Dear Readers, recall my introductory blog on Data Analytics course titled “Data Analysis – Tool for Business Executives” where I summarized the learnings from Prof. Bongo Adi, the Data Analytics facilitator at the ongoing Lagos Business School (LBS) Executive Master of Business Administration (EMBA) program. The blog referenced summarized the important role Probability concept plays in decision making supporting managers with information to determine likelihood of occurrence of an event thereby enabling appropriate plan to ensure business entity maximize or minimize the impact of such event occurrence depending on the event impact on the business entity (positive or negative). The entire article will be a good refresh to the summary of the entire Data Analytics I classroom sessions (22) that I am about to present.
With further sessions with Dr. Francis Okoye, revising the concept of Probability Distribution (PD) either via Discreet (Binomial or Poisson) PD or Continuous (Uniform or Normal or Exponential) PD. With fundamentals settled, we progressed into Bayes Theorem, Decision Tree, and Forecasting with Time series.
Hype the learning further, the facilitator introduced the Executives to world of Correlation, Linear Programming, from Simple Linear Programming to Multiple Linear Programming and relationship with Regression and Sensitivity Analysis. Forecasting and Measures of Probability with Time Value of Money capped the entire Data Analytics I sojourn.
Wait a minute! Are the classes all about mathematical formular and probability distribution graphs and symbols? Where is the business (B) and the administration (A) in the MBA or is it another Statistics or Mathematics class of undergraduate program?
Nay! The classed practicalized the use of each of the studied concept in real business day-day activities. Data Analytics is becoming an integral part of 21st century business suite because “it allows leadership to create evidence-based strategy, understand customers to better target marketing initiatives, and increase overall productivity”[1]. In a competitive business environment of today, companies are seeking continuously for areas to have leverage over competitors and offer consumer the reason why they should be patronized. The leverage maybe in form of distinct variety, cost for efficient service delivery, lower cost of production thereby translating affordable competitive cost offer to consumers and increase sales/revenue. All these objectives can be achieved faster and efficiently when data is used rightly to support the management decisions to face the global business.
The Data Analytics I also emphasized the need to separate Data Science from Business Data Analytics. Even though both study areas focus of mining data into meaningful entity, however, “business data analytics is concerned with extracting meaningful insights from and visualizing data to facilitate the decision-making process, whereas data science is focused on making sense of raw data using algorithms, statistical models, and computer programming”[2].
With completion of Data Analytics I which focused on descriptive analytics revealing what has already happened using data and introducing the participants to predictive analytics with forecasting, sensitivity analysis to explain what could happen and correlation with regression analysis to advise on what should happen in future for a business entity whose data is being analyzed[3], it is clear that the Data Analytics is not only for businesses but for individuals who want to be heard and influential in decision making because everyone can have an opinion but the person with opinion backed with data will influence the final decision.
[1] 5 key reasons why data analytics is important to business | Penn LPS Online (upenn.edu)
[2] Business Analytics: What It Is & Why It’s Important | HBS Online
[3] Importance of Business Analytics | Benefits and Types of Business Analytics | Exeed College
Financial Analysis – A view from the Liquidity lens