Business Analytics PDF

Business Analytics  Analytics is a field which combines following into one  Data  Information Technology  Statistical Analytics  Quantitative Methods  Computer-Based Tools  These all are combined to provide a decision makers all possible scenar...

Business Analytics  Analytics is a field which combines following into one  Data  Information Technology  Statistical Analytics  Quantitative Methods  Computer-Based Tools  These all are combined to provide a decision makers all possible scenarios to make a well thought and researched decision. Meaning of Business Analytics  Business Analytics (BA) refers to  The skills, technologies and practices for continious developing new insights and understanding of business performance based on data and statistical methods.  The practice of exploration of an organization’s data with emphasis on statistical analytics. Business Analytics is used by companies commited to data-driven decision making.  The statistical analysis of the data a business has acquired in order to make decisions that are based on evidence rather than a guess.  A combination of data analysis, business intelligence and computer programming. It is the science of analyzing data to find out patterns that will be helpful in developing strategies. Evolution of Business Analytics  Business analytics has been existence since very long time and has evolved with availability of newer and better technologies.  It has its roots in operation research, which has extensively used during World War 2. Operation research was an analytical way to look at data to conduct military operations.  Over a period of time, this technic started getting utilized for business. Here operation’s research evolved into management science. Again, basis for management science remained same as operation research in data, decision making models, etc.  As the economics started developing and companies became more and more competitive,management science evolved into  Business Intelligence  Decision Support Systems  PC Software Significance and Usages of Business Analytics  To make data-driven decisions  Converts available data into valuable information.  Eliminate guesswork  Get faster answer to questions  Get insight into customer behavior  Get key business metrics reports when and where needed.  It impacts functioning of whole organization. And hence, can  Improve profitability of the business  Increase market share and revenue and  Provide better return to a shareholder  Reduce overall cost  Sustain in competition  Monitor KPIs (Key Performance Indicators) and  React to changing trends in real time. Challenges for Business Analytics  Business analytics depends on sufficient volumes of high quality data.  The difficulty in ensuring data quality.  Data warehosuing require a lot more storage space than it did speed.  Business analytics is becoming a tool that can influence the outcome of customer interactions.  Technology infrastructure and tools must be able to handle the data and Business Analytics processes.  Organizations should be prepared for the changes that Business Analytics bring to current business and technology operations. Business Intelligence vs Business Analytics  Business Intelligence:  Consumes stored information  Monitors the dials on a dashboard  Answers existing questions  Business Analytics:  Produces new information  Moves the dials on a dashboard  Answers new complex, more relevant questions Domains of Business Analytics  Retail: Markdown and assortment planning  Marketing: CRM, segmentation and churn analysis  Financial Services: Risk management, credit scoring  Pharmaneutical: Drug Development  Text: Sentiment Analysis  Fraud: insurance and medical claims  Manufacturing: Warranty claiös  Hospital: Patient Scheduling  Human Resources: Workforce Planning  Police: Crime pattern analytics etc. Customer Value Management  Given the score resources of our marketing budget, which customer should we purchase?  A) Most profitable customer  B) More valuable customer  The difference is Customer Lifetime Value Customer Lifetime Value  Which customer is more important for pharmaneutical supplier?  Dentist A: 750000$ Sales and 100000$ Profits, Age=61 (More profitable)  Dentist B: 375000$ Sales and 40000$ Profits, Age = 25 (More Valuable) Scope of Business Analytics  Business analytics has a wide range of application and usages-  Descriptive analysis  Prescriptive analysis  Predictive analysis Descriptive Analysis  This branch of Business Analytics analyses and finds answer to the question  What has happened in the past?  Descriptive analysis/statistics performs the function of describing or summarizing raw data to make it easiliy understandable and interpretable by humans. Predictive Analytics  This branch of Business Analytics, uses forecasting techniques and statistical models to find out-  What is going to happen in the future?  Predictive analysis helps us in predicting the future course of events and taking neccessary measures for the same.  Predictive analysis employ  Predictive modelling and Machine Learning technics.  Predictive modelling uses statistics to predict outcomes.  Machine Learning (ML) statistical is the scientific study of algorithms and models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. Machine Learning algorithms build a mathematical model based on sample data, known in order to make predictions or decisions without being explicitly programmed to perform the task. Prescriptive Analytics  This branch of Analytics, makes use of optimization and simulation algorithms to find answer to the question-  What should we do?  Prescriptive Analysis is used to give advices on possible outcomes.  This is a relatively new field of analytics that allows users to recommend several different possible solutions to the problem and to guide them about the best possible course of action. Users of Business Analytics  Students  Business man  Accountants and Auditors  Organization/Companies/Group of Industries/ Small firm. Main Software Used for Business Analytics  MS –Excel  SPSS  R  Python  SAS  E-Views  Database Management Systems  SPSS  SPSS Statistics is a software package used for statistical analysis. Long produced by SPSS Inc., it was acquired by IBM in 2009. The current versions a re officially named IBM SPSS Statistics.  MS Excel  It is a spreadsheet application developed by Microsoft Windows. It features calculation, graphic tools, pivot tables, and a macro programming language called Visual Basic for Applications. MS Excel in Business Analytics  MS Excel is a spreadsheet application developed by Microsoft Windows.  It features  Calculation  Graphing Tools  Pivot Tables, and  A macro programming language called Visual Basic. Business Analytics Process Components of Business Analytics  There are 6 major components/categories in any analytics solution:  Data Mining  Text Mining  Forecasting  Predictive Analytics  Optimization  Visualization  Data Mining  Creates models by uncovering previously unknown trends and pattern in vast amounts of data etc. Detect insurance claims frauds, Retail Market basket analysis.  There are various statistical technics through which data mining is achieved.  Classification (when we know on which variables to classify the data etc. Age, demographics)  Regression  Clustering (when we don’t know on which factors to classify data)  Associations & Sequencing Models.  Text Mining  Discover and extract meaningful patterns and relationships from text collections.  Understand sentiments of Customers on social media sites like Twitter, Facebook, Blogs, Call center scripts etc. Which are used to improve the Products or Customer Service or understand how competitors are doing.  Forecasting  Analyze and forecast process that take place over the period of time.  Predict seasonal energy demand using historical trends,  Predict how many ice creams cones required considering demand.  Predictive Analytics  Create, manage and deploy predictive scoring models.  Customer Churn and retention  Credit Scoring  Predicting failure in shop floor machinery.  Optimization  Use of simulations technics to identify scenarios which will produce best results.  Sale price optimization.  Identifying optimal inventory for maximum fullfilment and avoid stock outs.  Visualization  Enhanced exploratory data analysis and output of modelling results with highly interactive statistical graphics.