MODULE 5FOUNDATIONS 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 returnBA enhances understanding of dataBA is vital for business to remain competitiveBA 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 analysis techniques

Data 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 TATAsGinger 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 •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.

SAS •This software has wide range of capabilities from data management to advanced analytics.

SPSS Modeler •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 •It provides a host of predictive analytics and data mining tools for businesses. This software is easy to use.

KXEN •Its one of the few companies that are driving automated analytics. This software can run huge amount of data. But its difficult to understand and explain the results.

MATLAB •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: •R is a programming language and software environment for statistical computing and graphics. It is used hardly for any analysis.

WEKA •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 Discovery1. Online Analytical Processing(OLAP)2. Ad-hoc Queries and Reports3. Data Mining4. Text Mining5. Web Mining6. Search Engines

2. Decision Support and Intelligence Systems1. Decision Support System(DSS)2. Group DSS Virtual Groups3. Executive Support4. Automated Decision Support5. Web Analytics

CATEGORIES OF BUSINESS ANALYTICS

2. Decision Support and Intelligence Systems7. Management Science and Statistical Analysis8. Applied Artificial Intelligence9. Business Performance Management(BPM)

3. Visualization1. Visual Analysis2. Dashboards and Scorecards3. Virtual Reality

DATA FOR BUSINESS ANALYTICS

DATA Collected facts and figures

DATABASE Collection 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

ExampleClassifying 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 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 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 readings SAT scores

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 GalaxyThe 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, t is time, e is the base of natural logarithms, and a, b and c are constants.

bect

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

Data, Uncontrollable Variables, and

Decision Variables

InputOutpu

t

DECISION MODELS

Example A Sales-Promotion Model of Big BazaarIn 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=500-0.05(price)+30(coupons)+0.08(advertising)

DECISION MODELS

Example An Influence Diagram for Total Cost

Influence Diagramsvisually show howVarious model elementsrelate to one another.

Descriptive Decision Models•Simply tell “what is” and describe relationships•Do not tell managers what to do

Fixed cost

Variable cost

Total cost

DECISION MODELS

Example A Mathematical Model for Total Cost

TC = F+VQ

TC is Total CostF is Fixed CostV is Variable Unit CostQ is Quantity Produced

Total Cost

Variable Cost

Unit Variable

Cost

Quantity Produced

Fixed Cost

DECISION MODELS

Example A Break – Even Decision ModelTC(Manufacturing) = Rs50,000 + Rs125*QTC(Outsourcing) = Rs175*Q

Breakeven Point:Set TC(Manufacturing)

= TC(Outsourcing)

Solve for Q = 1000 unit

DECISION MODELS

Examples A Linear Demand Prediction ModelAs price increases, demand falls.

DECISION MODELSExample A Nonlinear Demand Prediction

ModelAssumes 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) + 5200.6Total 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 PROCESS1. Recognizing the problem2. Defining the problem3. Structuring the problem4. Analyzing the problem5. Interpreting results and making a decision6. 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 includes1. 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 Queries against Summarized Data 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 DASHBOARDS1. Strategic Dashboard2. Analytical Dashboard3. Operational Dashboard

PRINCIPLES OF EFFECTIVE DASHBOARDS4. It should provide timely summary information

that are important to the user. Example- A CAR dashboard which provides all

information like speed, oil indicator, heat level, etc.

5. It should provide all information on one single screen, with multiple windows in it.

6. 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. A data dashboard should call attention to unusual measures that may require attention, but not in an overwhelming way.

5. 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 REPORTS1. Routine Reports

Example- weekly sales figures, units produced.

2. Ad-hoc(or On Demand) Reports Example- list of all customers who purchased a

company’s products more than Rs5000/- 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