NMIMS
Global Access
School for Continuing Education (NGA-SCE)
Course: Fundamentals of Big Data & Business Analytics
Internal Assignment Applicable for April 2022 Examination
1. On
the path towards industrial and social progress Tata Power-DDL has always been
a front runner in introducing reformative solutions such as Smart Grid
Operations, Automatic Meter Reading, etc. to the power segment in North &
North-West Delhi. Among other challenges, revenue leakage and power theft were
causing a roadblock in Tata Power-DDL’s goal to optimize the power supply for
consumers at reduced tariffs.By digitalizing the power distribution systems,
Tata Power – DDL opened the doorway to a vast amount of information that was
heterogenous and unstructured. With an aim to enhance decision-making and
optimize entire utility ecosystem, Tata Power-DDL’s outlook was to employ
advanced digital technology like Big Data analytics.
Tata Power-DDL partnered with Hitachi Systems Micro Clinic to leverage their IT
X OT expertise for social enhancement. Collaborating with Tata Power-DDL,
Hitachi designed a holistic blueprint for the implementation of end-to-end
advanced data analysis solutions; deploying world-class technologies to
streamline data ingestion from diverse platforms, systematize scheduling of
data and execute data engineering on big data along with swift advanced
analytics.
By creating an advanced and reliable system architecture for big data analytics
using IT X OT, Hitachi provided Tata Power-DDL with an operational advantage by
focusing on:
IT integration objectives·
Solution modifications·
Speedy execution by using efficient
operation technology and result optimization·
Power Operation Technology·
Thus, improving operational efficiency and accelerating the delivery of true
value to the society by curbing power losses and reducing tariffs for
consumers.
a. In
this case, how Big data analytics will enable prevention of revenue leakage in
power sector. Which tools can be leveraged for data ingestion, scheduling as
well as final operationalization of analytics?
b. How
is distributed computing different from parallel computing? Use this context to
explain the difference.
c.
Which analytics methodologies can be used to analyse the business problems
mentioned in the case? Which business metrics will be useful to track the
possible fallacy in meter reading?
(10 Marks)
2.
State 3 use-cases of business analytics within the banking industry, highlighting
usage of descriptive, predictive, and prescriptive analytics. Give an example
of how mobile analytics is relevant for the industry and the resultant impact
vs. the traditional banking systems.
(10 Marks)
3.a.
Explain how prescriptive analytics has increasingly been adopted along with big
data in the companies. You can also mention the relevant stakeholders in the
business who are needed to make this a success. (5 Marks)
3.b.
Mention 2 business examples of prescriptive analytics which are fueled by the
Big data and Mobile Analytics revolution with the necessary context and
methodology. (5 Marks)
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