Data and Analytics are fundamental to digital transformation, yet many companies are still under-utilizing them. To go full throttle, AI and automation technologies can be added across the full spectrum of your data journey to truly re-imagine processes and business models. Join Information Builders for this webinar on how AI: • Augments your traditional business intelligence and analytics systems • Minimizes manual inefficiencies with the way data is generated, collected, cleansed, and organized • Helps you realize substantial performance gains with use cases such as churn forecasting, predictive maintenance, supply chain planning, risk mitigation, and more

1. June 4th, 2020
Applying Artificial Intelligence
In All The Right Places
Aditya Sriram
Senior AI Strategist
Vince Deeney
Senior Director, Strategic Services

2. Agenda
Introduction to
Artificial Intelligence
Artificial Intelligence
Journey
Case Study
Common Pitfalls
Of AI

3. Introduction to
Artificial Intelligence

4. Myths vs Reality
Technology companies will be the main
beneficiary of AI
AI is already providing real value for
organizations applying AI in business
Senior leaders expect AI to reduce the
size of their workforce
AI is designed to complement personas
across an organization
AI can magically make sense of any and
all of your messy data
AI is not “load and go”, and the quality
of data is more important than the
algorithm
An organization requires Data
Scientists/ML experts and a huge
budget to use AI for business
applications
Many tools are increasingly available to
business users and don’t require large
investments to acquire
Myth Reality

6. Projected Revenue for AI (2016 – 2025)

7. The Difference between Then and Now
Practical
Faster
Computing
More
Data
95%
C-level executives believe that
data is an integral part of
forming business strategy.
- Experian, 2018
90%
Reduced cost when applying ML
for data cleansing, data
transformation, and deduplication.
- Stonebraker, Bruckner and
Ilhyas, 2013
Better
Algorithms

8. Common Data Governance Use-Cases
Anomaly
Detection
Metadata
Classification
Issue
Resolution
Automated
Data Profiling

9. Artificial Intelligence Overview
Artificial Intelligence uses
algorithm-based pattern
recognition to analyze
current and historical
data to make predictions
about future events
Monetize your investment in data
OPERATIONALIZE DATA BY BUILDING
THE RIGHT FOUNDATIONS FOR
ACTIONABLE OUTCOMES
BUSINESS OBJECTIVES/USE CASES
DATA-DRIVEN DECISION
BUSINESS INTELLIGENCE +
ARTIFICIAL INTELLIGENCE

10. AI Nomenclature
Intelligence/Learning = Finding new patterns in data

11. Machine Learning
Automated
Feature
Extraction
Raw Data
Clean Data Features
ML ModelResults
Pre-processing
Feature
Extraction
Training
Evaluation
80% to 90% Human Effort
Deep Learning

12. Data Science Business Process

13. Augmented Data Governance
AGGREGATE
REFINE
RECONCILE
RELATE
Unlimited Attributes
Trusted Data
Intelligent Matching
Build Relationships
EVOLVE
Integrate with existing Applications
& Data Warehouses
ACCOUNT 360
ASSET 360
CONSUMER 360
SUPPLIER 360
PRODUCT 360
VISUALIZE &
COLLABORATE
Personalized Views
ALIGN & ANALYZE RECOMMED & AUGMENT
Combine profiles with interaction used for
advanced analytics & machine learning
Write-back aggregate profile attributes for
operational context & segmentation
CONSUMPTION
IT, sales, marketing, &
other teams can consume
data per their business
goals
ORGANIZING DATA
INTELLIGENCE
INSIGHT

14. Artificial Intelligence
Journey

15. Define a
Strategic Goal
Understanding
the Data
Use-Case
Owner
Understand
Success
Metrics
Ethical and
Legal Issues
Technology &
Infrastructure
Skills &
Capacity
Change
Management
Organizational Process

16. Technology Process
Data Preparation Algorithm/Computation Visualize

17. Importance of Data Preparation

18. Learning Algorithms

19. V
i
s
u
a
l
i
z
e

20. Increase ROI using AI
Business Intelligence + Predictive Modeling = 145% ROI
Business Intelligence = 89% ROI
Artificial Intelligence Median ROI
Source: “Predictive Analytics and ROI: Lessons from IDC’s Financial Impact Study”
http://www.analyticalinsights.com/web_images/IDC-PredictiveanalyticsandROI.pdf
“Our organization is
under constant
pressure to lower the
amount spent to raise
a dollar. Artificial
Intelligence will never
pay back in time to
make a real impact on
our campaigns”

21. Common Pitfalls of
Artificial Intelligence

22. Organizational Challenges when Implementing AI
FAILING TO FOCUS ON A
SPECIFIC BUSINESS
INITIATIVE
FAILING TO
OPERATIONALIZE
MODEL
VALIDATION
INABILITY TO
FIND AI TALENT
85%
Gartner polls thousands of
CIOs around the world on
why AI projects will not
deliver
NOT HAVING
ENOUGH/RIGHT
DATA
- Refinitiv - Refinitiv
ANALYTIC
TOOLS

23. Driving ROI
Focusing on bottom-line
initiatives
Preparing Data
Evaluate the model
without over-evaluating
Deploying the results
Avoiding Pitfalls

24. Case Study

25. • Predicting B2B churn among their distributors such that they can proactively have a
retention strategy
• 3 phases: (a) who is likely to lapse, (b) what will customers purchase, and (c) what else are
customers interested in purchasing
Lipari Foods uses WebFOCUS Data Science to predict
B2B churn to identify at-risk distribution companies
Goal Strategy Outcome
To use WebFOCUS Data Science platform
to accurately identify and predict
distribution companies that are at-risk to
churn.
Lipari has gathered historical data,
approximately 10M records, across 9,000
customer locations which is used to
identify trends of distribution companies
(including product types, location data,
and sales data aggregated by period).
Using WebFOCUS Data Science, Lipari
developed a profile of at-risk distribution
companies using 20+ data features. The
application scores each distribution
company by predicting the likelihood of
churn .
Enables revisions to each distribution
company pathway based on risk of churn.
To proactively maximize retention of
these distribution companies, Lipari is
using WebFOCUS to visualize the churn
prediction by mapping the likelihood to
product types and other dimensions of
the dataset to monitor those distribution
companies more closely.

26. Common Use Cases
• Readmission Prediction
• Resource allocation
• Predicting diagnosis
• Pricing and risk
Health Care
• Predictive crime analysis
• Predict volume of
collision
• Congestion management
Government
• Lending cross-sell
• Forecasting default loan
• Profit/Revenue growth
• Customer segmentation
• Sales and marketing
campaign management
• Credit worthiness
Financial Services

27. Additional Reads
• “Machine Learning Yearning” – Andrew Yang
• “Data Science from Scratch: First Principles with Python” – Joel Grus
• “Thinking with Data: How to Turn Information into Insights” – Max Shron
• “Artificial Intelligence for healthcare” – Dolores Derrington
Interactive Python tutorial
• https://www.tutorialspoint.com/python/python_basic_syntax.htm
• https://www.w3schools.com/python/default.asp

28. Thank you
Aditya Sriram
Senior AI Strategist
Information Builders (Canada) Inc.
150 York Street, Suite 1000
Toronto, M5H 3S5
aditya_sriram@ibi.com
Vince Deeney
Senior Director, Strategic Service
Information Builders Inc.
2 Pennsylvania Plaza, New York,
NY 10121, United States
Vince_Deeney@ibi.com