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Forward-Looking BI

Fern Halper

TDWI Director of Research, Advanced Analytics

September 9, 2014

@fhalper

2

Speakers

Fern Halper Research Director for

Advanced Analytics,

TDWI

David Clement Product Marketing Manager,

BI and Predictive Analytics,

IBM

Agenda

• Industry trends for predictive analytics

• Use cases for predictive analytics and forward

looking BI

• Getting started

3

Predictive analytics

4

A statistical or data mining solution

consisting of algorithms

and techniques that can be used

on both structured

and unstructured data to determine outcomes.

Popular predictive analytics techniques

• Decision tree

• Regression

• Clustering

5

Classification, segmentation, association

Market trends shaping forward-looking BI

6

1. Democratization

2. Consumability

3. New data sources

4. New platforms

New Users Driven By EASE OF

USE

1. Democratizing BI

To extend the deployment of BI and analytics

tools to more users in the organization

7

Democratization

8

0% 5% 10% 15% 20% 25% 30%

70-100%

50-70%

30-50%

10-30%

<10%

Don't know

70-100% 50-70% 30-50% 10-30% <10% Don't know

Series1 15% 16% 13% 24% 24% 8%

What percent of your organization's employees is using BI and/or analytics tools on any platform?

31%

2. Consumability

Able to be used…

More accessible results…

9

Ease of Use

• Point and click visualization

• Software suggests models

• Collaboration/Sharing

10

Easy for Individual

The business analyst will build models

11

(source: TDWI 2014 Best Practices Report on Predictive Analytics)

Embedded Predictive Analytics

12

An example:

model

3. Data sources evolving

13

72%

76%

61%

4. Platforms are becoming popular

14

The Result

• Platforms offered:

– Relatively easy to use

– Incorporate disparate data types

– Extend the functionality of BI to predictive

analytics

15

Use case 1: Predict product levels

• Reactive: Tracking product purchases

To

• Proactive: Predicting product inventory

16

Reactive to proactive

• Reactive:

– Query to produce

reports and

dashboards

• Proactive

– Predict product levels

– Optimize inventory

17

Use case 2: Predict churn

• Reactive: Knowing when customers drop a

service

To:

• Proactive: Predicting when a customer will

drop a service

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• Reactive:

– Query: Number of

customers dropped per

month

– Query: Dollars lost per

year

• Proactive

– Outcome of interest:

Dropped vs. Retained

– Characteristics of

customers with that

profile

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Use case 3: Identify fraud

• Reactive: Knowing how much fraud is

happening

To:

• Proactive: Predicting when fraud will occur

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• Reactive:

– Query: Dollars of

revenue lost

– Query: Number of

fraud attempts

– Query: Number of SIU

cases per year

• Proactive

– Outcome of interest:

• Fraud vs. No Fraud

– Characteristics of

customers with that

profile

21

Getting Started

• Pick a project with a metric

• Develop a POV

• Build your skills

22

Poll Question

• Are you performing predictive analytics today? – Yes, we are doing this today

– No, we are not doing this today, but are planning to do it this year

– No, we haven’t even started our predictive analytics efforts yet,

but we’re interested in predictive analytics

– No plans

– Don’t know

23

© 2014 IBM Corporation 24

Predictive Business Intelligence Forward-Looking Business Intelligence Predictive Business Intelligence Forward-Looking Business Intelligence

© 2014 IBM Corporation

Forward-Looking Business Intelligence

Know the past, understand the present,

shape your future

David Clement

IBM BI and Predictive Analytics

Product Marketing Manager

@DWClement


Predictive Business Intelligence Forward-Looking Business Intelligence

Key trends are fueling the need and urgency for analytics

The emergence of big data analytics

1

35% of businesses use big

data for business

advantages.

Increasing consumer expectations

2

84% of consumers rely on

social networks for

purchase decisions.

Accelerating pressure to do more with less

3

32% is the higher return on

invested capital for

those organizations

using advanced

analytic software.



Challenges faced by business units

“We need to understand why

events are happening within our

operations and allow managers to

respond faster.”

“We need to better measure

business risk and reduce losses in

order to provide superior pricing

and increase profits.”

“We need to understand our

customers and prospects as a whole

and at the individual level to better

target our services and products.”



With business intelligence and predictive analytics from IBM, transform data into useful

insights. Help make confident decisions and improve operational efficiencies with historic,

current and predictive views of your business.

Predictive analytics enhances the value of deploying BI reports, dashboards

and scorecard capabilities across the enterprise with visibility into your

businesses past, present and future

Leverage data: Analyze information in any volume, combination and complexity

Provide insights: Use a trusted platform for

delivering forward looking business information

Make confident decisions: Complete

visibility into your business with

predictive analytics

Outperform expectations: Transform your business from a reactive operation to a proactive market leader



• Understand the past and current state of your business, as well as future outcomes and their drivers

• Model your existing historical data to give you a guideline for future business behaviors

• Optimize business outcomes by tying predictive measures against

operation processes

• Deploy forward-looking business intelligence information on

desktops, browsers and mobile devices

• Provide users of different skill levels with the analytical capabilities

that can satisfy the needs of self-service data analysis

• Share results broadly from within a single BI interface,

extending data-driven insights across departments and into

frontline applications

This solution is designed to:

Forward-looking business intelligence from IBM helps you

overcome these challenges by validating business goals and reducing

the risk of bad decisions



• IBM® Cognos Business Intelligence

• IBM® SPSS Modeler

• IBM® SPSS Statistics

• IBM® SPSS Collaboration and

Deployment Services

HCDE enhances student attendance, behavior and performance by turning big data into insight

13.6% increase in graduation rates since solution was implement

Solution Components

Business Challenge: HCDE had already taken the first steps in understanding the

signs that a student is at risk of under-performing – or even dropping out of the

school system completely. Now, it wanted to transform its ability to deliver this

information to teachers, social workers and others – giving them the right data to

make daily decisions that will help their students achieve a brighter future. The

Solution: HCDE chose to build on its existing predictive analytics solutions by

enhancing its reporting capabilities using business intelligence.

“We see analytics as an ongoing journey. We are constantly working to introduce

new capabilities. The first phase was predictive modeling of student performance.

Then we moved on to giving all 3,600 teachers and administrators access to that

student performance data via dashboards on their iPads or Android tablets. And

now we’re using text analytics and data mining techniques to look at professional

development too.”

- Dr. Kirk Kelly, Director of Accountability and Testing at HCDE

Top 5% in the Tennessee school systems

for performance

Top 10% in the Tennessee school systems

for progress, growth rate



• IBM Cognos® Business Intelligence

• IBM Cognos TM1®

• IBM SPSS® Modeler

Elie Tahari harnessed predictive technologies to match production with customer demand

<2.5% The variance between

predictions and actual

items sold

Solution components

Business Challenge: Assembling the information needed to make key decisions

was an arduous and time-consuming task. For store replenishment decisions, the

lack of granularity in the reports made it impractical for managers to fine-tune the

product and size mix they shipped to each customer or outlet based on differences

in sales patterns from store to store or region to region.

The Solution: Business analytics tools now provide real-time insight, predictive

analysis, and detailed planning capabilities. Predictive analytics enables highly

accurate production planning, lower-cost logistics and more efficient inventory

management

“The ability to look four months into the future and know what our inventory levels

need to be on a weekly basis is absolutely key to our success... It allows us to

adjust production to a level that reduces our exposure and still gives us the ability

to supply our customer with close to 100 percent of their orders."

—Nihad Aytaman, director of business applications, Elie Tahari

30% The reduction in logistics costs

when addressing product shipping

Reduce risk Monitor production and inventory,

reducing exposure while

maintaining orders



Why IBM?

31

Unified workspace: Forward-looking business intelligence provides

a single vendor solution that integrates world-class capabilities

Integration: Forward-looking business intelligence provides optimal

data integration between past views of data and marketplace-leading

predictive technologies

Analyze relevant data: Forward-looking business intelligence

provides simple interfaces to ease the process of exploring all data

Platform: A purpose-built enterprise-class platform supports robust

metadata capabilities, advanced security and the ability to plug in

new solutions as the business grows and the needs of

users mature



What to consider next: Visit AnalyticsZone.com and take a look at our Communities page • Try trial versions of our software in our predictive community • Learn about your analytics quotient in our Analytics Center of Excellence (ACE) community • Check out the extensive set of visualizations that come with IBM Cognos 10 – look at our

Visualizations community

http://www.analyticszone.com/



33

Thank you.

© 2013 IBM Corporation

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Wednesday, October 1, 2014

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https://ibm.biz/BdFrHJ

35

Questions?

36

Contact Information

• If you have further questions or comments:

Fern Halper

[email protected]

David Clement

[email protected]

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