Context Is Critical for IT Operations - How Rich Data Yields Richer Results
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This is the keynote presentation by Bhanu Singh delivered at Cloud Expo Santa Clara. He talks about the importance of context in AIOps and how rich data makes a difference in AIOps effectiveness. Learn more at https://www.opsramp.com Also, follow us on social media channels to learn about product highlights, news, announcements, events, conferences and more: Twitter - https://www.twitter.com/OpsRamp LinkedIn - https://www.linkedin.com/company/opsramp Facebook - https://www.facebook.com/OpsRampHQ/
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Context Is Critical for IT Operations - How Rich Data Yields Richer Results
- 1. Context is Critical: How Richer DataYields Richer Results Bhanu Singh, SVP of Engineering & DevOps
- 2. “Every 60 seconds 160M emails are sent, 98K tweets are shared on twitter, 600 videos are uploaded on YouTube and 1500 blog entries are created.” 2
- 3. Unprecedented Complexity, Change, and Opportunity AIOps Complexity > Automation > AIOps > Content DevOps ITOps SecOpsCloudOps DataOps Digitization Eliminates Lines Between Business and IT 3
- 5. 5 How to Achieve FutureOps Automation Governance Scale FutureOps • Data-Driven Automation • Connected Process Governance • Distributed Enterprise Scale
- 6. AIOps CanTransform IT Operations Into a New Framework 6
- 7. But Machines Need Proper ContextTo Perform Artificial Intelligence 7
- 8. 8 How Does AIOps Drive Actionable Insights? Recognizing Redundant Alerts • Topology based: Cascade of alerts from downstream dependencies • Clustering based: Cluster of alerts from a common root cause • Co-occurrence based: Alerts that occur together due to common root cause Preventative/Proactive Monitoring • Forecast-based alerting: metrics trending towards a specific limit • Anomaly detection: out of normal system and application behavior Learning-Based Resolution • Route the incident to the right available team based on the historical analysis, auto-creation, escalation, and notification.
- 9. Five Use Cases for Contextual Data and Actionable Insights 9
- 10. Cascading Alerts Across Connected Resources Five Alerts, But Only One Matters Switch A Switch B VM 1, 2, 3 10
- 11. Chain of Alerts With the Same Root Cause Three Alerts, But Only One Matters Server A 11
- 12. Chain of Alerts With Microservice Dependency Microservice-C Microservice-BMicroservice-A Microservice-D 12
- 13. Chain of Alerts Related to Hidden Cause 13 Three Alerts Tied To One Common Issue Server A Server B Server C Config Changes
- 14. DynamicThreshold: Forecast and Anomaly Detection Alert on forecasted time to breach Alert on sudden change in behavior 14
- 15. Using ContextTo Auto-Generate Incidents Categorization Prioritization Group Assignment/ Routing Available SME 15
- 16. 16 Five Steps To Creating A Data-Driven Organization
- 17. Build a culture of data-driven actions & outcomes • Build skills and expertise • Demonstrate opportunity through value • Do not start with data Focus on Business Objectives First1 17
- 18. 18 Shift From Process-Based to Data-BasedValues2 Staff up with key data leadership and data governance rules 18
- 19. Discover, understand, and rationalize all data sources Create a data model that drives relationship and context between technical and business services. Leverage a metadata-driven data model to deliver context. Create the right data via: • Data Selection • Data Cleaning • Data Reduction • DataTransformation 19 Get the Data Right!3 19
- 20. 20 Bring Qualitative and Quantitative DataTogether4 Selectively apply data-driven business process, technology innovation, and corporate governance 20
- 21. Use automation to eliminate tasks that are repetitive, higher value, and have limited human interaction 5 Start Small WithTargeted Use Cases 21
- 22. Why Embrace Contextual, Data-Driven IT Operations? Create transparency, team collaboration and productivity Drive change with speed, quality and confidence Manage risk and optimize cost 22
- 23. Bhanu Singh’s keynote at CloudExpo Santa Clara OpsRampTechTalk – Context is Critical: How Richer DataYields Richer Results OpsRamp’s Service-Centric AIOps solution The State of AIOps Report Useful Links 23
- 24. ThankYou Bhanu P. Singh Head of Engineering and Ops Bhanu.singh@OpsRamp.com linkedin.com/in/bhanu-singh/ @bhanu1527
Editor's Notes
- Business Enabling technologies are changing Faster than Ever Complexity is demanding automation as it is humanly impossible to monitor and manage distributed hybrid cloud apps and environment. The move towards an agile ops encompassing multi-cloud platform, containers and distributed apps architecture, combined with DevOps and continuous delivery practices challenges the effectiveness of traditional monitoring, infra and apps mgmt. tools. Factors challenging traditional tools are – Static to Dynamic Systems, Data to Insights (Future proofing technologies), More stakeholders, specialist vs. generalist Machines and algorithm needs right data and its context to accurately operate and perform the tasks that meet the needs and outcomes of the business. Automation is the only way for modern IT to transform its operating model and support the digitization initiatives of the business processes.
- Machines are moving from physical tasks to “Thinking” tasks. Automation is about applying algorithmic intelligence to eliminate mundane tasks and driving thinking into machine. A lot of hype and companies are trying to solve all this use cases magically but the key is data context and for each uses data preparation and quality could be different to drive human equivalent or better results following predictable and repeatable model that’s gets better with maturity and over time.
- Context could be who, where and when – Location, usage, timing, etc. Inconsistent data modeling results in a number of problems – Contextual blindness ( Give example of Alarm->Network Service) -- Gap Stitching – What can’t be model can’t be effectively monitored, diagnosed, interpreted or predicted. For example compute data utilization (CPU) exposed via infra monitoring should be presented in the context of app supported and the performance and vice versa. -- Problem often manifest at the deepest point -- Raw data may have many missing links and information -- Inaccurate and Fragmented data leads to expensive analysis and non-sensical results.
- Chief Information and Security Officer (CISO) Chief Data Officer (CDO)