Project Report: Business Analytics Case Studies and Capabilities

This project report provides a comprehensive overview of business analytics, divided into three parts. Part A analyzes case studies of Nest's thermostat and GE Power, examining how big data is applied to solve business problems such as energy wastage and the transition to renewable energy. It discusses the challenges faced, the data used, and recommendations. Part B explores the role of analytics in solving business problems, focusing on descriptive and predictive analytics. Part C reviews Accenture's report on developing and sourcing analytics capabilities, addressing topics like integrating analytics into decision-making, organizing analytics capabilities, sourcing and training talent, and key success factors for becoming analytics-driven. The report concludes with a synthesis of the findings.

Introduction to Business
Analytics

Table of Contents
Introduction......................................................................................................................................3
PART A: Case study analyses.........................................................................................................4
Part B: The Role of Analytics in Solving Business Problems.........................................................6
Part C: Developing and Sourcing Analytics Capabilities................................................................7
Conclusion.......................................................................................................................................9
References......................................................................................................................................10

Introduction
This project report is divided into three parts; A, B and C. In part A; analyses of case studies
named Big Data for consumers and GE Power has been discussed with identification of problems
and solutions through application of big data. Part B; discusses the role of analytics in solving
business problems and Part C; covers short report bases on the review of Accenture’s report. The
project report is ended with conclusion based on finding of the report.

PART A: Case study analyses
a) Nest’s thermostat’s industry and Global energy industry are the one for which analytics has
been applied.
b) In case study one “How Nest uses Big Data in practice”, business problem includes wastage
of huge amount of energy through inefficient home heating systems.
In another case study “GE Power: Big Data, Machine learning and The Internet of Energy” GE is
facing disruption as it transitions from fossils to renewable. The main challenge facing by this
industry is problem in balancing growing demand in developing nations with the need for
sustainability, and predicting the effect of extreme weather conditions on supply and demand.
c) Nest has used Big Data Analytics to collect various information’s to solve business problem
such as location data, sensors collected temperature data, humidity, light levels and motion
details. Additional to this it also gathers data’s about user’s history of temperature adjustments.
The various data’s collected by Nest’s smart thermostat used through conversion it into
automation or auto monitored. For instance, its sensors detect whether the room is empty or
someone is there; and adjust temperature or sometimes off the heating system accordingly to
save electricity.
GE Power also uses Big Data analytics to solve this problem. This model uses various data’s
such as weather data, energy market pricing data, lots of internal and external data to optimize
their business. These data’s are used by GE power in the form of networked model of optimized
infrastructures this includes operations optimization and business optimization models.
d)
Big Data for consumers: As data sets are becoming bigger and more diverse, there is a huge test
to logically connect them. If this is neglected, it will create holes and lead to wrong messages
and experiences. Information analysis is essential to ensure that this large amount of information
is provided in a consistent and useful way. With the incredible concentration of information,
there has been a great deal of interest for big data researchers and big data researchers. It is

important for business associations to hire an information researcher with adaptable skills as the
role of the information researcher is multidisciplinary. Another important test that organizations
are looking into is the lack of experts who see Big Data analytics. There is a severe shortage of
information seekers compared to the large amount of information provided.
Big Data for GE power: It is essential for business associations to obtain important information
from Big Data analysis and it is also important that this data is managed by the relevant office.
An extensive test that looked at groups in the Big Data study successfully captured this large
hole. Unsurprisingly, information improves over time. This basically shows that business
associations have to deal with a lot of information on a daily basis. The amount and range of
information accessible these days can surpass any information engineer and that is why it is seen
as the basis for making information available in a simple and beneficial way for brand owners
and consumers.
e) Recommendations:
Big Data for consumers: Buy-in can be a difficult thing to quantify, especially within different
partner classes. Without full buying from a variety of peers, media and partners, businesses could
easily progress and go further out of nowhere to be ugly, compromised by the latest expectations
and customer loyalty. -use. According to the explanations behind the change, this can cause
venture capitalists a lot of trouble when trying to get things back on track.
Big Data for GE power: Here are some different ways to improve communication with partners,
and it’s important to make them feel part of an organization.
1. Identify what motivates stakeholders
Understanding what drives each participant can make it much easier to get a bigger share of the
participant's purchase. This may sound awkward, but it can help keep the industry afloat.
2. Focus on telling the truth, even when it isn’t what stakeholders wants to hear
Building a culture of trust is essential to get stakeholders committed to expanding and fulfilling
promises as you would expect. In general, ecological factors, for example, low resilience,
negative culture and lack of trust, may lead participants to accept their commitment.

Part B: The Role of Analytics in Solving Business Problems
Business analytics is a solution for the data processing and Data Analytics sub-sets, includes the
use in order to interpret and turn data into usable information, to detect and forecast patterns and
performance, as well as eventually to make informed, data-driven decisions (Kunc and O’brien,
2019). There are a range of analytics which are explained below in such manner in order to solve
issues:
Descriptive analytics -Descriptive analytics interpret empirical data to help explain trends in an
organization. Descriptive analytics define the usage to allow observations with a variety of
historical results (Duan, Cao and Edwards, 2020). The financial pieces that are most frequently
mentioned are a result of descriptive analysis—for example, year-by-year adjustments in rates,
month-by-month increases in revenue, amount of subscribers, or average sales per subscriber.
These measurements all describe what took place over a certain time in an organization.
Descriptive analytics takes raw data and compiles the data to draw results that are usable and
intuitive for administrators, investors. A study showing sales of $1 million may sound amazing,
but it lacks background. This is a problem as this number reflects a drop of 20% month by
month. If that is an improvement of 40% over the course of the year it indicates that the sales
plan is right. However, to provide an honest perspective of the company's success, the broader
context, including influencing economic, is important. Descriptive analytics use a wide variety of
details to offer a clear description of what happens in a firm and how it is distinct from other
similar time spans. This efficiency measures may be used to define success and vulnerability
areas in order to inform management techniques. Descriptive research can be useful in the aspect
of above case to solve challenges and concerns soundly, and most types of analytics can do very
little without a firm understanding. An rational judgment needs to be taken well-understood and
specifically in that particular descriptive analytics
Predictive analytics- The use of metrics and models to assess future success based upon present
and historical data is represented in predictive analytics (Kraus, Feuerriegel and Oztekin, 2020).
Predictive analytics analyze data trends to see if these trends are likely to reappear, and enable
corporations and investors to change where their capital are being spent to take advantage of
potential events. A variety of predictive analytical approaches are available. Data mining, for
example, involves processing large data slices to find patterns. Except for large blocks, the text

review does the same. Predictive models analyze past data in order to evaluate the possibility of
future effects, while qualitative studies examine past data to obtain how a group will respond to a
variety of variables. Insurance firms, for example, evaluate insurance applicants in order to
assess the probability of potential lawsuits on the basis of an existing exposure pool of those
policyholders and previous payout incidents. Marketers see how consumers responded to the
general economy when preparing a new strategy and will use population shifts to assess whether
consumers are encouraged to buy the existing combination of goods.
Part C: Developing and Sourcing Analytics Capabilities
1. How do we best ingrain analytics into our decision-making processes?
The combination of a controversial unstable world, the horrific proliferation of information and
the tension of wanting to stay on the brink of conflict has resulted in zero ties to the use of
control to guide critical business choices. Business analysis allows managers to understand the
elements of their business that anticipate market trends and monitor risks. Instead of "go by your
gut" when caring for inventory, estimating deals or hiring capacity, organizations rely on
effective fact-finding and fact-finding to negotiate options. which develops skills, which brings
risks and benefits into play.
Information and analysis relate to action plans and biological systems. The expansion of new
information collections and the demonstration of enormous information transfer capabilities are
destroying existing data and innovative sources. From using granular information to customize
objects and administrations to scaling computer layers to coordinate buyers and sellers,
organizations use industry analytics to power dynamics faster and with certainty. In fact, the
consideration shows that knowledge-based companies are better suited to critical choices, but
they also value high operational capacity and better customer loyalty and strong levels of profit
and income. A recent study similarly shows that associations that target information are often
linked to secure messages, many times less likely to retain those customers, and many times less
likely to be profitable.
Information is the new oil and the best way for organizations to get there and realize that it is
digitizing their journeys. Digitization of user links can provide data sources, with which

organizations can deal with procedures, agreements, advertising and article development. Sharp,
gritty and granular information can enable organizations to achieve small goals to personalize
their customers and articles and administrations. Further in-house digitization creates
information that administrators can use to improve their responsibilities, including driving and
transportation, asset distribution and retention, range organization, and collection. Similarly,
these models encourage a number of organizations to enter their “Business Intelligence” and
“Research Activity” units on the shared belief of progressive and advanced research. Both
networks are currently using measurable and numerical methods to address critical business
issues and systematize a dynamic system.
2. How do we organize and coordinate analytics capabilities across the organization?
Analysts who collaborate on unified or affiliated collections through the focus of the highest
level of engagement have declared their intention to stay with their groups. In fact, researchers in
unified collections were twice as likely to be specifically affiliated with scattered researchers.
Analysts at the center of magnitude expressed fundamental expectations of staying with their
manager, although those in utilitarian, consulting, and decentralized models cited lower
engagement levels and more fragile goals of staying.
3. How should we source, train and deploy analytics talent?
Analyzing talent is not significantly different from analyzing customer relationships or supply
chain management. It begins with the introduction of historical facts (“What happened?”) and
ends with the constant removal of the capacity dependent on rapidly changing needs types of
analysis for workforce monitoring, from the most difficult to generalize, human resources data,
scientific human resources, human resource profitability analysis, workforce estimates, capacity
assessment model and flexible chain capacity.
4. Discuss the key success factors that underpin and define an organization’s journey toward
becoming analytics driven
Organizations in all industries are increasingly recognizing the value of data-driven business
strategy and operations. Advances in understanding, vision, executive officers and research
enable new activities in key business areas, promising organizations change in a way that has not

been seen in twenty years or more, when the redesign of business measures is underway lead on
how best to increase efficiency and productivity. Of course, information is seen as the new oil
and engine for industrial development and real segregation.
The board must provide a reasonable vision, organize scientific applications, understand profit
margin, allocate appropriate resources, monitor skill, ensure cross-resource coordination, and
eliminate some of the limitations. which certainly come when used. Finally, operators must
request consistency with legal and administrative requirements for information in areas, for
example, safety and security.
Information management programs must be consistent with and guided by ranking objectives,
identifying systems, arrangements, cycles and principles underpinning those objectives.
Associations should review their current status and formulate plans to achieve an appropriate
level of administrative improvement over time. It’s important to recognize that governance is
never complete, it will evolve, just as corporate needs and goals, technology, and legal and
regulatory aspects do.
Conclusion
Big Data Analytics is a tool for improving the security of things to come. The amount of data
that can be collected, coordinated and applied to messages in the typical style of people would
take days, weeks or even a long time to complete. In the free enterprise market, for example,
competition from the United States of America, conflict is a key issue. We can't waste time
collecting data and setting options for events that just happened. Leaving events speechless,
completing visual assignments and separating negotiation sources must happen quickly and
consider chairs / table to decide on the spot. With big data analytics, more informed choices can
be made and the focus can remain on the business activities that go on.

References
Duan, Y., Cao, G. and Edwards, J.S., 2020. Understanding the impact of business analytics on
innovation. European Journal of Operational Research, 281(3), pp.673-686.
Kunc, M. and O’brien, F.A., 2019. The role of business analytics in supporting strategy
processes: Opportunities and limitations. Journal of the Operational Research Society, 70(6),
pp.974-985.
Kraus, M., Feuerriegel, S. and Oztekin, A., 2020. Deep learning in business analytics and
operations research: Models, applications and managerial implications. European Journal of
Operational Research, 281(3), pp.628-641.
Big Data for Consumers: The Internet of Things revolution, 2020; Available through online:
https://www.bernardmarr.com/default.asp?contentID=704
GE Power: Big Data, Machine learning And ‘The Internet of Energy’, 2020; Available through
online: https://www.bernardmarr.com/default.asp?contentID=1266
Accenture report, 2020; Available online through:
https://www.accenture.com/us-en/~/media/accenture/conversion-assets/dotcom/documents/
global/pdf/industries_2/accenture-building-analytics-driven-organization.pdf