Introduction to business analytics and historical overview

1. Introduction to business analytics
Defination
 ”Business analytics refers to the skills, technolo- gies, practices for continuous
iterative exploration and investigation of past business performance to gain insight
and drive business planning.” In short,
 BA is a rational, fact-based approach to decision making.

2.  These facts come from data, therefore BA is about the science and the
skills to turn data into decisions.
 The science is mostly statistics, artificial intelligence (data mining and
machine learning), and optimization; the skills are computer skills, com-
munication skills, project and change management, etc.
 It should be clear that BA by itself is not a science. It is the total set of
knowledge that is required to solve business problems in a rational way.
 To be a successful business analyst, experience in BA projects and
knowledge of the business areas that the data comes from (such as
healthcare, advertis- ing, finance) is also very valuable.

3. Business analytics is sub divided
into three consecutive activities:
 descriptive ana- lytics,
 predictive analytics, and
 prescriptive analytics.
 During the descriptive phase, data is analyzed and patterns are found.

4.  Predictive phase : The insights are conse-quently used in the predictive phase to
predict what is likely to happen in the future, if the situation remains the same.
 Finally, in the prescriptive phase, alternative decisions are determined that
change the situation and which will lead to desirable outcomes.

5.  Analytics can only start when there is data.
 Certain organizations al- ready have a centralized data warehouse in which
relevant current and
his-torical data is stored for the purpose of reporting and analytics.
Setting up such a data warehouse and maintaining it is part of the business
intelligence(BI) strategy of a company.
 However, not all companies have such a central- ized database, and even when
it exists it rarely contains all the informationrequired for a certain analysis.

6.  Therefore, data often needs to be collected, cleansed and combined with other
sources. Data collection, cleansing and further pre-processing is usually a very
time-consuming task, often takingmore time than the actual analysis.
 data collection and pre-processing are always the first stepsof a BA project.
 Following the data collection and pre-processing
 the real data science steps begin with descriptive analytics.
 BA project does not end with prescriptive analytics,
1. e., with generating an (optimal) decision. The decision has to be implemented,
which requires various skills, such as knowledge of change management.

8.  The model above suggests a linear process, but in
practice this is rarely the case.
 At many of the steps, depending on the outcome, you
might re- visit earlier steps. For example, if the predictions
are not accurate enough for a particular aaplication
 then you might collect extra data to improvethem.
 Furthermore, not all BA projects include prescriptive
analytics, many projects have insight or prediction as goal
and therefore finish after the de- scriptive or predictive
steps.

9. Historical overview
 Business analytics combines techniques from different fields all
originating from their own academic background.
 We will touch upon the main con-stituent fields
 statistics,
 artificial intelligence,
 operations research, and also
 BA and data science (DS).


10. Statistics
Statistics is a mathematical discipline with a large body of
knowledge developed in the pre-computer age. Eg: mean median
mode frequencies etc..
The central bodyof knowledge concerns the behavior of statistical
quantities in limiting sit- uations,

11. Artificial intelligence (AI)
Artificial intelligence (AI) is a field within computer
science that grew rapidly from the 1970s with the
advent of computers.
 Initial expectations were highly inflated.
 One believed, that so-called expert systems would
soon replace doctors in their work of diag-nosing
illnesses in patients.

12.  This did not happen and the attention for AI
diminished.
 Today, the expectations are high again, largely
due the fields of data mining and machine
learning

13. contdd
 They devel-oped more recently when large data sets
became available for analysis.
 Both fields of data mining and machine learning focus
on learning from data and making predictions using
what is learned.
 Machine learning focuses on predictive models, data
mining more broadly on the process from datapre-
processing to predictive analytics, with a focus on
data-driven methods.

14. Contdd..
 The difference between statistics and machine
learning are their origins and the more data-
oriented approach of ML:
 Mathematicians want to prove the- oretically
that things work, computer scientists want to
show it using data.

15. Operations research (OR)
 Operations research (OR) is about the application of
mathematical opti- mization to decision making
problems in organizations.
 OR, (scientific study of the management ), also raised
big expectations, in the 1950s, following the first
successes of the allied forces of OR being applied during
World War II.

16.  However, the impact at the strategic decision
level remained very limited and OR appli- cations
are mainly found at the operational level. Quite
often the application of OR would be to a
logistical problem such as the routing of delivery
vans, outside the scope of higher management.

17. Business analytics
 BA on the other hand, developed in organizations that
realized that their data was not just valuable for their
current operations,
 but also to gain in- sight and improve their processes.
 Starting in the 1990’s, we saw more and more analysts
working with data in organizations.

18. An important difference with OR is that many executives do
understand the value of analytics and adopt a company-wide BA
strategy.
The fact is that the availability of data, computers and
software made the widespread use of BA possible.
Finally, BA methods— also the ones originating from the
mathematical sciences—are used on a huge scale in
companies, institutions and research centers, offering count-
less opportunities for business analysts and data scientists.

19. Evolution of Business Analytics
 Today, business analytics has become a buzzword
for companies around the globe.
 Every business, irrespective of its size, is on a
lookout for different ways to make sense of the vast
amount of raw data available.

20.  This is because business analytics has been
transforming the way companies function for over a
decade now.
 From targeting the right customers and increasing
sales
 Its helps HR personnel select the right candidates
and reducing overhead costs;

21. How Business
Analytics Has Evolved
Over the Years

22. BA in the 1800sThe need to stay
ahead
 The first use of data to stay ahead of his competitors dates
back to 1865.
 During this time, Mr Richard Miller Devens described in his
book
 how Sir Henry Furnese, a banker, was always one step
ahead by actively gathering information and acting on it before
any of his competitors.
 This makes it clear that professionals such as Sir Furnese
relied more on data and empirical evidence, rather than gut
instinct.

23. BA in the late 1800s
The Advent of Scientific Management
 During this time,
 Frederick Taylor introduced the first-ever
system of business analytics in the United
States of America, and he called it scientific
management.
 The purpose of this system was to analyze the
production techniques and labourers body
movements in production unit to identify
greater efficiencies.

24. BA in the early 1900s
The Transformation of the Manufacturing Industry
 Frederick Taylors scientific management system inspired Henry Ford,
 who hired Taylor as his consultant. Ford was willing to measure the time
each component of his Ford Model took to complete on his assembly line.
 Scientific calculation of the work
 This analysis transformed his work and the manufacturing industry
across the globe.

25. BA in the late 1900s
The Emergence of Business
Intelligence
 Owing to the lower prices for storage space and better
databases,
 the next generation of business intelligence solutions
was all set to step in.
 By now, there was a considerable amount of data
available but not a centralized place to store it.
 To address this problem, Ralph Kimball and Bill Inmon
proposed similar strategies to build data warehouses
(DW)

26. BA in the new Millenium
Availability of different analytical solutions
 By this time, medium and large-sized businesses had already realized the
value of business intelligence solutions.
 Companies such as IBM, Microsoft, SAP, and Oracle were at the forefront
of offering such solutions to change the way businesses function.

27. BA in 2005
Accessibility of Data for the Common People
 Considering the extensive usage of data, companies started directing their
efforts on improving the speed at which the information was available.
 New business analytics tools were introduced to ensure technical as well as
non-technical people were able to mine the data and gain insights.


28.  Around this time, the increasing interconnectivity of the business
world led to the need for real-time information.
 This was when Google Analytics was introduced. Google wanted to
provide a free and accessible way for users to analyze their website
data.

29. BA from 2005 to 2020
The Bread and Butter for Companies
globally
 With the internet available to almost everyone and the increasing data,
companies needed better solutions to store and analyze all the
information.
 Building computers with more storage capacity and better speed wasnt
possible for many,
 so companies resorted to using several machines at the same time. This
was the beginning of cloud computing.


30.  Since the last decade, big data, cloud computing, and business analytics
have become integral for almost all companies.
 The new advancements have made these technologies even better. Now,
data analytics and science are known to be the future. From advertising
and marketing to recruiting and planning operational activities, these
terms are tossed around in every field.