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Hosted By:John PrestridgeVP of Marketing & Product StrategySunView Software

Guest Speaker:Lawrence O. HallDistinguished University ProfessorDept. of Computer Science& EngineeringUniversity of South Florida

How Big Data &Machine Learning are Transforming ITSM

Today’s Presenters

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John Prestridge - HostVP of Marketing and Product Strategy – SunView Software

Lawrence O. Hall – Guest SpeakerDistinguished ProfessorDept. of Computer Science & EngineeringUniversity of South Florida

Housekeeping

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• This webinar will be available shortly after its conclusion• Share this webinar and check out the supplemental resource kit for

machine learning and ITSM• Have a question regarding anything that is covered during this

webinar? Use the BrightTalk ‘Ask A Question’ window to submit your question to the webinar panel!

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 SpeakerDistinguished ProfessorDept. of Computer Science & EngineeringUniversity 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 AlgorithmsThere 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 NoHot High True NoMild High False NoCool Normal False YesMild High True No

Sunny

Overcast

Rainy

3 - Yes

3-Yes2 - No

Outlook

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Decision Tree Example -Best First Two Tests

Sunny

Overcast

Rainy

3 - Yes

HighNormal

4 - No1 - Yes

3-Yes2 - No

Outlook

Humidity

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Decision Tree Example -Final Tree

Sunny

Overcast

Rainy

3 - YesHumidity

High Normal

4 - No1 - Yes

TrueFalse

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 PrestridgeVP of Marketing and Product Strategy

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Supporting the Digital Workplace

BYOD

CLOUD

MOBILEWORKFORCE

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

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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 HistoryKnowledgeAssetsInteractionsUsage Patterns…..

Large ScaleData ProcessingEnvironment

90% of data today is machine generated

or people interactions

DOMAINMODEL

MACHINELEARNING

Algorithms Regression Anomaly Detection Clustering Classification ....

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Big Data + Machine Learning

DATA

Ticket HistoryKnowledgeAssetsInteractionsUsage Patterns…..

DOMAINMODEL

MACHINELEARNING

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Big Data + Machine Learning

DATA

Ticket HistoryKnowledgeAssetsInteractionsUsage Patterns…..

IncidentService RequestProblemChange…..Algorithms

Regression Anomaly Detection Clustering Classification ....

DOMAINMODEL

MACHINELEARNING

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Big Data + Machine Learning

DATA

Ticket HistoryKnowledgeAssetsInteractionsUsage Patterns…..

IncidentService RequestProblemChange…..Algorithms

Regression Anomaly Detection Clustering Classification ....

NEEDS: ITSM Expert Data Scientist Big Data Infrastructure Machine Learning Tools

DOMAINMODEL

MACHINELEARNING

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Big Data + Machine Learning

DATA

Ticket HistoryKnowledgeAssetsInteractionsUsage Patterns…..

INTELLIGENTFEATURES

Recommendation Engines Intelligent Search Predictive Analytics BOTS

API

Algorithms Regression Anomaly Detection Clustering Classification ....

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ITSM + Machine Learning

IntelligentFeatures

Predictive Analytics User Sentiment Score Change Risk Predict Problems

Intelligent Search Knowledge Curation Smart Notifications Contextual Search

BigData

MachineLearning

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 aproven 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_learningLearn 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

Thank You!If you would like to find out more visitwww.SunViewSoftware.com

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