MODULE 5 FOUNDATIONS OF ANALYTICS OBJECTIVES To understand

MODULE 5 FOUNDATIONS OF ANALYTICS

OBJECTIVES To understand the fundamentals of business analytics. To know the evolution of business analytics. To study the scope of business analytics. To evaluate the DATA for business analytics. To describe Decision Models. To understand fundamentals of data warehousing. To prepare dashboard and reporting.

DEFINING BUSINESS ANALYTICS Analytics is the use of data, information technology, statistical analysis, quantitative methods, and mathematical or computer –based models to help managers gain improved insight about their business operations and make better, fact-based decisions.

BUSINESS ANALYTICS APPLICATIONS Management of customer relationships Financial and marketing activities Supply chain management Human resource planning Pricing decisions Sport team game strategies

IMPORTANCE OF BUSINESS ANALYTICS There is a strong relationship of BA with: � Profitability of business � Revenue of business � Shareholder return BA enhances understanding of data BA is vital for business to remain competitive BA enables creation of informative reports

EVOLUTION OF BUSINESS ANALYTICS Operations research Management science Business intelligence Decisions support systems Personal computer software

TYPES OF BUSINESS ANALYTICS Descriptive analytics � Uses data to understand past and present. Predictive analytics- Analyzes past performance Predictive Data analysis techniques mining Simulation Prescriptive analytics- Uses optimization techniques � Prescriptive analytics techniques Simulation optimization Decision analysis

SHOPPERS STOP-RETAIL MARKDOWN Shoppers stop clears seasonal inventory by reducing prices. The question is: � When to reduce the price and by how much? Descriptive analytics: examine historical data for similar products(prices, units sold, advertising, . . ) Predictive analytics: predict sales based on prices Prescriptive analytics: find the best sets of pricing and advertising to maximize sales revenue.

SCOPE OF BUSINESS ANALYTICS Analytics in Practice: Ginger Hotel from TATAs Ginger has owns numerous hotels. Uses analytics to: � Forecast demand for rooms � Segment customers to chose right destination Uses prescriptive models to: � Set room rates � Allocate rooms

TOOLS OF BUSINESS ANALYTICS MS Excel SAS SPSS Modeler • Excel is an excellent reporting tool. We may use different analytic software to do analytical work but at the end we will use Excel for reporting and presentation of results. • This software has wide range of capabilities from data management to advanced analytics. • It’s a data mining software. This tool has an intuitive GUI and its point-and-click modelling capabilities are very comprehensive.

TOOLS OF BUSINESS ANALYTICS Salford Systems KXEN MATLAB • It provides a host of predictive analytics and data mining tools for businesses. This software is easy to use. • Its one of the few companies that are driving automated analytics. This software can run huge amount of data. But its difficult to understand explain the results. • It’s a statistical computing software. It allows matrix manipulations, plotting of functions and data, implementation of algorithms and creations of user interfaces.

TOOLS OF BUSINESS ANALYTICS R: WEKA • R is a programming language and software environment for statistical computing and graphics. It is used hardly for any analysis. • Waikato Environment for Knowledge Analysis (WEKA), it’s a machine learning software. It’s a open source software most popular among business peoples.

CATEGORIES OF BUSINESS ANALYTICS 1. Information and Knowledge Discovery 1. 2. 3. 4. 5. 6. 2. Online Analytical Processing(OLAP) Ad-hoc Queries and Reports Data Mining Text Mining Web Mining Search Engines Decision Support and Intelligence Systems 1. 2. 3. 4. 5. Decision Support System(DSS) Group DSS Virtual Groups Executive Support Automated Decision Support Web Analytics

CATEGORIES OF BUSINESS ANALYTICS 2. Decision Support and Intelligence Systems 7. 8. 9. 3. Management Science and Statistical Analysis Applied Artificial Intelligence Business Performance Management(BPM) Visualization 1. 2. 3. Visual Analysis Dashboards and Scorecards Virtual Reality

DATA FOR BUSINESS ANALYTICS DATA � Collected DATABASE � Collection facts and figures of computer files containing data INFORMATION � Comes from analyzing data

DATA FOR BUSINESS ANALYTICS EXAMPLES OF USING DATA IN BUSINESS: Annual reports Accounting audits Financial profitability analysis Economic trends Marketing research Operations management performance Human resource measurements

DATA FOR BUSINESS ANALYTICS Metrics are used to quantify performance. Measures are numerical values of metrics. Discrete metrics involve counting � On time or not on time � Number or proportion of on time deliveries Continuous metrics are measured on a continuum � Delivery time � Package weight � Purchase price

DATA FOR BUSINESS ANALYTICS Excel sheet example

DATA FOR BUSINESS ANALYTICS Four Types Data Based on Measurement Scale: Categorical (nominal) data Ordinal data Interval data Ratio data

DATA FOR BUSINESS ANALYTICS Example Classifying Data elements in Purchasing database

DATA FOR BUSINESS ANALYTICS Classifying Data elements in Purchasing database categorical Ratio Interval

DATA FOR BUSINESS ANALYTICS Categorical (nominal) Data placed in categories according to a specified characteristic Categories bear no quantitative relationship to one another Examples: � Customer’s location (America, Europe, Asia) � Employees classification (manager, supervisor, associate)

DATA FOR BUSINESS ANALYTICS Ordinal Data is ranked or ordered according to some relationship with one another No fixed units of measurement Examples: � College football rankings � Survey responses (poor, average, good, very good, excellent)

DATA FOR BUSINESS ANALYTICS Interval Data Ordinal data but with constant differences between observations No true zero point Ratios are not meaningful Examples; � Temperature � SAT scores readings

DATA FOR BUSINESS ANALYTICS Ratio Data Continuous values and have a natural zero point Ratios are meaningful Examples: � Monthly sales � Delivery times

DECISION MODELS Model: An abstraction or representation of a real system, idea, or object Captures the most important features Can be a written or verbal description, a visual display, a mathematical formula, or a spreadsheet representation

DECISION MODELS Examples Three Forms Of a Model- Samsung Galaxy The sales of a Samsung Galaxy. Often follow a common pattern. Sales might grow at an increasing rate over time as positive customer feedback spreads. (See the S-shaped curve on the following slide. ) A mathematical model of the S-curve can be identified; for example, S=a℮ , where S is sales, bectlogarithms, and a, t is time, e is the base of natural b and c are constants.

DECISION MODELS

DECISION MODELS A decision model is a model used to understand, analyze, or facilitate decision making. Types of model input � Data � Uncontrollable variables � Decision variables (controllable) Types of model output � Performance measures � Behavioral measures

DECISION MODELS Nature of Decision Models Output Input Data, Uncontrollable Variables, and Decision Variables Decision Model Measures of Performance or Behavior

DECISION MODELS Example A Sales-Promotion Model of Big Bazaar In the big bazaar, managers typically need to know how best to use pricing, coupons and advertising strategies to influence sales. Using Business Analytics Big Bazaar can develop a model that predicts sales using price, coupons and advertising.

DECISION MODELS Sales=5000. 05(price)+30(coupons)+0. 08(advertising)

DECISION MODELS Descriptive Decision Models • Simply tell “what is” and describe relationships • Do not tell managers what to do Example An Influence Diagram for Total Cost Influence Diagrams visually show Various model elements relate to one another. Fixed cost Total cost Variable cost

DECISION MODELS Example A Mathematical Model for Total Cost TC = F+VQ Total Cost TC is Total Cost F is Fixed Cost V is Variable Unit Cost Q is Quantity Produced Variable Cost Unit Variable Cost Fixed Cost Quantity Produced

DECISION MODELS Example A Break – Even Decision Model TC(Manufacturing) = Rs 50, 000 + Rs 125*Q TC(Outsourcing) = Rs 175*Q Breakeven Point: Set TC(Manufacturing) = TC(Outsourcing) Solve for Q = 1000 unit

DECISION MODELS Examples A Linear Demand Prediction Model As price increases, demand falls.

DECISION MODELS Example A Nonlinear Demand Prediction Model Assumes price elasticity (constant ratio of % change in demand)

DECISION MODELS Predictive Decision Models often incorporate uncertainty to help managers analyze risk. Aim to predict what will happen in the future. Uncertainty is imperfect knowledge of what will happen in the future. Risk is associated with the consequences of what actually happens.

DECISION MODELS Prescriptive Decision Models help decision makers indentify the best solution. Optimization – finding values of decision variables that minimize (or maximize) something such as cost (or profit). Objective function – the equation that minimizes (or maximizes) the quantity of interest. Constraints – limitations or restrictions. Optimal solution – values of the decision variables at the minimum (or maximum) point.

DECISION MODELS Example A Pricing Model A firm wishes to determine the best pricing for one of its products in order to maximize revenue. Analysts determined the following model: Sales = -2. 5698(price) + 5200. 6 Total revenue = (price) (sales) Identify the price that maximizes total revenue, subject to any constraints that might exist.

DECISION MODELS Deterministic prescriptive models have inputs that are known with certainty. Stochastic prescriptive models have one or more inputs that are not known with certainty. Algorithms are systematic procedures used to find optimal solutions to decision models. Search algorithms are used for complex problems to find a good solution without guaranteeing an optimal solution.

PROBLEM SOLVING AND DECISION MAKING BA represents only a portion of the overall problem solving and decision making process. SIX STEPS IN PROBLEM SOLVING PROCESS 1. 2. 3. 4. 5. 6. Recognizing the problem Defining the problem Structuring the problem Analyzing the problem Interpreting results and making a decision Implementing the solution

PROBLEM SOLVING AND DECISION MAKING 1. Recognizing the problem Problems exists when there is a gap between what is happening and what we think should be happening. For example: Cost are too high compared with competitors.

PROBLEM SOLVING AND DECISION MAKING 2. Defining the problem Clearly defining the problem is not a trivial task. Complexity increases when the following occur: � � � Large number of courses of action Several competing objectives External groups are affected Problem owner and problem solver are not the same person Time constraints exist

PROBLEM SOLVING AND DECISION MAKING 3. Structuring the Problem Stating goals and objectives Characterizing the possible decisions Identifying any constraints or restrictions

PROBLEM SOLVING AND DECISION MAKING 4. Analyzing the problem Identifying and applying appropriate Business Analytics techniques Typically involves experimentation, statistical analysis, or a solution process Much of this course is devoted to learning BA techniques for use in step 4.

PROBLEM SOLVING AND DECISION MAKING 5. Interpreting Results and Making a Decision Managers interpret the results from the analysis phase. Incorporate subjective judgment as needed. Understand limitations and model assumptions. Make a decision utilizing the above information.

PROBLEM SOLVING AND DECISION MAKING 6. Implementing the Solution Translate the results of the model back to the real world. Make the solution work in the organization by providing adequate training and resources.

DATA WAREHOUSING What is DATA WAREHOUSING? It’s a subject oriented integrated non- volatile, time varying collection of data in support of its decision making process.

INTRODUCTION-CONT’D. Where is it used? It is used for evaluating future strategy. It needs a successful technician: � Flexible. � Team player. � Good balance of business and technical understanding.

DATA WAREHOUSE Subject oriented Data integrated Time variant Nonvolatile

CHARACTERISTICS OF DATA WAREHOUSE Subject oriented. Data are organized based on how the users refer to them. Integrated. All inconsistencies regarding naming convention and value representations are removed. Nonvolatile. Data are stored in read-only format and do not change over time. Time variant. Data are not current but normally time series.

DATA WAREHOUSING ARCHITECTURE

DATA WAREHOUSING ARCHITECTURE It’s a structure that brings all the components of a data warehouse together is known as architecture. Architecture is a comprehensive blueprint. It defines the standards, measurements, general design, and support techniques.

DATA WAREHOUSING ARCHITECTURE It includes 1. Warehouse Database Server: The bottom tire is a warehouse database server. � It is a relational database system, Data from operational databases and external sources(such as customer profile information provided by external consultants) are extracted using application program interfaces known as gateways. � 2. OLAP Server: Middle tire one is an OLAP server. � Which is implemented using � A relational OLAP (ROLAP) A multidimensional OLAP (MOLAP)

DATA WAREHOUSING ARCHITECTURE 3. Client: Top tire is a client. � Which contains query and reporting tools, analysis tools and data mining tools( ex – Trend analysis, prediction) � ADVANTAGES OF DW � � � More Cost Effective Decision Making Better Enterprise Intelligence Enhanced Customer Services Business Reengineering Information System Reengineering

DISADVANTAGES OF DW Installation cost Time – Taking Change Resistance Specific Skills Required Complex Management Acceptance Security Issues

APPLICATIONS OF DW Standard Reports and Queries against Summarized Data Mining Interface with Other Data Warehouses

DASHBOARD It’s an executive system UI that is designed to be easy to read. It provides decision makers the input necessary to “drive” the business. It displays tables, graphics, gauges (colour differences) It’s a combined information holder which provides multiple views to user i. e he can access the information in any devices.

TYPES OF DASHBOARDS 1. 2. 3. 1. Strategic Dashboard Analytical Dashboard Operational Dashboard PRINCIPLES OF EFFECTIVE DASHBOARDS It should provide timely summary information that are important to the user. � 2. 3. Example- A CAR dashboard which provides all information like speed, oil indicator, heat level, etc. It should provide all information on one single screen, with multiple windows in it. The Key Performance Indicators(KPI) is displayed in the data dashboard should convey meaning to its end user and be related to the decisions the user makes

PRINCIPLES OF EFFECTIVE DASHBOARDS 4. 5. A data dashboard should call attention to unusual measures that may require attention, but not in an overwhelming way. Color should be used to call attention of specific values.

BENEFITS OF DASHBOARD Visual presentation of performance measures. Ability to identify and correct negative trends. Measures efficiencies/inefficiencies. Ability to generate detailed reports showing new trends. Ability to make more informed decisions based on collected data. Align strategies and organizational goals. Save time over running multiple reports. Gain total visibility of all systems instantly.

REPORTING These are often used to display the results of an experiment, investigation or inquiry. Reports provide thus some static snapshots in time of the performance/status of the entity one is examination. TYPES OF REPORTS 1. Routine Reports � 2. Example- weekly sales figures, units produced. Ad-hoc(or On Demand) Reports � Example- list of all customers who purchased a company’s products more than Rs 5000/- each during October 2005.

MASTER DATA MANAGEMENT(MDM) Definition: It is a comprehensive method of enabling an enterprise to link all of its critical data to one file, called a master file, that provides a common point of reference. When properly done, MDM streamlines data sharing among personnel and departments. In addition, MDM can facilitate computing in multiple system architectures, platforms and applications.

Categories of Data Meta Data Reference Data Master Data Transaction Data Historical Data

ADVANTAGES OF MDM Enhances efficiency Optimise outcome Spot and Act on Insights Faster Accelerate Time to Market Elevate Customer Satisfaction

DISADVANTAGES OF MDM Lack of Functional sponsorship Failure to Adjust Business Processes Accordingly Lack Of Validation Taking an “ALL at Once” Approach to Deployment Failure to Create And Enforce Data Governance Procedures