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

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. 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. Proprietary │Page 3© 2016 HfS Research
HfS Research Gives You an Unvarnished View on the Market

4. Proprietary │Page 4© 2016 HfS Research
HfS Research Has Been Writing About Intelligent Automation for 4+ Years

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. Proprietary │Page 6© 2016 HfS Research
Automation Really is in the Eye of the Beholder

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. 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. 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. 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. 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. Maitreya Natu
Lead Scientist, Digitate
Victor Thu
Head of Marketing, Digitate
Cognitive Batch
Jobs Management

14. 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

15. 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”

16. 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

17. Cognitive Batch Management
Maitreya Natu
Lead Scientist, Digitate

18. 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

19. 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

20. 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

21. 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

22. 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

23. 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

24. Confidential 24 A Tata Consultancy Services Venture
Suppress false alerts
• Dynamic thresholds for run
time, start time and end times
SmartTriggers

25. 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

26. 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

27. 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

28. 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

29. 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

30. 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

31. Proprietary │Page 31© 2016 HfS Research
Questions?

32. Proprietary │Page 32© 2016 HfS Research
APPENDIX

33. 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

34. Proprietary │Page 34© 2016 HfS Research
Mind The Gap: These Topics Have To Be On The
(Automation) Center Stage

35. 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