Course: Fundamentals of Big Data & Business Analytics
December 2021 Examination
1. General Electric – a literal powerhouse of a corporation involved in virtually every area of
industry, has been laying the foundations of what it grandly calls the Industrial Internet for
some time now. But what exactly is it? Here’s a basic overview of the ideas which they are
hoping will transform industry, and how it’s all built around big data. A simple way to think
of the industrial internet is as a subset of that, which includes all the data-gathering,
communicating and analysis done in industry. In essence, the idea is that all the separate
machines and tools which make an industry possible will be “smart” – connected, dataenabled
and constantly reporting their status to each other in ways as creative as their
engineers and data scientists can devise.
This will increase efficiency by allowing every aspect of an industrial operation to be
monitored and tweaked for optimal performance and reduce down-time – machinery will
break down less often if we know exactly the best time to replace a worn part. Data is behind
this transformation, specifically the new tools that technology is giving us to record and
analyse every aspect of a machine’s operation. And GE is certainly not data poor – according to
Wikipedia, its 2005 tax return extended across 24,000 pages when printed out. And
pioneering is deeply engrained in its corporate culture – being established by Thomas Edison,
as well as being the first private company in the world to own its own computer system, in
the 1960s. So of all the industrial giants of the pre-online world, it isn’t surprising that they are
blazing a trail into the brave new world of big data. GE generates power at its plants which is
used to drive the manufacturing that goes on in its factories, and its financial divisions enable
the multi-million transactions involved when they are bought and sold. With fingers in this
many pies, it’s clearly in the position to generate, analyse and act on a great deal of data.
Sensors embedded in their power turbines, jet engines and hospital scanners will collect the
data – it’s estimated that one typical gas turbine will generate
500Gb of data every day. And if that data can be used to improve efficiency by just 1%
across five of their key sectors that they sell to, those sectors stand to make combined
savings of $300 billion. With those kinds of savings within sight, it isn’t surprising that GE is
investing heavily. In 2012 they announced $1 billion was being invested over four years in
their state-of-the-art analytics centre in San Ramon, California, to attract pioneering data
talent to lay the software foundations of the Industrial Internet.
a) State and explain how this generated data is leveraged to enable growth in manufacturing
sector for GE. Provide example of 2 business questions using the data and potential analytics
approach.
b) What is the end-to-end big data architecture required in this context? Show it preferably
using a diagram/ flowchart. You can explain possible tools which can be leveraged in the
life cycle and the rationale for the tool. (10 Marks)
2. Explain how clouds have increasingly transformed the adoption of big data in the
companies. You must mention 3 examples of business cases which could transform their
business. What choices one needs to make to improve cost optimization while using the
cloud-based platform. (10 Marks)
3. Indian Banking Industry is facing fraud related issues for the past few years. Indian Banks,
especially Public Sector banks are suffering from mounting losses and rise in NPAs on
account of increased level of number of frauds. Loan sanctioning for new projects, as
happened in recent case of Nirav Modi, have brought sufferings for the industry. Many of
the top executives have been charged for alleged corruption and deceitful intentions in
granting loans. This creates a question mark on corporate governance and ethics in the
industry. In fact, the menace of rising NPA is a global crisis that is responsible for slowdown
in industry. The strength of financial system of any economy can be judged by its level of
production and consumption. The living standard and status of people can be judged with
soundness of financial system; but if financial system is packed with frauds and high degree
of NPAs, it should be a cause of worry for any nation.
Loan loss- The risk of increasing NPA and Bad debts in India is increased manifold, due to
lack of appropriate methods of monitoring of loans and not by proper due diligence.
a. State what approach can help achieve controlling bad debts and NPAs due to loan loss? How
can each BI and BA help solve this problem? (5 Marks)
b. How can predictive analytics be enabled in this case? Illustrate the solution detail. (5 Marks)
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