the sources which produce different data which are to be
collected, stored as well as analyse successfully. Along with this the
data scientist as well as analyst or not only limiting their job to
collecting data from one source but there are a number of different
sources which are providing data.
Velocity: When considering the amount of data its volume and
the variety there is consistent flow of data. This gives the birth to 3rd
characteristic that is velocity . Velocity of data means that more data is
available on certain days and due to this the velocity of data analysis is
also required to be high. There are data professionals who gather data
over time and the date at the end of week or a month or quarter or rather
hide and that at other time of days. Due to this velocity is a major
characteristic of big data which is to be analyse successfully.
Veracity: Voracity refers to the accuracy, quality as well as
trustworthiness of the data that is collected. The reliability of data needs
to be distinctive in order to make sure that the data which is collected is
accurate and conclusions can be drawn from it. It is often required to
understand the valuable sources of information from which the big data
can be analysed successfully. When the veracity of data is low it is often
estimated that bad decisions can be evaluated and drawn due to the data.
Information Systems and Big
INTRODUCTION Marketing and Finance Department
The characteristics of big data can be characterized into
four components as mentioned below:
Volume: Volume is the first and major
characteristic of big data which makes the dataset big in
its size which is to be evaluated. The date upsets are
usually stretch to petabytes and exabytes. There are
huge volumes of powerful data present and powerful
processing techniques are required in order to assess this
data.
Variety: The variety which is present in big data is very
high. Examples can be taken of different email, CRM
system, mobile data as well as Google advertisements
that are included in different data sets.

Big Data Analytics
Big data analytics refers to the specialized
analytic software in high-powered computing system
which is enabling scientist and analytics to use the
volume of structured as well as unstructured data in
order to draw conclusions from it for business
benefits. These benefits can be the revenue
opportunities, improving operational efficiency as
well as effectiveness in marketing campaigns in a
business.
Various techniques for analysing big data
Association rule learning
Classification tree analyses
Genetic algorithms
Machine learning
Big Data in business
There are a number of technologies through which organisations are
using big data in order to bring benefits for them. Some of such use of big
data is mentioned below:
To boost customer acquisition and retention: Data is successfully
allowing businesses to observe their customers and understand the patterns
and trends. This helps in triggering loyalty within the customers.
To solve advertisers problem and offer marketing insights: The
marketing and advertising technology sector is now effectively using big
data analysis in order to understand the online activities and monitor the
point of sale transactions so that they can effectively generate more targeted
campaigns for their consumers.
Risk management: There are high-risk business environments which
are requiring risk management processes. A risk management plan is
investment for business necessary regardless of whichever sector it belongs
to. Big data analytics contribute greatly towards development of different
risk management solutions. The tools are allowing business to quantify and
also model the risk they are facing every day.
Challenges of big data analytics
Need for synchronization through different
data sources:
Shortage of professionals
Generating meaningful insights
Getting voluminous data into big data
platform
Data storage and quality:
Security and privacy of data:
References
Choi, T.M., Wallace, S.W. and Wang, Y., 2018. Big data analytics in operations management. Production and Operations Management, 27(10), pp.1868-1883.
Ghani, N.A., Hamid, S. and Ahmed, E., 2019. Social media big data analytics: A survey. Computers in Human Behavior, 101, pp.417-428.
Tiwari, S., Wee, H.M. and Daryanto, Y., 2018. Big data analytics in supply chain management between 2010 and 2016: Insights to industries. Computers & Industrial
Engineering, 115, pp.319-330.
Hwang, K. and Chen, M., 2017. Big-data analytics for cloud, IoT and cognitive computing. John Wiley & Sons.