Importance of Business Analytics

• There is a strong relationship of BA with:

  • Profitability of businesses

  • Revenue of businesses

  • Shareholder return

  • BA enhances understanding of data

  • BA is vital for businesses to remain competitive

  • BA enables creation of informative reports

• Areas where Analytics are often used:

  • NEW CUSTOMER ACQUISITION

  • CROSS-SELL / UP-SELL

  • PRICING TOLERANCE

  • SUPPLY OPTIMIZATION

  • STAFFING OPTIMIZATION

  • FINANCIAL FORECASTING

  • PRODUCT PLACEMENT

  • CHURN

  • INSURANCE RATE SETTING

  • FRAUD DETECTION

OLAP (online analytical processing)

• Google sheets is an example of OLAP system

Type of Data

• Four types of data or levels of measurement:

  • Nominal(Categorical)

  • Ordinal

    • The order of the values is what’s important and significant

    • The differences between each one is not really known, and
      may or may not equal

  • Interval

    • Similar to an ordinal variable, except that the intervals between the values of the interval variable are equally spaced.

    • There is no true zero

    • Can add and subtract but no multiplication

  • Ratio

    • Ratio scales are the ultimate measurement scales because they tell us about the order, they tell us the exact value between units, AND they also have an absolute zero–which allows for a wide range of both descriptive and inferential statistics to be applied.

    • Ratio variables can be meaningfully added, subtracted, multiplied, divided (ratios).

• What type of statistical testing does each data type use:

  • Nominal and Ordinal uses non-parametric testing

  • Interval and Ratio uses parametric testing

What type of data is each level of measurement

• Nominal and Ordinal are qualitative

• Interval and Ratio are quantitative

• Nominal scales are used for labeling variables/classes, without any quantitative value or logical ordering.

• Continuous variables-is a variable that has an infinite number of possible values.

Data for Business Analytics

• What is the area circled(red) in the graphic referred to as?

  • Entities

• What is the area circled(blue) in the graphic referred to as?

  • Attributes

What is the area surrounded(green) in the graphic referred to as?

• Records

12-Step Methodology

• Define or refine the business objective

• Select the data

  • Questions to ask:

    • What is available?

    • What is the right level of granularity?

    • How much data is needed?

    • How much history is required?

    • How many variables should be used?

• Explore input data

  • Examine distributions

    • Study histograms

    • Identify extreme and missing values

  • Compare values with descriptions

  • Validate assumptions

• Prepare or repair the input data

  • Data imperfectly describes the features of the real world.

    • Data might be missing or empty.

    • Samples might not be representative.

    • Numeric variables might have unusual distributions and outliers.

    • Data might be coded inconsistently

• Transform the input data

  • Change counts into percentages.

  • Remove outliers.

  • Combine variables.

  • Replace categorical variables with some numeric function of the categorical values.

  • Assign missing values.

  • Transform using mathematical functions, such as logs.

  • Translate dates to durations.

• Apply Analysis

• Deploy the Models

• Assess the Results

  • Compare actual results against expectations.

  • Compare the challenger’s results against the champion’s.

  • Did the model find the right people?

  • Did the action affect their behavior?

  • What are the characteristics of the customers
    most affected by the intervention?

• As you examine the earliest models performance, refine your business goals to get more out of the data

Type of Questions and Analytics

• Descriptive

  • Questions:

    • What happened?

    • What’s happening?

    • What exactly is the problem?

    • What actions are needed?

  • Enablers

    • Ad hoc Reports

    • Dashboards

    • Data Warehousing

    • Visualization

  • Outcomes

    • Well defined business problems and opportunities

• Predictive

  • Questions

    • Why is this happening?

    • What will happen next?

    • Why will it happens?

  • Enablers

    • Data Mining

    • Text Mining

    • Web/Media Mining

    • Forecasting

  • Outcomes

    • Accurate projections of the future states and conditions

• Prescriptive

  • Questions

    • What should I do?

    • Why should I do it?

    • What’s the best that can happen?

  • Enablers

    • Optimization

    • Decision Modeling

    • Randomized Testing

  • Outcomes

    • Best possible business decisions and transactions

Business Analytics vs. Data Mining

• What is a key part of effective business analytics?

  • Data Mining

• Data Mining - is a computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems.

• Components of Data Mining

  • Customer segmentation

  • Predictive modeling

  • Association rule mining

Predictive Modeling with Machine Learning(ML)

• Machine Learning(a branch of artificial intelligence)

  • Is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on learning patterns and inferences from examples instead

Supervised vs. Unsupervised Learning

• Supervised learning:

  • Learning a function that maps an input to an output based on example input-output pairs.
    Y = f(x)

  • The goal is to approximate the mapping function so well that when you have new input data (x) that you can predict the output variables (Y) for that data.

  • In training data, for any input, the correct output (i.E., Class labels) are known.

• Examples: decision tree, logistic regression

• Unsupervised learning is a type of ML algorithms used to draw inferences from datasets consisting of input data without labeled responses.

• Clustering analysis: grouping similar instances

• Example applications

  • Customer segmentation in CRM

Parametric vs. Non-parametric Testing

• Parametric - statistical test is one that makes assumptions about the parameters (variables) of the population distribution(s) from which one's data are drawn.

• Non-parametric- test is one that makes no such assumptions about the parameters(variables) of the population distribution(s) from which one’s data are drawn.

• Interval and ratio data are used with parametric tests such as ANOVA and the independent samples t-test in which distributions are predictable.

• Nominal and ordinal data are used with non-parametric tools such as the histogram.

Descriptive Statistics

• The goals when you are describing data are to

  • Screen for unusual sample data values

  • Inspect the spread and shape of continuous variables

  • Characterize the central tendency of the sample (e.g., Mean, SD).

Statistical Description of Data

• Statistics describes a numeric set of data by its

  • Center

  • Variability

  • Shape

• Statistics describes a categorical set of data by

  • Frequency, percentage or proportion of each category