Business Analytics

Business Analytics is “the study of data through statistical and operations analysis, the formation of predictive models, application of optimization techniques, and the communication of these results to customers, business partners, and college executives.” Business Analytics requires quantitative methods and evidence-based data for business modeling and decision making; as such, Business Analytics requires the use of Big Data.

Big Data: An Overview

SAS describes Big Data as “a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis.” What’s important to keep in mind about Big Data is that the amount of data is not as important to an organization as the analytics that accompany it. When companies analyze Big Data, they are using Business Analytics to get the insights required for making better business decisions and strategic moves.

Benefits of Data-Driven Decision Making with Business Analytics

Companies use Business Analytics (BA) to make data-driven decisions. The insight gained by BA enables these companies to automate and optimize their business processes. In fact, data-driven companies that utilize Business Analytics achieve a competitive advantage because they are able to use the insights to:

  • Conduct data mining (explore data to find new patterns and relationships)
  • Complete statistical analysis and quantitative analysis to explain why certain results occur
  • Test previous decisions using A/B testing and multivariate testing
  • Make use of predictive modeling and predictive analytics to forecast future results

Business Analytics also provides support for companies in the process of making proactive tactical decisions, and BA makes it possible for those companies to automate decision making in order to support real-time responses.

Challenges with Business analytics

challenges with Business Analytics: there is “a greater potential for privacy invasion, greater financial exposure in fast-moving markets, greater potential for mistaking noise for true insight, and a greater risk of spending lots of money and time chasing poorly defined problems or opportunities.” Other challenges with developing and implementing Business Analytics include…

  • Executive Ownership – Business Analytics requires buy-in from senior leadership and a clear corporate strategy for integrating predictive models
  • IT Involvement – Technology infrastructure and tools must be able to handle the data and Business Analytics processes
  • Available Production Data vs. Cleansed Modeling Data – Watch for technology infrastructure that restrict available data for historical modeling, and know the difference between historical data for model development and real-time data in production
  • Project Management Office (PMO) – The correct project management structure must be in place in order to implement predictive models and adopt an agile approach
  • End user Involvement and Buy-In – End users should be involved in adopting Business Analytics and have a stake in the predictive model
  • Change Management – Organizations should be prepared for the changes that Business Analytics bring to current business and technology operations
  • Explainability vs. the “Perfect Lift” – Balance building precise statistical models with being able to explain the model and how it will produce results

Course Content:

  • Introduction to Statistics and Data Science and its Life Cycle
  • Basic of R and Data Analysis, Data Cleaning and preparing data for analysis in R Language
  • Measures and spread, CLT, Different Types of Test, HT and Tests, Bivariate Analysis, ANOVA
  • Linear Regression
  • Logistic Regression
  • Supervised & Unsupervised Algorithm
  • Market Basket Analysis
  • Time Series Modeling
  • Ridge Regression