hfs webinar slides: how cognitive systems like ignio™ simplify batch jobs management

HowCognitiveSystemslikeignio aresimplifyingBatchJobsManagement

TomReuner,ResearchVP,HfS Research

[email protected]@tom_reuner @hfsresearch

Web:www.hfsresearch.com |Blog:www.horsesforsources.com

WebinarincollaborationwithTCS

November3rd,2016

Proprietary│Page2©2016HfSResearch

[email protected]

Overview§ TomReunerisResearchVicePresident,IntelligentAutomationatHfS.TomisresponsiblefordrivingtheHfSresearchagendaforIntelligentAutomationpracticeacrossthewholegamutrangingfromRPAtoAutonomicstoCognitiveComputingandArtificialIntelligence.AkeyelementinTom’sresponsibilitiesisguidingclientsandstakeholdersontheevolutionofIntelligentAutomationincludingthecoverageofnewplayersandapproaches.Furthermore,heisdrivingtheresearchonapplicationtestingandservicemanagement.Acentralthemeforallofhisresearchistheincreasinglinkagesbetweentechnologicalevolutionandevolutioninthedeliveryofbusinessprocesses.

PreviousExperience§ Tom’sdeepunderstandingofthedynamicsofthismarketcomesfromhavingheldseniorpositionswithGartner,

OvumandKPMGConsultingintheUKandwithIDCinGermanywherehisresponsibilitiesrangedfromresearchandconsultingtobusinessdevelopment.Hehasalwaysbeeninvolvedinadvisingclientsontheformulationofstrategies,guidingthemthroughmethodologiesandanalyticaldataandworkingwithclientstodevelopimpactfulandactionableinsights.Tomisfrequentlyquotedintheleadingbusinessandnationalpress,appearedonTVandisaregularpresenteratconferences.

Education§ TomhasaPhDinHistoryfromtheUniversityofGöttingen inGermany.

TomReuner,ResearchVP,HfSResearch


HfS Research Gives You an Unvarnished View on the Market


HfS Research Has Been Writing About Intelligent Automation for 4+ Years


FixedAssetsLeveragedAssets

2DesignThinking

3BrokersofCapability

1WriteOffLegacy

4CollaborativeEngagement

7HolisticSecurity

5IntelligentAutomation

6Accessible&Actionable

Data

8Plug&PlayDigitalServices

SOLUTIONIdeals

LEGACY

ECONOMY

AS-A-SERVICE

ECONOMYCHANGEMGMTIdeals

Ø “As-a-Service”isaboutaugmentinghumanperformance byre-thinkingbusinessmodelchanges,enabled byDigitalTechnologiesandIntelligentAutomation

Ø MovingintotheAs-a-ServiceEconomymeanschangingthenature,attitude,focusandfinancialconstructsofengagements betweenEnterpriseBuyers,theirServiceProviders,technologysuppliersandadvisors

Industry is heading toward the As-a-Service Economy


Automation Really is in the Eye of the Beholder


trigger based

Characteristic of process

rules baseddynamic language

rules basedstandardized language

Structured

Characteristic of data/information

Unstructured without patternsUnstructured patterned

Data CenterAutomation:

RunbookScriptingSchedulingJob controlWorkloadautomationProcessorchestration

SOAVirtualization

Cloud services

RPA CognitiveComputing

ArtificialIntelligence

BPMWorkflow

ERP

Autonomics

HfS Sees Intelligent Automation As A Continuum Today


trigger based

Characteristic of process

rules baseddynamic language

rules basedstandardized language

Structured

Characteristic of data/information

Unstructured without patternsUnstructured patterned

Data CenterAutomation:

RunbookScriptingSchedulingJob controlWorkloadautomationProcessorchestration

SOAVirtualization

Cloud services

RPA CognitiveComputing

ArtificialIntelligence

BPMWorkflow

ERP

Autonomics

The HfS Intelligent Automation Continuum


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


Insights, Data

Vertical Processes

Vertically infusedinsights and data

BPaaS, BPO as-a-stack,Industry platforms.

Machine learning,Neural networks,Enterprise search,Artificial intelligenceAnalytics

Endgame: Vertically infused data and insights?!


VictorThu

GlobalHeadofMarketingandProductMarketing

Digitate

Dr.Maitreya Natu

LeadScientistDigitate

Our Panelists

Maitreya NatuLead Scientist, Digitate

Victor ThuHead of Marketing, Digitate

Cognitive Batch Jobs Management

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


Impa

ct

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

| Four Types of Cognitive Activities

Pioneers “Cognitive Automation”


Our First Product

Adaptive cruise control Autonomous operations

NavigatorInvestigate 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

Cognitive Batch Management

Maitreya NatuLead Scientist, Digitate


High complexity and noiseü High heterogeneityü Most of the time is wasted in eliminating noiseü High dependence on tacit knowledge

Difficulties in assessing impact of changeü Forced to react to business or technology changes

• Instability and high business impact

Surprisesü “Unexpected” outages, delays, and SLA violationsü Inability to prioritize actionsü Insufficient time to take corrective actions

Batch Processing | Problem Areas


Scale and Complexityü 100K+ jobs spread across many business unitsü Complex inter-dependencies across jobs, processes, data feeds, vendor

feeds, files, hosts.ü Complex workload, resource, performance relationships

Changing environmentü Every day changing jobs and dependenciesü Changing compute and storage infrastructure allocation

Diversityü Different applications, business units, and business processesü Different schedulers – Autosys, ControlM, TWS, Opconü Different platforms – mainframes and distributed systemsü Different environments - prod, non-prod, dev, QA, UAT

Batch Processing | Key Obstacles


Batch Processing | Problem Areas

Intelligent Command Center

ProactiveResilience

Agile Transformation

Improve transparency and eliminate noise

Generate proactive notifications to predict and prevent

What-If and If-what analysis


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


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


Normal Behavior Characterization

Profile vitals and issues• Changes• Trends• Outliers• Temporal patterns

Profile dependencies• Influencers• Influencees• Cuts across technologies, and business

units


Suppress false alerts• Dynamic thresholds for run

time, start time and end times

Smart Triggers


• 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


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


Generate Proactive Notifications

• Derive historical trends and patterns to predict future behavior

• Predict likely SLA violations • Predict time-to-saturation of

resources


• 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 AnalysisDerive 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


If-What Analysis

Derive the plan for optimizing • Batch execution time• SLA adherence• Number of required

CPUs/MIPS• Peak MIPS usage


Lessons Learned

Needtostartoffwithacomprehensivetopologyconstruction

Selflearnandadapttosystemchangesautomatically

Knowledge-centricprocesstotranslateanalyticalobservationsintorecommendations

Detailedbehaviormodelingforaccuratepredictions


Questions?


APPENDIX


RoboticProcessAutomation describes asoftwaredevelopmenttoolkitthatallows non-engineersquicklytocreatesoftwarerobotstoautomaterules-drivenbusinessprocesses.E.g.digitizingtheprocessof collectingofunpaidinvoices,thatinvolves mimicking manualactivitiesintheRPAsoftware,the integrationof electronic documents andgenerationof automatedemailstoensurethewholecollections,processisrundigitallyandcanberepeatedinahigh-throughput,high intensity model.

Cognitivecomputing isthesimulationofhumanthoughtprocessesinan IntelligentAutomation processorsetofprocesses.Itinvolvesself-learningsystemsthatusedatamining,patternrecognitionandnaturallanguageprocessingtomimicthewaythehumanbrainworks,withoutcontinuous manualintervention.E.g.aninsuranceadjudicationsystemthatassessesclaims,basedonscanneddocumentsandavailabledatafromsimilarclaims andevaluatespaymentawards.

Autonomics isreferringtoself-learningandself-remediatingengines,wherethe systemmakesautonomousdecisions,usinghigh-levelpolicies,constantlymonitoringandoptimizingitsperformanceandautomaticallyadaptingitselftochangingconditionsandevolvingbusinessrulesanddynamics.Increasinglyminimalhumanintervention.

E.g.avirtual supportagentcontinuously learningtohandlequeriesandcreatingnewrules/exceptionsasproductsevolvesandquerieschange.

Artificial Intelligence iswhereintelligentautomationsystemsgobeyondroutinebusinessandITprocessactivitytomakedecisionsandorchestrateprocesses.E.g.anAIsystem managing afleetofself-drivingcarsordronesto deliver goodstoclients,manage aftermarketwarrantiesandcontinuouslyimprovethesupplychain.

How HfS Defines the Building Blocks for Intelligent Automation


Mind The Gap: These Topics Have To Be On The (Automation) Center Stage


About HfS ResearchHfS 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.

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