IBM's Business Analytics Portfolio for Training Purposes

IBM Business Analytics Portfolio Presented by:

analyticsNatalija Pavic, Account Manager 647 678 5907, [email protected]

Growing complexity of business demands for information

Why?

How are we doing?

What should we be doing?

…

Analysis

Reporting

Planning

INTERN

AL D

ATA

EXT

ERNA

L DA

TA

ERP

MAINFRAME

EXTERNAL

BILLING

HR

CRM

…

Dashboarding

Scorecarding

Budgeting …

Information Information-driven and accountable culture through Dashboards and Reports

Insight Early identification of opportunities and issues through Analysis

Action Align resources with decisions through Planning

Planning Analysis

Dashboards/Reports

Relevant Information

Actionable Insights

Smarter Decisions

Better Outcomes

Business Analytics

Investing in an Analytics Platform

Risk Management

Competence / Skill Level

Com

petit

ive

Adv

anta

ge

Transactional Data

Forecasting & Planning

Ad Hoc reporting

Information Warehouse

Standardized Reporting

Predictive Modeling

Standards: Master Data Dataset Management Common Dimensions

Applications: Blue Insight Cognos BI Cognos TM1 SPSS Algorithmics Excel Automation

Resulting Capabilities:

In Memory Analytics Integrated Planning & Analytics Enterprise Data Scale Real time reporting Advanced analytics Predictive Capabilities Mobile

Common Delivery: Finance Analytics Portal

The Business Analytics “Stack”

Degree of Complexity

Valu

e an

d O

pera

tiona

l Exc

elle

nce

Standard Reporting

Ad hoc reporting

Query/drill down

Alerts

Simulation

Forecasting

Predictive modeling

Optimization

What exactly is the problem?

What will happen next if ?

What if these trends continue?

What could happen…. ?

What actions are needed?

How many, how often, where?

What happened?

Stochastic Optimization

Based on: Competing on Analytics, Davenport and Harris, 2007

COGNOS BI Business Intelligence

COGNOS TM1 Table Manager 1

SPSS Statistical Package for the Social Sciences Performance

Management

Predictive Analytics Advanced Analytics

•  Reporting •  Data Visualizations •  Dashboards •  Key Performance

Indicators •  Scorecarding •  For the company wide

distribution of Metrics •  Can drill down, Ad-Hoc

Queries •  Maximally customizable •  Enterprise-wide and

Scalable

•  Planning, forecasting •  “Excel-hell” •  Write back •  Security, Validation,

Webportal •  Budgeting, Capital

Planning, Cost Analysis •  Finance Department •  Perfect compatibility

with excel, can go back and forth

•  Can load data from anywhere into a TM1 “cube”

•  Predicting •  Finding correlations,

trends and patterns in data sets

•  Widely used by statisticians and academics

•  Statistical analysis •  Build models, use

machine learning •  WATSON •  Big Data

BI TM1

SPSS

•  Maximally customizable •  Compatible with all

data •  Report creation abilities

not existent with competitors

•  Visualizations are not the best when comparing to Tableau or QlickTek

•  Resiliant to scalability, enterprise ready

•  Deep integration with other IBM products for the whole stack

•  Maximally customizable •  Compatible with all

data •  The only software to

have a smooth transition from and to excel

•  Looks like excel so friendly for excel users

•  Manipulate multiple datasets easily – true sandbox

•  Can be used by different departments for different purposes

•  Powerful consolidation of data

•  User-friendly •  Easy to use •  Makes powerful

statistical tools accessible to a more commercial user

•  Does not compete with Oracle

•  SAP cannot compete with it yet

•  Can be used as a single user licence by one user on a desktop

Competitive Comparisons

Competitors

It is this approach to link data with analytics capabilities to manage outcomes

How are we doing? Why is this occurring? What should we be doing?

Dashboards & scorecards

Social Analytics

Reporting & visualization

Sentiment Analysis

Real-time Decisions

Predictive modeling

Forecasting & simulation

Planning/ budgeting

Analy7cs capabili7es

Message sources

Rela-onal sources

Applica-on sources

OLAP sources

Modern and legacy sources

Unstructured data

Varied, unconnected data sources

The Data Layer

The Analy7cs Layer

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How are we doing? Immediate Insights to Business Performance

•  Scorecards

•  Dashboards

•  Reports

•  Real-Time Monitoring

19

Why? Deeper Analysis of Trends & Patterns

•  Ad-hoc Query

•  Analysis and Exploration

•  Trend and Statistical Analysis

20

What should we be doing? Foresight to Plan & Allocate Resources

•  What-If Analysis

•  Predictive Analysis

IBM COGNOS BI Business Intelligence

22

A Unified Workspace instantly usable by everyone

Built-In Collaboration

Progressive Interaction

How the full breadth of BI capabilities come together

All Time Horizons

Unified Workspace

Complete perspective on the business

Simple to use with unprecedented power one click away

Place historical information alongside real-time updates, plans, and predictive results

Follow the natural path from viewing, to light exploration, to deeper analysis

Engage the right people at the right time to exchange ideas and knowledge

Accelerate alignment and improve decision-making

Defini7on

Dashboards

•  Contains necessary informa-on to quickly determine the state of affairs for users

Characteris7cs •  Should have a simple layout showing summary •  May contain scorecard style data, but will oFen contain other types of data •  Will oFen link to more detailed reports

Best Prac7ces

•  Keep the dashboard simple and easy to comprehend •  Informa-on at a glance •  No prompt pages •  Use drill-‐through reports to provide more details •  Use charts and color paleLes to highlight important informa-on

Defini7on

Interac7ve Reports

•  Used to support daily decision making

Characteris7cs •  Fast to load •  Informa-on at a glance •  Easily move from one report to another using drill-‐through links

Best Prac7ces

•  Show only the informa-on needed •  Be conscious of the limited display area •  Avoid `next page` •  Avoid horizontal scrolling •  Minimize ver-cal scrolling •  Use promp-ng to limit data or to change layout

Defini7on

Ac7ve Reports

•  Used for High level summarized data •  Interac-ve graphic rich format •  Offline (runs disconnected from Cognos server)

Characteris7cs •  Meant for `Presenta-on` type of data •  Package in one file

Best Prac7ces

•  Complete requirements are essen-al •  Design for a specific interface (iPad, Android devices..) •  Graphic Design mentality required •  Story boards, PowerPoint mock ups •  Pay aLen-on to file sizes

Drag-‐and-‐drop data, smart filters, and intui7ve analysis

Modify plans, budgets, and forecasts accordingly

Exchange files or publish and extend within the Cognos Family

Add compelling visuals, widgets and themes

Insight to Action Model scenarios, test assump7ons, and op7mize

Model scenarios, test assump7ons, and op7mize

Cognos Insight

Cognos Insight

When to use? Who?

ü  Personal Desktop Analytics ü  Prototyping ü  Ad-Hoc Analysis ü  Workflow Application Design ü  What-if Analysis ü  “Throw-away” Analysis

ü  Data Analysts ü  Workflow Contributors

Type of Client Skills Required

ü  Thick Client ü  Can be launched from the Web

ü  Minimal Training Required (1-2 days) ü  Knowledge of Data

Cognos BI – Report Studio

Cognos BI – Report Studio

When to use? Who?

ü  Pixel-Perfect Reports ü  Emailed Reports ü  Scheduled Reports

ü  Report Authors


ü  Web-Based / Thin Client ü  Report Studio

Cognos BI – Active Reports

Cognos BI - Mobile

Cognos BI – Active Reports & Mobile

When to use? Who?

ü  Mobile Device ü  Online or Offline

ü  End Users


ü  Web-‐Based (Mobile Browser) ü  Na-ve iPad or Android App

ü  None

Cognos BI – Cognos Workspace

Cognos BI – Cognos Workspace

When to use? Who?

ü  Self-Service Assembly of Existing BI Content ü  Dashboards

ü  End Users


ü  Web-Based / Thin Client ü  Cognos Workspace (1/2 day training)

Cognos BI – Cognos Workspace Advanced

Cognos BI – Cognos Workspace Advanced

When to use? Who?

ü  Ad-‐Hoc Analysis ü  Self-‐Service Repor-ng ü  Add in External Data Files

ü  Advanced Ad-‐hoc Users ü  Analysts


ü  Web-‐Based / Thin Client ü  Cognos Workspace Advanced (1-‐2 day training) ü  Basic Ad-‐hoc Repor-ng

IBM COGNOS TM1 Table Manager 1

Performance Management

© 2012 IBM Corpora-on

Common Informa7on & Technology PlaPorm

IBM BA Performance Management

Profitability Modeling & Optimization

Management & Performance Reporting

Scorecarding & Strategy Management

Financial Close Management

Sales Performance Management

Integration and Automation Hierarchy Management Analytic Data Management

Planning Analysis & Forecasting

Align resources with corporate objectives and market events through improved visibility and control over the levers of performance

Finance IT Human

Resources Sales Marketing Customer Service Operations

Product Development

Performance Management

Finance Opera-ons Sales / Customer

Account Analysis

& Close

Financial Consol

Repor-ng &

Analysis

Disclosure Mgmt &

XBRL

Sales and Ops

Planning

Capital Expend. Planning

Product Profitability

Incen-ve Comp Mgmt

Quota Planning

Territory & Channel Mgmt

Planning Analysis & Forecas-ng

Profitability Modeling & Op-miza-on

Performance Repor-ng & Scorecarding

Governance, Risk, and Compliance Industry Specific Blueprints Industry Specific Blueprints

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IBM BA Performance Management Solutions

© 2012 IBM Corpora-on

Key Contacts For Performance Management

WHO MAIN CONCERNS

CFO l  Accountability & Timeliness l  Integration across all LoBs l  Insightful Info to drive business

VP Planning l  Accuracy, Forecast/Actual deltas l  Frequency of collection l  Amount of detail, value of info

Controller l  Accuracy l  Adequate Controls l  Flexibility to react

Accounting/Finance Manager l  Time spent collating l  No time to check, value add activity l  Evenings and weekend work

CIO / IT l  Audit, Controls, IT Standards l  Finance system integration l  Reporting & Data management

VP Sales l  Territory Planning challenges l  Complex Compensation plans l  Integration w/ Finance

IBM BA Performance Management – BeLer Outcomes

Drive efficiencies and scale •  Eliminate intensely manual efforts •  Structure and automate dynamic processes •  Scale to large user communi-es and data sets

Gain agility and preparedness •  Link opera-onal and financial performance management •  Support advanced analy-c techniques (e.g., scenario •  and predic-ve analy-cs, narra-ve performance repor-ng) •  Eliminate delays in coordina-ng around emerging reali-es

Improve effec7veness and outcomes •  Drama-cally reduce risk of errors •  Cost-‐effec-vely address compliance •  Drive new confidence in analy-cs-‐driven decision making

Confidence

Control

Time

What is TM1?

§  Created in 1984 §  64-bit In-Memory OLAP Database §  Designed for Writeback §  Supports Real-Time Calculations §  Secure and Centralized §  Integrated with Excel, Cognos BI

IBM Cognos TM1 Solutions

Ø  Compensation Management Ø  Sales Performance Management Ø  Staffing Optimization Ø  Training Certification Ø  Intranet Employee Portal

Ø  Retail Sales Analysis Ø  New Product Planning Ø  Customer Profitability Ø  Customer Churn Analysis

Ø  Financial Consolidations Ø  Financial Reporting Ø  Planning Ø  Driver Based Budgeting Ø  Rolling Forecasts Ø  Risk Analysis

Ø  Demand Analysis Ø  Inventory Optimization Ø  Logistics Planning Ø  Product Profitability Ø  Production Planning

Financial Operations

Workforce Customer

Complete Performance Management Applications •  Read/Write Planning & What-If •  Ad Hoc Analysis and Formatted Reporting

TM1 Terminology §  Dimension – A collection of Elements and their relationships

to one another (e.g. Chart of Accounts). §  Cube – A collection of dimensions whose intersections (cells)

store data (e.g. Sales). §  Element – A single member within a dimension (e.g. one

account from the Chart of Accounts). §  Attribute – A piece of information that describes an element

(e.g. an inactive flag on an account). §  Subset – A Subset of Elements within a Dimension (e.g. all

revenue accounts) §  Rule – A formula that describes business logic §  Process – An ETL script that can import, export, and

transform data.

TM1 Interfaces

§  TM1 Applications / Workflow §  Cognos Insight §  TM1 Perspectives (Excel Add-In) §  TM1 Web §  TM1 Architect §  Performance Modeler §  Cognos BI

TM1 Applications / Workflow

TM1 Applications / Workflow

When to use? Who?

ü  Data input ü  Approvals and Rejec-ons ü  Email No-fica-on ü  Status Repor-ng

ü  End Users in a Workflow Process


ü  Web-‐based / Thin Client for End Users ü  Performance Modeler for Admin

ü  None

TM1 Web

TM1 Web

When to use? Who?

ü  Data Input ü  Ad-‐Hoc Analysis ü  Repor-ng

ü  End Users ü  Power Users


ü  Web-‐Based / Thin Client ü  None

TM1 Perspectives (Excel Add-In)

TM1 Perspectives (Excel Add-In)

When to use? Who?

ü  Template and Report Crea-on ü  Data Input ü  Repor-ng

ü  Template Authors ü  End Users ü  Power Users


ü  Excel Add-‐In ü  Requires Client Installa-on

ü  Excel

CAFÉ (Excel Add-In) Cognos Analysis for Excel

CAFÉ (Excel Add-In)

CAFÉ (Excel Add-In)

When to use? Who?

ü  Ad-‐Hoc Analysis ü  Slice and Dice ü  Data Entry

ü  End Users ü  Power Users


ü  Excel Add-‐In ü  Requires Client Installa-on

ü  Excel

TM1 Architect

TM1 Architect

When to use? Who?

ü  Modeling ü  Impor-ng and Expor-ng Data ü  Managing Security

ü  Modelers ü  Administrators


ü  Thick Client -‐ Windows ü  Requires Client Installa-on

ü  TM1 Modeling

Performance Modeler

Performance Modeler

When to use? Who?

ü  Modeling ü  Impor-ng and Expor-ng Data ü  Managing Security ü  Workflow Applica-on Design

ü  Modelers ü  Administrators


ü  Thick Client ü  Can be launched from the Web

ü  TM1 Modeling

CUSTOMER EXPERIENCE

THE DATA-DRIVEN Selling SPSS:

What does SPSS stand for? Statistical. Package. for the Social. Sciences.

What does SPSS stand for? Statistical. Package. for the Social. Sciences.

It should be SPFC: Solving. Problems. For. Customers.

Purpose •  Introduce focus on

Customer Experience •  Spin the SPSS story

from the Customer Experience perspective

•  Organize SPSS solutions into the customer experience theme

•  Define Predictive Analytics (PA) market

Studies have shown that the objects people chose to surround themselves with are indicative of their personalities. Gosling, Sam. Snoop: What Your Stuff Says About You. New York: Basic, 2008. Print.

PERCEPTION. IS. REALITY.

Questions to answer: •  How can we use data to alter the

customer experience? •  How does data impact brand perception? •  How do customer interact with data? •  How is data integrated in the shopping

experience?

OF CUSTOMERS’ EVERYDAY POTENTIAL INTERACTION WITH AN ORGANIZATION’S DATA

EXAMPLE

Meet Henry Hipster •  Received phone calls at 8pm •  Bank selling him accounts he has •  Service provider can’t remember him •  Customer service reps annoy him •  Receives Junk Mail •  Mailbox filled with SPAM NOW •  No-logo philosophy •  Obscure bands •  Whole-foods •  Hipster

Meet Serene Serena •  Trip to Bahamas •  Coupons on the way •  Exclusive product launches •  No problems with service provider •  Pizza remembers favorite order •  Issues resolved in advance •  Receives messages she wants

NOW •  Blogger •  Superstar •  Fan base •  Lucky

By effectively analyzing and utilizing:

Sell the SPSS solution as a data-centric way to improve the customer experience.

What is the Data-Driven Customer Experience?

Using data, data mining methods, and predictive analytics to: •  Create the best buying environment for your

customers. •  Facilitate communication with your customers

that is effective and consistent. •  Anticipate and predict customer needs.

Using data to improve the customer experience by focusing on…

Buying Environment Communication

Customer Needs

Categorizing and grouping SPSS solutions thematically so they are relevant to Enterprises.

Buying Environment

Digital Optimization

Preventing Fraud/Theft

Targeted Advertising

Market Basket

Analysis

Buying Environment

Digital Optimization Creating a better user

interface that improves the sales funnel.

Preventing Fraud/Theft

Creating a safe environment for customers that

encourages spending.

Targeted Advertising Creating personalized

message and content to increase impact.

Market Basket Analysis

Grouping relevant items to facilitate

increasing the single purchase price.

.

Communication

Customer Segmentation

Targeted Marketing

Text Analytics

Churn Analytics

Communication

Customer Segmentation Understanding customer micro-niches to better align products

with customer interests

Targeted Marketing Sending customers they want to see in

their preferred deliver methods to

increase engagement.

Text Analytics Understanding customer sentiment

in free form text to create an informed communication strategy.

Churn Analytics Identify customers at risk of leaving

to resolve customer issues

faster.

.

Customer Needs

Recommendation Engine

Promotional Analytics

Store Planning

Merchandising Analytics

Customer Needs

Recommendation Engine Making targeted recommendations to facilitate customers finding what they

are looking for.

Promotional Analytics

Identifying ROI for promotions in order to continue offering customer discounts.

Store Planning Identifying new markets in order to provide services to new potential

customers.

Merchandising Analytics

Anticipating demand in order to stock inventory levels appropriately.

.

Buying Environment

Communica7on Customer Needs

Digital Op-miza-on Preven-ng Fraud/TheF Targeted Adver-sing Market Basket Analysis

Customer Segmenta-on Targeted Marke-ng Text Analy-cs Churn Analy-cs

Recommenda-on Engine Promo-onal Analy-cs Store Planning Merchandising Analy-cs

Summary Reference Tables of SPSS Solutions Categories

Big Data Analy-cs Market Size by Business Category

Most Opportunity for Analysis

Gartner Report

Predictive Analytics Market – Global Industry Analysis from Transparency Market Research

•  PA predicted to grow from $2.08B today to $6.54B in 2019 globally

•  Banking, FinServ, Insurance sectors largest market share

•  Retail and manufacturing expected to grow faster than any other segment

•  Fast growing consumer driven digital data •  Need to extract strategically cri7cal informa7on •  Rise in fraud, payment defaults, over or under stock inventory levels, stringent regula-ons

Predictive Analytics Market – Global Industry Analysis from Transparency Market Research

•  Companies to adopt predictive models to gain futuristic insights

•  Leading segments: customer intelligence, fraud and security, campaign management – accounted for 50% market revenue 2012

•  Target Departments: sales and marketing, customer and channel management, operations and workforce management, finance and risk management.

•  Finance and Risk 40.9% of revenue share in 2012.

QUESTIONS? THANK YOU

IBM's Business Analytics Portfolio for Training Purposes

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