What is the Value of SAS Analytics?

Copy r ight © 2013, SAS Ins t i tu te Inc . A l l r ights reserved.

SAS ANALYTIC VALUE


SAS ANALYTIC AREAS


ANALYTICS LIFECYCLE OF ANALYTICS

IntelligenceIntelligence

Bus

ines

s V

alue

Bus

ines

s V

alue

Industry ExpertiseIndustry Expertise

OptimizationOptimization

Predictive ModelingPredictive Modeling

ForecastingForecasting

Reporting / OLAPReporting / OLAP

Data ManagementData Management

Data AccessData Access

Beyond BI™

What’s the best that can happen?

How much and where?

What will happen next?

What happened?

How many, how often?


ANALYTICSWHAT’S THE FIRST THING YOU WOULD DO WHEN PRESENTED WITH DATA?


DETERMINE BUSINESS OBJECTIVE

• Overall: What are we trying to accomplish?• Data: What data is required and what is available?• Modelling: What models can be built?• Scoring: How will models be evaluated?• Deployment: How will results be communicated

back to the business?

The Analytic Process


ANALYTICS NOW ON TO THE DATA

Target Variable(s) – What do you wish to measure or predict (i.e. Sales, Revenue) and what format does it take (numeric, binary etc.)

ID Variable(s) – What unique identifiers are in your data the help you to identify distinct observations (i.e. Transaction #s, Test #s, client ID #s, Visit #s, etc.)

Explanatory Variables – What variables do you have that might impact your target (i.e. customer demographic, timeframes, ratings, prior ratings/spend etc.)

Classification Variables – What information do you have that would help in splitting out the data into distinct groups – could be segment specific data that can be used to build hierarchies that you can drill down into (i.e. Country ProvinceCity)


DATA UNDERSTANDING

• Describe the Data: Create summary statistics and

correlations.• Explore the Data: Make discoveries about data patterns.• Verify Data Quality: Assess missing, un-standardized and

data with large numbers of categories.



DATA PREPARATION

• Clean Data: What steps are required to clean up missing

values and transform information.• Construct Data: Modify and compute new columns for

better modelling.

Data Mining Process


ANALYTICS STATISTICS

Statistics that are used to: describe sample characteristics are called descriptive statistics draw conclusions about the population are called inferential

statistics.


ANALYTICS DATA MINING AS A PROCESS

What? Selecting, exploring and modeling large amounts of data with speed and accuracy.

Why? Uncover previously unknown patterns and trends to give you a competitive edge.

Visualize

Manage

Optimize

Deploy Results

Collaborate

Monitor Performance

Explore Data

Identify Metrics

Formulate Problem

Experiment

Develop Models

Validate Models


DATA MINING TWO PASSAGES

Predictive

Predict or estimate an outcome Describe patterns in data

Descriptive


ANALYTICAL TECHNIQUES BUSINESS PROBLEM

Segmentation

Predictive Modeling

Text Mining

Association Analysis


MODELING

• Build multiple models using appropriate

algorithms.• Assess model performance in a way that matches

the business objectives.



FORECASTING IS UBIQUITOUS


WHAT IS A TIME SERIES?

•Anything measured over time… Weekly sales Daily interest rates Annual income Hourly call center volume


ANALYTICS TIME SERIES FORECASTING

• Before commencing any time series

forecasting task it is important to get a better

understanding of the data at hand• This will help you answer questions such as

• What is the degree of seasonality?• Is there an underlying trend?• Is there a hierarchy in my data I should use?• Would it make more sense to try and segment

my data and model each segment separately?• Are there time series which are not suitable for

time series modeling?• Are there indications that my forecast is

influenced by external factors?


TEXT ANALYTICS WHAT IS IT AND WHY NOW?

Using statistical methods to analyze and interpret the meaning of textual data.

Automated solutions went mainstream in early 2000’s. Unstructured data now accounts for 80% of all data being created.

Social Media has kicked off a race to capture the broad and vast content now being exposed by the web.

Text Analytics is useful when it is part of a complete data mining process.


WHAT IS TEXT MINING?

•The process of discovering and extracting •meaningful patterns and relationships from text collections

Uncovering underlying themes or concepts in large document

collections Uses descriptive modeling to discover themes and concepts in a

document collection Uses predictive modeling to classify documents into categories Converts unstructured text into structured data objects (converts

text to numbers!) Combining free-form text and quantitative variables to derive

information

Knowledge


ANALYTICS MORE TEXT

Content Categorization works from the top down and categorizes documents based on what you do know – on categories that you have previously set up. The two technologies augment each other leading to better categorization and of text documents.

Sentiment Analysis collects text inputs from Web sites and internal files systems, converts different text formats, and automatically assesses the positive, negative and neutral opinions contained within electronic unstructured data.


OPTIMIZATION: DEFINITION

• Optimization is the process of choosing the actions that result in the best

outcome• Optimization is a technology for calculating the best possible utilization of

resources needed to achieve a desired result


OPTIMIZATION: KEY ELEMENTS

2. Objective Maximize Profit Minimize Costs

Minimize

Distance

Traveled

Minimize

Unused

Raw Materials

3. Constraints

Factory Capacities

Customer Demands Materials/Personnel

Available

Available Routes

Budget

Select decision variable values (make decisions) toachieve the objective while obeying the constraints.

1. Decision Variables

Production Levels

Route Selections

Resource Allocations

Schedule Elements

Go/No Go Choices


BUSINESS DEPLOYMENT

• Integrate the results into business processes. For example utilize a

score code to get a probability of churn/response/purchase. Include

the probability into reports that get distributed to business users. • Automatically monitor the frequency and distribution of customer

segments to identify changes in patterns.• This step is where most unsuccessful modelling practices fail.

Similar to Step 1 this is not a technology capability. It rests with the

business to properly plan for and utilize a modelling project.


Copy r ight © 2012, SAS Ins t i tu te Inc . A l l r ights reserved. www.SAS.com

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What is the Value of SAS Analytics?