The Risks and Rewards of AI
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The Risks and Rewards of AI: Tomorrow’s IT Operations and Business Process Strategy
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The Risks and Rewards of AI
- 1. © 2 0 2 0 S P L U N K I N C . © 2 0 2 0 S P L U N K I N C . The Risks and Rewards of AI: Tomorrow’s IT Operations and Business Process Strategy
- 2. During the course of this presentation, we may make forward‐looking statements regarding future events or plans of the company. We caution you that such statements reflect our current expectations and estimates based on factors currently known to us and that actual events or results may differ materially. The forward-looking statements made in the this presentation are being made as of the time and date of its live presentation. If reviewed after its live presentation, it may not contain current or accurate information. We do not assume any obligation to update any forward‐looking statements made herein. In addition, any information about our roadmap outlines our general product direction and is subject to change at any time without notice. It is for informational purposes only, and shall not be incorporated into any contract or other commitment. Splunk undertakes no obligation either to develop the features or functionalities described or to include any such feature or functionality in a future release. Splunk, Splunk>, Data-to-Everything, D2E, and Turn Data Into Doing are trademarks and registered trademarks of Splunk Inc. in the United States and other countries. All other brand names, product names, or trademarks belong to their respective owners. © 2020 Splunk Inc. All rights reserved. Forward- Looking Statements © 2 0 2 0 S P L U N K I N C .
- 3. © 2 0 2 0 S P L U N K I N C . Head oaf IT Market Groupe UK&IE Dr. Siyka Andreeva IT Markets Strategist EMEA Alex Afflerbach
- 4. © 2 0 2 0 S P L U N K I N C . +416% increase in share of CEOs in EMEA who expect global economic growth to ‘decline’ “Despite billions of dollars of investment and priority positioning on the C-suite agenda—the gap between the information CEOs need and what they get has not closed in the past ten years.” 0% 10% 20% 30% 40% 2019 top ten threats
- 5. © 2 0 2 0 S P L U N K I N C . *PwC “22nd Annual Global CEO Survey” , 2019 80% The share of CEOs in Western Europe planning ‘operational efficiencies’ to drive revenue growth Faced with the new realities,organisations are turning inward to drive revenue growth 0% 20% 40% 60% 80% 100% Operational efficiencies Launch a new product or service Enter a new market Collaborate with entrepreneurs… Activities planned for the next 12 months to drive revenue growth
- 6. © 2 0 2 0 S P L U N K I N C . *PwC “22nd Annual Global CEO Survey” , 2019 “One of the more striking findings in this year’s survey is the fact that the ‘information gap’ — the gap between the data CEOs need and what they get — has not closed in the ten years since we last asked them these questions.”
- 7. © 2 0 2 0 S P L U N K I N C . recognize that they simply don’t have the capability to use the data they have to make optimized decisions 54% Lack of analytical talent 51% Data siloing 50% Poor data reliability Source: PwC 22nd Annual Global CEO Survey CEOs
- 8. © 2 0 2 0 S P L U N K I N C . *PwC “22nd Annual Global CEO Survey” , 2019 “Majority of CEOs believe AI will have a larger impact than the internet revolution”
- 9. © 2 0 2 0 S P L U N K I N C . “We are now moving into the world of anticipative computing. We’re not only gathering data in real time, but also anticipating the data to come. You can tell what’s likely to happen in the next 30 seconds. And if you can predict it in that time, that’s all the time you need to prevent it or make use of it.” *PwC “22nd Annual Global CEO Survey” , 2019 –Natarajan Chandrasekaran Chairman, Tata Sons, One of the Largest Enterprises in South Asia
- 10. © 2 0 2 0 S P L U N K I N C . “To help unlock internal growth potential in their organisations, chief executives are paying close attention to emerging digital technologies such as AI. As noted, the prize for getting this right is immense. PwC estimates US$15.7 trillion in global GDP gains from AI by 2030.“ –Bob Mortiz Global Chairman, PwC *PwC “22nd Annual Global CEO Survey” , 2019
- 11. © 2 0 2 0 S P L U N K I N C . Despite this bullish view, most organisations have not introduced AI initiatives *PwC “22nd Annual Global CEO Survey” , 2019
- 12. © 2 0 2 0 S P L U N K I N C . About AI
- 13. © 2 0 2 0 S P L U N K I N C . Should we fear AI? Should We Fear AI?
- 14. © 2 0 2 0 S P L U N K I N C . “come on in” “Do not cross”
- 15. © 2 0 2 0 S P L U N K I N C . James Bridle, The artist using ritual magic to trap self-driving cars Do not cross! Maybe not… Should We Fear AI?
- 16. © 2 0 2 0 S P L U N K I N C . "Google Maps Hack," artist Simon Wecker used 99 phones to fake a Google Maps traffic jam Definitely not… Should We Fear AI?
- 17. © 2 0 2 0 S P L U N K I N C . AI VS ML VS Deep Learning
- 18. © 2 0 2 0 S P L U N K I N C . Humans are good at learning, but we get lost in volume and details… Why use AI/ML?
- 19. © 2 0 2 0 S P L U N K I N C . AI+ML: • $2.6T in value by 2020 in Marketing and Sales • Up to $2T in manufacturing and supply chain planning • $2B in risk • $2B in service operations • $1B in product dev $3.9T: • Business value created by AI in 2022 $77.6B: • Worldwide spending on cognitive and AI systems in 2022 Value Creation
- 20. © 2 0 2 0 S P L U N K I N C . Artificial Intelligence Machine Learning Deep Learning Engineering of making Intelligent machines and programs (the name of the whole knowledge field) Ability to learn without being explicitly programmed (learning form experience) Learning based on Deep Neural Network (self-educatingmachines) Input Feature extractio n Classificati on Car Input Feature extraction & classification Car
- 21. © 2 0 2 0 S P L U N K I N C . Machine Learning Types of Machine Learning (ML) Supervised Unsupervised Reinforcement Task Driven • Makes machine learn explicitly • Predict outcomes • Resolves classification & regression problems Data Driven • Machine understands data • Identifies patterns, clusters… • Evaluation is qualitative or indirect Reinforcement Learning • Learn from mistakes • Machine learns how to act in a certain environment • Rewards based learning Inputs training Outputs Inputs Outputs Inputs rewards Outputs
- 22. © 2 0 2 0 S P L U N K I N C . AI/Machine Learning Examples & Use Cases Retail Marketing Telco Finance Demand forecasting Recommendation engines & targeting Customer churn Risk analysis Supply chain optimization Social Media Analysis Anomaly detection Credit scoring Market segmentation and marketing AD optimization Preventative maintenance Fraud Examples Sound Text Time Series Image Voice recognition (UX/UI, Automotive, Security, IoT) Sentiment Analysis (CRM, Social Media…) Log Analysis (Data Centers, ITOps, Security, Finance…) Image Search (Social Media…) Sentiment Analysis (CRM…) Augmented search (Finance…) Predictive Analysis (IoT, ITOps, Hardware manufacturer…) Machine Vision (Aviation, Automotive…) Fraud detection, latent audio artifacts (Finance…) Fraud detection (Finance, Insurance…) Business Analytics (Accounting, Gov, Finance…) Photo Clustering (Telecom, Handset makers…) Use cases “Hey Alexa
- 23. © 2 0 2 0 S P L U N K I N C . Why Use ML? Fraud Detection Catching obvious fraudulent scenarios Long-term processing Rule-based fraud detection ML-based fraud detection Requires much manual work to enumerate all possible detection rules Multiple verification steps that harm user experience Real-time processing Long-term processing Automatic detection of possible fraud scenarios Reduced number of verification measures
- 24. © 2 0 2 0 S P L U N K I N C . Data Acquisition Interpretation of results Time and resources Has no creativity ML CONs “GIGO – Garbage In, Garbage Out – is a saying that’s been around since the early days of computing. But in the age of artificial intelligence, machine learning, and data quality, that old adage is more relevant than ever” A.K.A “Rubbish In – Rubbish Out – RIRO”
- 25. © 2 0 2 0 S P L U N K I N C . ML PROs • Easily identifies trends and patterns (detect the unseen) • Predicts future outcomes • Reduces noise (events, alerts…) • No human intervention needed (or limited) • Continuous improvement • Handling multi-dimensional and multi-variety data • Wide applications • Rational and Accurate decision maker • Accurate decision making • Selfless with no breaks
- 26. © 2 0 2 0 S P L U N K I N C . Machine Learning Applied to IT Splunk for AIops
- 27. © 2 0 2 0 S P L U N K I N C . Drive new revenue Launch new products Improve Business process Meet SLAs Reduce app Time to Market Secure my organization Move to predictive / proactive IT Get full-stack Observability Service Manager Product Owner CISO DevOps Process Engineer Operations Manager NOC COO Operational efficiencies Improve analytical skills Break data silos Improve data reliability Leverage AI CEO IT *Splunk Inc., “State of Dark Data Report” , May 2019 of organizations report that the majority of their data is still dark* 60% Unanalyzed | Unowned | Uncaptured | Untapped
- 28. © 2 0 2 0 S P L U N K I N C . IT Infrastructure is Riddled with Dark Data Dev / Apps Cloud Office Backup/Dr Remote Security Storage Network Servers Facility
- 29. © 2 0 2 0 S P L U N K I N C . DEV Can Also be Riddled with Dark Data
- 30. © 2 0 2 0 S P L U N K I N C . Online Services Networks Security Call Detail Records Web Services Telecoms Web Clickstreams Online Shopping Cart Smartphones and Devices Custom Applications Energy Meters Storage Servers GPS Location RFID Databases Messaging Firewall APM Tracing Social Media Containers Turn Data Into Doing To Everyone Drive new revenue Launch new products Improve Business process Meet SLAs Reduce app Time to Market Secure my organization Move to predictive / proactive IT Get full-stack Observability Service Manager Product Owner CISO DevOps Process Engineer Operations Manager NOC COO ML
- 31. © 2 0 2 0 S P L U N K I N C . Realtime Cause & Effect Infrastructure Cloud Networks Security API WEB Smartphones and Devices Custom Applications Storage Servers DB APM Containers APP logs Syslogs APP TraditionalITOps Monitoring BIZ Call center Revenue NPS Customer retention Funnel Exec MBO’s Business-value Monitoring Joining Data from all ‘Altitudes’ See the transactions See the users See the value See the systems
- 32. © 2 0 2 0 S P L U N K I N C . DATA Online Services Networks Security Call Detail Records Web Services Telecoms Web Clickstreams Online Shopping Cart Smartphones and Devices Custom Applications Energy Meters Storage ServersGPS Location RFID DatabasesMessaging Firewall APM Tracing Social Media Containers MACHINE LEARNING “data scientist in a box” ITOPS | DEVOPS SECURITY BUSINESS ANALYTICS | IOT Custom dashboards Report & analyze Monitor and alert Developer Platform Ad hoc search SPLUNK PLATFORM On-prem or cloud SPLUNKBASE 2000+ Free Apps/add-ons Splunk ML toolkit “bring your own algorithms”
- 33. © 2 0 2 0 S P L U N K I N C . Where Does Splunk Fit? Got Busy, Got Complex, Got Expensive, More Failure Agile/Superior CXAlways On Simplify and promote IT Capability AI OPS Event ManagementApplication Management Incident Management Got Busy, Got Complex, Just Failed Workflow DevOps Automation Business Intelligence Cloud Monitoring Database Monitoring Application Monitoring System Monitoring VM/Container Monitoring Storage Monitoring Mobile App Monitoring Windows Monitoring Networks Monitoring Social Media Monitoring Simple and frictionless routes to revenue Very high availability of services Anticipate and meet customer needs before they know it. DIGITAL TRANSFORMATION Splunk > Data Aggregation , Search and Investigate Splunk > Service Intelligence, Business Flow, AI Ops
- 34. © 2 0 2 0 S P L U N K I N C . Machine Learning ToolkitPredictions Real-time Event ClusteringAdaptive ThresholdsAnomaly Detection • Deviation from past behavior • Deviation from peers (Multivariate or Cohesive Anomaly Detection) • Unusual change in features • Predict service health score, churn • Capacity planning, trend forecasting • Detecting influencing entities • Early warning - predictive maintenance • Identify peer groups • Event correlation • Reduce alert noise • Behavioral analytics Solving Problems With Machine Learning • Move form “working/broken” thresholds to “normal/abnormal” • Baseline normal operations and adapt thresholds dynamically • Codeless, step-by-step ML • Integrates with open source algorithms • Launch inside any Splunk search / query pipeline Requires Splunk and analytics expertise Reduce Noise and Remove False Positives Prevent Service degradation entirely and return time to the business Extend
- 35. © 2018 SPLUNK INC. How to find a needle in multiple haystacks? (chooseyourtool) Network? Database? Middleware? Hardware? Wrong command? Connection? Apache? VM? Mainframe? Load balancer?Wrong code released? Collect ALL data • Collect from all silos • Data in original raw format • Add open sources apps to ingest data on the fly • Schema on the fly • Dynamic thresholding • Realtime correlation Clustering & aggregation • Real time event clustering/correlation • Reduce alert noise • Behavioural analytics • Deduplication Add context • Measure / report on indicators that matters • Add service / business context • Add actionable information to detection Salessso Claims Anomaly detection • Catch issues that thresholds cannot • Reduce event clutter • Deviation from past behaviour • Deviation from peers • Unusual change in features Assisted deep dive investigation • Root cause analysis • Powerful & easy to use search & investigate language ? Predictive Analytics • Predict service health • Predict events • Trend forecasting • Detect influencing entities • Early warning of failure 70% to 90% Reduction in investigation time 15% to 45% Reduction in high priority incidents 67% to 82% Reduction in business impact
- 36. © 2 0 2 0 S P L U N K I N C . Machine Learning Applied to IT Customer examples using ML
- 37. © 2 0 2 0 S P L U N K I N C . Needed to pare down thousands of alerts and events from many silos (applications, security, network…) Needed real-time correlation and rule engine to automate event handling “There are days when you get a flood of events; Splunk ITSI prioritizes the events, gives you insight into not only that this is broken but what’s been affected right as you look at the alert screen.” Don Mahler, Director of Performance Management, Leidos 20 management systems 120 IT services 240 Locations 5000 Daily alerts 50 Tickets -97% event noise
- 38. © 2 0 2 0 S P L U N K I N C . TransUnion helps businesses manage risk while also helping consumers manage their credit, personal information and identity. • Needed help meeting customer SLAs • Quick discovery of incident root-causes • Reduction in number of false alerts “Understanding customer volume patterns is important for the business. If traffic falls outside of a certain range, an alert is created. Splunk machine learning allows us to investigate early to ensure a seamless customer experience.” S. Koelpin, Lead Splunk Developer – TransUnion “We were excited to utilize machine learning to establish our customer activity baseline and help with performance monitoring of our applications,” E. Bailey, Senior Monitoring and Operations Architect - TransUnion
- 39. © 2 0 2 0 S P L U N K I N C .
- 40. © 2 0 2 0 S P L U N K I N C . Turning dark data into value Data is the fuel Data “as it is” Operationalizing ML Single Platform
- 41. © 2 0 2 0 S P L U N K I N C . Splunk ML in Action ITSI Demo
- 42. © 2 0 2 0 S P L U N K I N C . You! Thank
Notas do Editor
- Key Takeaways -Most of this data is dark data. -Dark data in unowned, uncaptured, unanalyzed. -This may be due to technical or organizational reasons (often both) Talk Track What we’ve found is that most of the data within organizations is still “dark data.” Dark data is either uncaptured, or it’s captured but now owned or analyze in a way that drives value for your company. In our recent report on the state of dark data, we found that 60% of companies reported that the majority of data was dark data. And this can happen for a number of reasons. It may be that your systems and apps simply weren’t designed for this type of analysis. They weren’t designed with ”observability” in mind, making it hard look across different systems to get a complete view. OR, the challenges may be organizational, as different parts of your business have built their own systems in silos to address their individual business needs. Regardless of the cause, most companies find themselves at a crossroads, trying to plot the best path forward as extracting value from real-time data can mean real competitive advantage.
- Key Takeaways -Most of this data is dark data. -Dark data in unowned, uncaptured, unanalyzed. -This may be due to technical or organizational reasons (often both) Talk Track What we’ve found is that most of the data within organizations is still “dark data.” Dark data is either uncaptured, or it’s captured but now owned or analyze in a way that drives value for your company. In our recent report on the state of dark data, we found that 60% of companies reported that the majority of data was dark data. And this can happen for a number of reasons. It may be that your systems and apps simply weren’t designed for this type of analysis. They weren’t designed with ”observability” in mind, making it hard look across different systems to get a complete view. OR, the challenges may be organizational, as different parts of your business have built their own systems in silos to address their individual business needs. Regardless of the cause, most companies find themselves at a crossroads, trying to plot the best path forward as extracting value from real-time data can mean real competitive advantage.
- Key Takeaways -Most of this data is dark data. -Dark data in unowned, uncaptured, unanalyzed. -This may be due to technical or organizational reasons (often both) Talk Track What we’ve found is that most of the data within organizations is still “dark data.” Dark data is either uncaptured, or it’s captured but now owned or analyze in a way that drives value for your company. In our recent report on the state of dark data, we found that 60% of companies reported that the majority of data was dark data. And this can happen for a number of reasons. It may be that your systems and apps simply weren’t designed for this type of analysis. They weren’t designed with ”observability” in mind, making it hard look across different systems to get a complete view. OR, the challenges may be organizational, as different parts of your business have built their own systems in silos to address their individual business needs. Regardless of the cause, most companies find themselves at a crossroads, trying to plot the best path forward as extracting value from real-time data can mean real competitive advantage.
- Key Takeaways -Most of this data is dark data. -Dark data in unowned, uncaptured, unanalyzed. -This may be due to technical or organizational reasons (often both) Talk Track What we’ve found is that most of the data within organizations is still “dark data.” Dark data is either uncaptured, or it’s captured but now owned or analyze in a way that drives value for your company. In our recent report on the state of dark data, we found that 60% of companies reported that the majority of data was dark data. And this can happen for a number of reasons. It may be that your systems and apps simply weren’t designed for this type of analysis. They weren’t designed with ”observability” in mind, making it hard look across different systems to get a complete view. OR, the challenges may be organizational, as different parts of your business have built their own systems in silos to address their individual business needs. Regardless of the cause, most companies find themselves at a crossroads, trying to plot the best path forward as extracting value from real-time data can mean real competitive advantage.
- Alex and Siyka to challenge this assumption live to keep it entertaining and establish them as experts
- Bring the Citizen Data Scientist concept from Gartner here, challenging this slide
- By applying machine learning to our business problems we can augment and amplify the strengths that we have as humans, allowing us to: -detect what is not visible to the human eye: AI that provides automated, real-time detection can uncover important insights in any data set, versus manual, time-intensive processes in which you could still miss the aberrations or outliers that are subtle, but could still be consequential. -predict future outcomes: a major and ongoing challenge for business is dealing with unexpected circumstances. while humans are able to do forecasting and make educated guesses, computer can compute and analyze data, identifying patterns and predicting outcomes faster than any human. -reduce noise: With all the data that’s generated, comes a lot of noise which makes it difficult for humans to know what is important for them to focus on. With AI it’s possible to classify data points into specific groups to get insights.
- Key Takeaways -We take a different approach and allow you to turn data into doing -We allow you to bring data from the connected world and drive business outcomes faster than ever before Talk Track At Splunk our approach is different. We allow you to ingest data from all kinds of different sources: Be it systems, devices or interactions, and turn that data into meaningful business outcomes across your organization. That’s the power of Splunk. Let’s let’s take a quick look at how…
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- Before Splunk Failure detection — Customer often informs IT Incident Triage — All hands on deck, taking up 30 to 40 minutes Incident Troubleshooting — Lengthy log analysis done manual Service Restoration — Fix is implemented Root Cause Analysis — Up to 30% unknown root causes, causing incidents to recur With Splunk Better detection customer is notified by IT Faster triage often conducted by 1st level staff without all hands on deck Faster investigation (MTTI) through rapid log search and correlation conducted in conjunction by different teams (everyone looks at the same data) Faster and more comprehensive root cause analysis reduces incident recurrence
- Today, approximately 20 management systems, from Microsoft System Center Configuration Manager (SCCM) to SolarWinds network management tools, more than 4,500 configuration items (CIs) across 120 IT services and 240 locations worldwide, feed into Splunk ITSI at Leidos, helping the company boil 3,500 to 5,000 daily alerts down to roughly 50 tickets for network and datacenter operations to act on. Passing CMDB information into Splunk ITSI allows different alert displays for different staff.
- Siyka to present TransUnion is a big data and Informations solution company founded in 1968 4.8 Billion data updates each month 30+ countries served 90 000 data sources 50+ PB of information
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