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AIS ELEC 1 (1) - Introduction to Business Analytics
BS Secondary Education (DRRR 01)
Cotabato State University
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Central Mindanao Colleges
Osmena Drive, Kidapawan City
College of Accountancy and Business
Management Education
MODULE
In
Business Analytics
(AIS ELEC 1)
CHAPTER 1 – INTRODUCTION TO BUSINESS
ANALYTICS
References: The History of the Evolution of Business Analytics by Café University of the Philippines Open University Fundamentals of Business Analytics “A Business Analytics Course” by Asst. Professor Myra Almodiel and Dr. Primo G. Garcia
compiled by:
Mr. Ariel L. Sulog, CPA
College Instructor
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Discuss the basic concepts on business intelligence, big data and business analytics.
Trace the evolution of business analytics; and
Give examples of big data service providers.
In the era of knowledge economy, getting the right information to decision makers at the
right time is critical to their business success, and one such attempt includes the growing
use of business analytics (Min, 2017). Business analytics is one of the most talked-about
topics in the field of business and information technology. And as expected, business
analytics is becoming one of the most sought-after courses in the academe.
LEARNING OBJECTIVES
BUSINESS ANALYTICS AND BUSINESS
INTELLIGENCE
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From ancient times, people have had this need to predict the future. They have used crude methods like stones and sticks to project how much crops they were going to harvest or how much they were going to yield. In this section, we shall trace the development of business analytics from the ancient to the modern time.
Business Analytics in a Barter Economy
Let us take a moment and go way back if only for the sake of giving ourselves a baseline example. Imagine how one might have tracked the first barter system without access to pencils and paper, let alone computers. Numerical markings on cave- dwelling walls or with wood and stones are our first glimpse at the evolution of business analytics. A tracking system of sorts would have been needed to trace who had what and when.
This is an obvious oversimplification, but from this example, we can better understand how and why business analytics evolved as industry expanded.
Business Analytics in the Industrial Era
The industrial revolution which began in the mid- to late-1700s brought with it new manufacturing processes with water and steam followed soon after by railroads, steel, and oil. These are complex industries that quickly grew out of their local storefronts into nation-wide companies.
During the late-1800s Frederick W. Taylor introduced the first formalized system of business analytics in the United States. Taylor’s System of Scientific Management began with time studies that analyzed production techniques and laborer’s body movements to find greater efficiencies that ultimately boosted industrial production. Taylor acted as a consultant to Henry Ford and directly influenced Ford’s car assembly line time measurements.
In the early 1900s, Ford measured the time each component of his Ford Model T took to develop through completion on his assembly line. Perhaps a seemingly simple task, but Ford singlehandedly revolutionized not only the automobile industry, but manufacturing world-wide.
It is safe to say that the earlier days in the evolution business analytics focused mostly on improving production, its efficiencies, quantities, and cost-effectiveness.
Operational Reporting
ORIGIN OF BUSINESS ANALYTICS
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Operational reporting still functions in most of today’s businesses as a day-to-day summary of what is happening now. But leading up to the digital and informational ages in the late 1900s, operational reporting rued the day as highly segmented workflow analytics.
This means information was gathered and saved, but typically housed in informational silos that weren’t easily shared company wide. It is not that this was anyone’s specific intention, but it was a tremendous challenge to update and share, for example, a handwritten ledger that analyzed the company’s daily reports. Operational reporting resulted in very little integration and low to zero historical data. Organizationally speaking, the challenge of sharing information was great. The bigger the business, the more challenging the data collection process.
Business Analytics in the Digital & Information Ages
Behold the 1970s when computers began to be in regular use at larger corporations. Business analytics in this era were headed by Decision Support Systems (DSS). DSSs grew in popularity as they helped to sort and filter larger quantities of data that assisted executives in data-driven business decision-making. DSS systems helped collate data from various areas of business, for example production and sales, to give key decision-makers a bird’s eye perspective of business in a way that hadn’t really existed before. Examining different slices of data through filtering processes was a game-changing experience in the world of business innovation.
DSS analytics tools are typically driven by the following process:
Automated Inputs → User Inputs → Outputs → Results
Computers continued to boom throughout the 1980s and into the 1990s (and certainly beyond) in what is commonly called the Information Age. A big part of this information was initially historical information. With the technology boom of the Information Age comes a tremendous increase in information storage capacity.
Suddenly, data warehouses could save historical computer data (market trends, growth, pricing) gathered over time and prepped for data analysis. Earnings and operations report became a regularly accepted way to understand businesses and began to fuel business dealings, investments, and decision-making.
Meet Microsoft Excel
Microsoft Excel built upon this DDS-type platform and introduced its still increasingly popular software in 1985. Excel enables its users to not only sort and filter data, but to program formulas that splice and display the data as specifically instructed. Long gone where the days of handwritten ledgers once Microsoft Excel spreadsheets entered the scene.
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cloud data drawn from a tremendous user base, big data is groundbreaking in its ability to move the evolution of business analytics forward.
Predictive analytics: Based on past trends, predictive analytics looks to big data collected over time to predict future actions.
Automated analytics: Automated analytics are analytics that ultimately require very few to zero manual inputs. Data is automatically analyzed in ways that optimize business systems.
Analytics are still not easy to manage. But dashboards like Cyfe have certainly improved the way we view, digest, and make sense of the information. Either way, analytics are essential, perhaps now more than ever as business and technology continue to grow at an exponential rate.
The reality is that we live in a world today where Data Scientists and Chief Analytics Officers (CAOs) are common and blossoming career paths. Business Analytics is even a degree program at many schools.
The evolution of business analytics will continue to evolve as it has done so throughout the ages. Perhaps what we currently deem the future of business analytics will one day soon be as obsolete as tracking sales with sticks and stones, but in the meantime, let us agree to appreciate the technology we have and use it to make the best possible business decisions we can.
Big Data and Business Analytics
We have already defined Business Analytics and Business Intelligence. This time, let us learn another concept related to BA which is the Big Data. So, what is big data? SAS defines big data as a term that describes the large volume of structured and unstructured data which can be analyzed for insights needed for better decisions and strategic business moves. IBM, on the other hand, refers to it as data sets whose size or type is beyond the ability of traditional relational databases to capture, manage, and process the data with low-latency.
What is the difference? Business Analytics is said to focus on financial and operational analytics of the business while big data involved machine automation to analyze data. The importance of big data is not on how much data you have, but what you do with those data.
There are four aspects that define big data which are volume, variety, velocity and veracity.
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Volume is about how huge the data sets are.
Variety includes how many pieces of data we gather together from social media data, government data, financial data, banking data, all sorts of transactions all combined together to make one or more profiles for your customers.
Velocity is the speed of data.
Veracity means that there is a lot of uncertainty, meaning, there is all these different data coming together, but the problem is we don’t know what to do with them.
Assessment
After learning about the different definitions of BA and BI, can you now compare/differentiate BI and BA?
Galleto (2018) mentioned that "While Business Intelligence answers what happened, Business Analytics answers why it happened and whether it will happen again". What are your thoughts on this?
AIS ELEC 1 (1) - Introduction to Business Analytics
Course: BS Secondary Education (DRRR 01)
University: Cotabato State University
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