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[Webinar] How Big Data and Machine Learning Are Transforming ITSM

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[Webinar] How Big Data and Machine Learning Are Transforming ITSM

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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.

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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[Webinar] How Big Data and Machine Learning Are Transforming ITSM

  1. 1. + 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. 2. Today’s Presenters 2 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. 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 • 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!
  4. 4. Agenda 4  An Overview of Big Data & Machine Learning  Big Data & Machine Learning for ITSM  Q&A
  5. 5. 5 Key Drivers of Machine Learning
  6. 6. 6 Lawrence O. Hall – Guest Speaker Distinguished Professor Dept. of Computer Science & Engineering University of South Florida
  7. 7. 7 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
  8. 8. 8 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.
  9. 9. 9 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
  10. 10. 10 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
  11. 11. 11 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?
  12. 12. 12 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
  13. 13. 13 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
  14. 14. 14 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
  15. 15. 15 Decision Tree Example For our data, we need features. Assume we want to decide whether to play tennis and have historical data…
  16. 16. 16 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
  17. 17. 17 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
  18. 18. 18 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
  19. 19. 19 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
  20. 20. 20 Decision Tree Example - Best First Two Tests Sunny Overcast Rainy 3 - Yes High Normal 4 - No 1 - Yes 3-Yes 2 - No Outlook Humidity
  21. 21. 21 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
  22. 22. 22 • Now you know something of big data • You have heard of some machine learning success • You can build a simple decision tree!
  23. 23. 23 John Prestridge VP of Marketing and Product Strategy
  24. 24. 24
  25. 25. 25 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
  26. 26. 26 Supporting the Digital Workplace Transition from being reactive to a proactive delivery of services that leverages a people-centric approach to empower employee effectiveness.
  27. 27. 27 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
  28. 28. 28 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. 29. DOMAIN MODEL MACHINE LEARNING Algorithms Regression Anomaly Detection Clustering Classification .... 29 Big Data + Machine Learning DATA Ticket History Knowledge Assets Interactions Usage Patterns …..
  30. 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. 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. 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 ....
  33. 33. 33 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
  34. 34. 34 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
  35. 35. 35 ITSM + MACHINE LEARNING www.sunviewsoftware.com/learn/machine_learning Learn More:
  36. 36. 36 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
  37. 37. 37 Q&A
  38. 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

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