As we move into a new era of ITSM computing, new big data and machine learning tools and methodologies are being developed to support IT staff by intelligently extracting insights and making predictions from the enormous amounts of data accumulated from the organization. According to Gartner, I&O leaders must take a comprehensive approach to incorporate advanced big data and machine learning technologies into their organizations or risk becoming irrelevant. But what exactly is big data and machine learning all about? How can you introduce these concepts into your existing Service Desk? Join USF’s distinguished Computer Science and Engineering Professor Lawrence Hall and SunView Software’s VP of Marketing and Product Strategy John Prestridge as they break down the fundamentals of big data and machine learning and provide real-world examples of the impact the technologies will have on ITSM.

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Hosted By:
John Prestridge
VP of Marketing & Product Strategy
SunView Software
Guest Speaker:
Lawrence O. Hall
Distinguished University Professor
Dept. of Computer Science
& Engineering
University of South Florida
How Big Data &
Machine Learning are Transforming ITSM

2. Today’s Presenters
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John Prestridge - Host
VP of Marketing and Product Strategy – SunView Software
Lawrence O. Hall – Guest Speaker
Distinguished Professor
Dept. of Computer Science & Engineering
University of South Florida

3. Housekeeping
3
• This webinar will be available shortly after its conclusion
• Share this webinar and check out the supplemental resource kit for
machine learning and ITSM
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webinar? Use the BrightTalk ‘Ask A Question’ window to submit
your question to the webinar panel!

4. Agenda
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 An Overview of Big Data & Machine Learning
 Big Data & Machine Learning for ITSM
 Q&A

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Key Drivers of Machine Learning

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Lawrence O. Hall – Guest Speaker
Distinguished Professor
Dept. of Computer Science & Engineering
University of South Florida

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What is Big Data?
Data that has a large:
• Volume: lots of records
• Variety: lots of different kinds of data
• Velocity: changing fast
• Or some combination of the three V’s

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Big Data
We Need New Ways to Analyze Data
Curating and storing lots
of data can be a
challenge when using
machine learning for
predictive analytics.

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Big Data Examples
• The friends network in Facebook
• Amazon history of purchases
• Records of cell phone calls, texts, or tweets
• History of all service requests in your company

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Big Data Examples
• There is velocity as posting is constant
• Evenings lead to more posts
• That cute cat picture gets posted…. A lot!
Consider image posts and shares on Facebook
Netflix records ratings of movies and shows by user

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What Do We Want to Know from Big Data?
• Amazon wants to suggest books you might buy
• Or, perhaps Amazon suggests related material based
of a past purchase of biking gloves…
• How do they do this?

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Machine Learning
• What is machine learning, data mining, and predictive
analytics?
• From data, preferably with some ‘class’ labels, a machine
learning algorithm can build a predictive model
• Amazon, has lots of users and products. If it can aggregate
what users have bought together (or over time), it can
suggest what you might like to buy

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Are All Those Cats The Same?
• If a learned model can recognize the same
image, Facebook and others who store
images can have just one linked copy
• There are now models that are nearly
perfect at matching the same thing

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Machine Learning/
Data Mining Algorithms
There are many algorithms and we only touch on a few
• Decision tree algorithms are fast to build and reasonably
accurate. Use a random forests ensemble for better accuracy
with a wisdom of crowds approach
• If you have big, labeled image data – Convolutional Neural
Networks, using deep learning, are really good
• Support Vector Machines let you project data into a higher
dimension (“kernel trick”) and then linearly separate them
• No labels but you want to group data? Try K-means or fuzzy
K-means clustering

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Decision Tree Example
For our data, we need features.
Assume we want to decide
whether to play tennis and
have historical data…

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Decision Tree Example -
Evaluating Weather Attributes
Outlook Temp Humidity Windy Play
Sunny Hot High False No
Sunny Hot High True No
Overcast Hot High False Yes
Rainy Mild High False Yes
Rainy Cool Normal False Yes
Rainy Cool Normal True No
Overcast Cool Normal True Yes
Sunny Mild High False No
Sunny Cool Normal False Yes
Rainy Mild Normal False Yes
Sunny Mild High True No
Overcast Mild High True Yes
Overcast Hot Normal False Yes
Rainy Mild High True No

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Decision Tree Example -
Evaluating Weather Attributes
Attribute Rules Errors Total
errors
Outlook Sunny  No 1/5 3/14
Overcast  Yes 0/4
Rainy  Yes 2/5
Outlook Temp Humidity Windy Play
Sunny Hot High False No
Sunny Hot High True No
Overcast Hot High False Yes
Rainy Mild High False Yes
Rainy Cool Normal False Yes
Rainy Cool Normal True No
Overcast Cool Normal True Yes
Sunny Mild High False No
Sunny Cool Normal False Yes
Rainy Mild Normal False Yes
Sunny Mild High True No
Overcast Mild High True Yes
Overcast Hot Normal False Yes
Rainy Mild High True No

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Decision Tree Example -
Evaluating Weather Attributes
Attribute Rules Errors Total
errors
Outlook Sunny  No 1/5 3/14
Overcast  Yes 0/4
Rainy  Yes 2/5
Temp Hot  No* 2/4 6/14
Mild  Yes 3/6
Cool  Yes 1/4
Humidity High  No 3/8 4/14
Normal  Yes 1/6
Windy False  Yes 2/8 4/14
True  No* 2/6
* indicates a tie
Outlook Temp Humidity Windy Play
Sunny Hot High False No
Sunny Hot High True No
Overcast Hot High False Yes
Rainy Mild High False Yes
Rainy Cool Normal False Yes
Rainy Cool Normal True No
Overcast Cool Normal True Yes
Sunny Mild High False No
Sunny Cool Normal False Yes
Rainy Mild Normal False Yes
Sunny Mild High True No
Overcast Mild High True Yes
Overcast Hot Normal False Yes
Rainy Mild High True No

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Decision Tree Example -
Best First Test
Temp Humidity Windy Play
Hot High False No
Hot High True No
Mild High False No
Cool Normal False Yes
Mild High True No
Sunny
Overcast
Rainy
3 - Yes
3-Yes
2 - No
Outlook

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Decision Tree Example -
Best First Two Tests
Sunny
Overcast
Rainy
3 - Yes
High
Normal
4 - No
1 - Yes
3-Yes
2 - No
Outlook
Humidity

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Decision Tree Example -
Final Tree
Sunny
Overcast
Rainy
3 - Yes
Humidity
High Normal
4 - No
1 - Yes
True
False
2 - No 3 - Yes
Outlook
Windy

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• Now you know something of big data
• You have heard of some machine learning success
• You can build a simple decision tree!

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John Prestridge
VP of Marketing and Product Strategy

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Supporting the Digital Workplace
BYOD
CLOUD
MOBILE
WORKFORCE
SHADOW IT
IOT
 Volume , Velocity and Variety of Requests
 Business will expect more apps, delivered
more quickly, with consumer-like support
 Do more with less
SELF-SERVICE

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Supporting the Digital Workplace
Transition from being reactive to a
proactive delivery of services that
leverages a people-centric approach to
empower employee effectiveness.

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Key Opportunity
-
By 2019, IT service desks utilizing
machine-learning enhanced
technologies will free up to 30%
of support capacity.*
*Apply Machine Learning and Big Data at the IT Service Desk to Support the Digital Workplace
February 2016 Analyst(s): Colin Fletcher | Katherine Lord

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Big Data + Machine Learning
DATA
Ticket History
Knowledge
Assets
Interactions
Usage Patterns
…..
Large Scale
Data Processing
Environment
90% of data
today is machine generated
or people interactions

29. DOMAIN
MODEL
MACHINE
LEARNING
Algorithms
Regression
Anomaly Detection
Clustering
Classification
....
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Big Data + Machine Learning
DATA
Ticket History
Knowledge
Assets
Interactions
Usage Patterns
…..

30. DOMAIN
MODEL
MACHINE
LEARNING
30
Big Data + Machine Learning
DATA
Ticket History
Knowledge
Assets
Interactions
Usage Patterns
…..
Incident
Service Request
Problem
Change
…..Algorithms
Regression
Anomaly Detection
Clustering
Classification
....

31. DOMAIN
MODEL
MACHINE
LEARNING
31
Big Data + Machine Learning
DATA
Ticket History
Knowledge
Assets
Interactions
Usage Patterns
…..
Incident
Service Request
Problem
Change
…..Algorithms
Regression
Anomaly Detection
Clustering
Classification
....
NEEDS:
 ITSM Expert
 Data Scientist
 Big Data Infrastructure
 Machine Learning Tools

32. DOMAIN
MODEL
MACHINE
LEARNING
32
Big Data + Machine Learning
DATA
Ticket History
Knowledge
Assets
Interactions
Usage Patterns
…..
INTELLIGENT
FEATURES
 Recommendation Engines
 Intelligent Search
 Predictive Analytics
 BOTS
A
P
I
Algorithms
Regression
Anomaly Detection
Clustering
Classification
....

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ITSM + Machine Learning
Intelligent
Features
Predictive Analytics
 User Sentiment
 Score Change Risk
 Predict Problems
Intelligent Search
 Knowledge Curation
 Smart Notifications
 Contextual Search
Big
Data
Machine
Learning
Recommendation Engines
 Resolution Suggestions
 Level 1 Ticket Completion
 Intelligent Routing
“Better Decisions” “Faster Resolutions”
“Improved Self-Service”
“Engaged Users”
BOTS
 Intelligent Autoresponder
 Self-Service Virtual Assistant

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Summary
 Big Data is here and Machine Learning is a
proven technology
 Need proactive delivery of services to support the
digital workplace
 Invest in big data, machine learning, and other AI
technologies to transform ITSM

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ITSM + MACHINE LEARNING
www.sunviewsoftware.com/learn/machine_learning
Learn More:

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Get Connected
Do you have any personal experience or additional
questions regarding the topics we covered today?
Get into the discussion via email:
• Lawrence Hall: lohall@mail.usf.edu
• John Prestridge: jprestridge@sunviewsoftware.com

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Q&A

38. Thank You!
If you would like to find out more visit
www.SunViewSoftware.com
LinkedIn.com/companies/sunview-software-inc-
Twitter.com/SunViewSoftware
Facebook.com/SunViewSoftware