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HfS Webinar Slides: How Cognitive Systems like ignio™ Simplify Batch Jobs Management

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Batch jobs are the lifeblood for thousands of businesses—many of which run millions of batch jobs every year. Unfortunately, managing these high volumes of batch jobs has become a huge nightmare: numerous errors require a large amount of resources to validate and isolate the problems. Even then, batch jobs still run into unexpected outages, while Service Level Agreement (SLA) violations threaten the proper operation of the business.

This webinar demonstrates how ignio™, the world’s leading cognitive system, has been helping customers tackle this complex problem. We share real world examples on how ignio™ is implemented and highlight the lessons learned from these implementations.

Attend and learn:
Why batch job management issues are affecting business operations
How Cognitive systems like ignio™ solve the complex issues of batch jobs management
How implementing ignio™ resolves customer problems—using real world examples
Insights from practitioners on how this is implemented and the lessons learned from these implementations.

Speakers:
Dr. Maitreya Natu, Digitate
Dr. Thomas Reuner, HfS Research
Victor Thu, Digitate

Published in: Business
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HfS Webinar Slides: How Cognitive Systems like ignio™ Simplify Batch Jobs Management

  1. 1. How Cognitive Systems like ignio are simplifying Batch Jobs Management Tom Reuner, Research VP, HfS Research tom.reuner@hfsresearch.com @tom_reuner @hfsresearch Web: www.hfsresearch.com | Blog: www.horsesforsources.com Webinar in collaboration with TCS November 3rd, 2016
  2. 2. Proprietary │Page 2© 2016 HfS Research tom.reuner@hfsresearch.com Overview § Tom Reuner is Research Vice President, Intelligent Automation at HfS. Tom is responsible for driving the HfS research agenda for Intelligent Automation practice across the whole gamut ranging from RPA to Autonomics to Cognitive Computing and Artificial Intelligence. A key element in Tom’s responsibilities is guiding clients and stakeholders on the evolution of Intelligent Automation including the coverage of new players and approaches. Furthermore, he is driving the research on application testing and service management. A central theme for all of his research is the increasing linkages between technological evolution and evolution in the delivery of business processes. Previous Experience § Tom’s deep understanding of the dynamics of this market comes from having held senior positions with Gartner, Ovum and KPMG Consulting in the UK and with IDC in Germany where his responsibilities ranged from research and consulting to business development. He has always been involved in advising clients on the formulation of strategies, guiding them through methodologies and analytical data and working with clients to develop impactful and actionable insights. Tom is frequently quoted in the leading business and national press, appeared on TV and is a regular presenter at conferences. Education § Tom has a PhD in History from the University of Göttingen in Germany. Tom Reuner, Research VP, HfS Research
  3. 3. Proprietary │Page 3© 2016 HfS Research HfS Research Gives You an Unvarnished View on the Market
  4. 4. Proprietary │Page 4© 2016 HfS Research HfS Research Has Been Writing About Intelligent Automation for 4+ Years
  5. 5. Proprietary │Page 5© 2016 HfS Research Fixed Assets Leveraged Assets 2 Design Thinking 3 Brokers of Capability 1 Write Off Legacy 4 Collaborative Engagement 7 Holistic Security 5 Intelligent Automation 6 Accessible & Actionable Data 8 Plug & Play Digital Services SOLUTION Ideals LEGACY ECONOMY AS-A-SERVICE ECONOMYCHANGE MGMT Ideals Ø “As-a-Service” is about augmenting human performance by re-thinking business model changes, enabled by Digital Technologies and Intelligent Automation Ø Moving into the As-a-Service Economy means changing the nature, attitude, focus and financial constructs of engagements between Enterprise Buyers, their Service Providers, technology suppliers and advisors Industry is heading toward the As-a-Service Economy
  6. 6. Proprietary │Page 6© 2016 HfS Research Automation Really is in the Eye of the Beholder
  7. 7. Proprietary │Page 7© 2016 HfS Research trigger based Characteristic of process rules based dynamic language rules based standardized language Structured Characteristic of data/information Unstructured without patternsUnstructured patterned Data Center Automation: Runbook Scripting Scheduling Job control Workload automation Process orchestration SOA Virtualization Cloud services RPA Cognitive Computing Artificial Intelligence BPM Workflow ERP Autonomics HfS Sees Intelligent Automation As A Continuum Today
  8. 8. Proprietary │Page 8© 2016 HfS Research trigger based Characteristic of process rules based dynamic language rules based standardized language Structured Characteristic of data/information Unstructured without patternsUnstructured patterned Data Center Automation: Runbook Scripting Scheduling Job control Workload automation Process orchestration SOA Virtualization Cloud services RPA Cognitive Computing Artificial Intelligence BPM Workflow ERP Autonomics The HfS Intelligent Automation Continuum
  9. 9. Proprietary │Page 9© 2016 HfS Research Automation is a journey: Automation is not a quick fix; it is a journey. It takes preparation to find the right candidates and can be done effectively only by taking support from the people who are involved in the business or IT operations War for talent: IA strategies require a unique talent set with the right mix of technical knowledge and business acumen. Scarcity of this talent is currently the biggest factor limiting the speed of execution Finding a common language: As IA is not defined, stakeholders are struggling with blurred perceptions in the marketplace. Many tools and approaches use the automation moniker. Many stakeholders fail to understand the nuanced differences Data curation is critical: Applying Cognitive and machine learning solutions to IA requires access to large amounts of relevant data to build reliable models Crossing the chasm: A major challenge is to convince and align client stakeholders. In the words of one executive: “People don’t believe, people don’t trust. A lot of people are talking about automation, but few really understand it.” Look beyond task automation: The marketing noise is largely around RPA and implicitly notions of task automation. Therefore, it can be challenging to get a sense of the bigger picture It Is A Nascent Market But There Are Broad Lessons Already
  10. 10. Proprietary │Page 10© 2016 HfS Research Insights, Data Vertical Processes Vertically infused insights and data BPaaS, BPO as-a-stack, Industry platforms. Machine learning, Neural networks, Enterprise search, Artificial intelligence Analytics Endgame: Vertically infused data and insights?!
  11. 11. Proprietary │Page 11© 2016 HfS Research Victor Thu Global Head of Marketing and Product Marketing Digitate Dr. Maitreya Natu Lead Scientist Digitate Our Panelists
  12. 12. Maitreya Natu Lead Scientist, Digitate Victor Thu Head of Marketing, Digitate Cognitive Batch Jobs Management
  13. 13. Confidential 14 A Tata Consultancy Services Venture A Tata Consultancy Services Venture Trusted by Fortune 500 Enterprises Intelligently Managing Over 600,000 Infrastructure Resources 25+ Patents (pending) 400+ Employees Globally
  14. 14. Confidential 15 A Tata Consultancy Services Venture Impact Activity Complexity Automate simple tasks Procedural Perform complex activities without explicit instructions Investigative Drive proactive continuous optimizations Analytical Strategize and plan for the future Planning | FourTypes of Cognitive Activities Pioneers “Cognitive Automation”
  15. 15. Confidential 16 A Tata Consultancy Services Venture Our First Product Adaptive cruise control Autonomous operations Navigator Investigate and guide Self-learned enterprise context (insights & patterns about interconnected business applications and their infrastructures) Machine learning & AI Model-driven software engineering Pre-built knowledge (about IT infrastructure technologies) + = A layer of intelligence for enterprise technology and operations
  16. 16. Cognitive Batch Management Maitreya Natu Lead Scientist, Digitate
  17. 17. Confidential 18 A Tata Consultancy Services Venture High complexity and noise ü High heterogeneity ü Most of the time is wasted in eliminating noise ü High dependence on tacit knowledge Difficulties in assessing impact of change ü Forced to react to business or technology changes • Instability and high business impact Surprises ü “Unexpected” outages, delays, and SLA violations ü Inability to prioritize actions ü Insufficient time to take corrective actions Batch Processing | Problem Areas
  18. 18. Confidential 19 A Tata Consultancy Services Venture Scale and Complexity ü 100K+ jobs spread across many business units ü Complex inter-dependencies across jobs, processes, data feeds, vendor feeds, files, hosts. ü Complex workload, resource, performance relationships Changing environment ü Every day changing jobs and dependencies ü Changing compute and storage infrastructure allocation Diversity ü Different applications, business units, and business processes ü Different schedulers – Autosys, ControlM, TWS, Opcon ü Different platforms – mainframes and distributed systems ü Different environments - prod, non-prod, dev, QA, UAT Batch Processing | Key Obstacles
  19. 19. Confidential 20 A Tata Consultancy Services Venture Batch Processing | Problem Areas Intelligent Command Center Proactive Resilience Agile Transformation Improve transparency and eliminate noise Generate proactive notifications to predict and prevent What-If and If-what analysis
  20. 20. Confidential 21 A Tata Consultancy Services Venture Case Study: How Customer Uses ignio for Batch Job Management Context • Proactive management of batch jobs of a leading bank in the UK Scope and Scale • 52 business units • 2500 business processes • ~23,000 batch jobs per day • ~100,000 job-job dependencies • 2 batch schedulers – OpCon and ControlM
  21. 21. Confidential 22 A Tata Consultancy Services Venture Blueprint Construction 360-degree view: Graph model relating business units, to business processes to batch jobs Nodes represent business units, business processes (streams), and jobs Nodes and edges are associated with static and dynamic attributes (e.g., job start time, run time, end time, …) Edges represent precedence and containment relationships Entities, relationships and attributes are mined from batch schedulers, batch run logs, job definitions, SLA definitions, & other data sources
  22. 22. Confidential 23 A Tata Consultancy Services Venture Normal Behavior Characterization Profile vitals and issues • Changes • Trends • Outliers • Temporal patterns Profile dependencies • Influencers • Influencees • Cuts across technologies, and business units
  23. 23. Confidential 24 A Tata Consultancy Services Venture Suppress false alerts • Dynamic thresholds for run time, start time and end times SmartTriggers
  24. 24. Confidential 25 A Tata Consultancy Services Venture • Computes probability of failure by analyzing past failures and SLA violations • Computes impact by analyzing dependencies • Reports jobs with a high failure risk Assess and Manage Risks
  25. 25. Confidential 26 A Tata Consultancy Services Venture Predict a Future Batch • Status of scheduled jobs: running, delayed, failed jobs • Inter-stream and inter-BU dependencies • Anomalies and SLA violations • Critical paths and critical jobs
  26. 26. Confidential 27 A Tata Consultancy Services Venture Generate Proactive Notifications • Derive historical trends and patterns to predict future behavior • Predict likely SLA violations • Predict time-to-saturation of resources
  27. 27. Confidential 28 A Tata Consultancy Services Venture • Predict execution behavior of jobs and business processes • Predict potential SLA violations • Identify critical jobs and paths to act upon to prevent SLA violations What-If Analysis Derive the impact of change • Business change • Change in workload • Operations change • Addition/Deletion of jobs and dependencies • Change in schedule • Change in runtime • Infrastructure change • Change in provisioned CPU/MIPS • Change in number of worker processes
  28. 28. Confidential 29 A Tata Consultancy Services Venture If-What Analysis Derive the plan for optimizing • Batch execution time • SLA adherence • Number of required CPUs/MIPS • Peak MIPS usage
  29. 29. Confidential 30 A Tata Consultancy Services Venture Lessons Learned Need to start off with a comprehensive topology construction Self learn and adapt to system changes automatically Knowledge-centric process to translate analytical observations into recommendations Detailed behavior modeling for accurate predictions
  30. 30. Proprietary │Page 31© 2016 HfS Research Questions?
  31. 31. Proprietary │Page 32© 2016 HfS Research APPENDIX
  32. 32. Proprietary │Page 33© 2016 HfS Research Robotic Process Automation describes a software development toolkit that allows non-engineers quickly to create software robots to automate rules-driven business processes. E.g. digitizing the process of collecting of unpaid invoices, that involves mimicking manual activities in the RPA software, the integration of electronic documents and generation of automated emails to ensure the whole collections, process is run digitally and can be repeated in a high-throughput, high intensity model. Cognitive computing is the simulation of human thought processes in an Intelligent Automation process or set of processes. It involves self-learning systems that use data mining, pattern recognition and natural language processing to mimic the way the human brain works, without continuous manual intervention. E.g. an insurance adjudication system that assesses claims, based on scanned documents and available data from similar claims and evaluates payment awards. Autonomics is referring to self-learning and self-remediating engines, where the system makes autonomous decisions, using high-level policies, constantly monitoring and optimizing its performance and automatically adapting itself to changing conditions and evolving business rules and dynamics. Increasingly minimal human intervention. E.g. a virtual support agent continuously learning to handle queries and creating new rules/exceptions as products evolves and queries change. Artificial Intelligence is where intelligent automation systems go beyond routine business and IT process activity to make decisions and orchestrate processes. E.g. an AI system managing a fleet of self-driving cars or drones to deliver goods to clients, manage aftermarket warranties and continuously improve the supply chain. How HfS Defines the Building Blocks for Intelligent Automation
  33. 33. Proprietary │Page 34© 2016 HfS Research Mind The Gap: These Topics Have To Be On The (Automation) Center Stage
  34. 34. Proprietary │Page 35© 2016 HfS Research About HfS Research HfS Research is The Services Research Company™—the leading analyst authority and global community for business operations and IT services. The firm helps enterprises validate their global operating models with world-class research and peer networking. HfS Research coined the term The As-a-Service Economy to illustrate the challenges and opportunities facing enterprises to re-architect their operations and thrive in this era where emerging disruptive competitors are using digital platforms and cognitive computing that can wipe out traditional enterprises overnight. HfS’ OneOfficeTM Paradigm is centered on creating the digital customer experience and an intelligent, single office to enable and support it. HfS’ vision is about helping clients achieve an integrated support operation has the digital prowess to enable its enterprise to meet customer demand - as and when that demand happens. With specific practice areas focused on the Digitization of business processes and Design Thinking, Intelligent Automation and Outsourcing, HfS analysts apply industry knowledge in healthcare, life sciences, retail, manufacturing, energy, utilities, telecommunications and financial services to form a real viewpoint of the future of business operations. HfS facilitates a thriving and dynamic global community which contributes to its research and stages HfS holds several OneOfficeTM Summits each year, bringing together senior service buyers, advisors, providers and technology suppliers in an intimate forum to develop collective recommendations for the industry and add depth to the firm’s research publications and analyst offerings. Now in its tenth year of publication, HfS Research’s acclaimed blog Horses for Sources is the most widely read and trusted destination for unfettered collective insight, research and open debate about sourcing industry issues and developments. HfS was named Analyst Firm of the Year for 2016, alongside Gartner and Forrester, by leading analyst observer InfluencerRelations. To learn more about HfS Research, please email research@HfSResearch.com

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