Srinivasan Mangadu Practice Leader - Global Delivery Office,CDB– Analytics & Information Management

Subramanian KubendranPractice Leader, Cognizant Digital Business – Analytics & Information Management

Jayasree PrabhakaranPractice Leader, Analytics & IM QA

Vinithra Ashok Process Specialist & Delivery Excellence Analytics & IM

Emerging Methodologies for Project management in Digital Era

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Topics

Abstract

Introduction & Change in Business Context in Digital Era

New Age Business Models & Key Drivers

Emerging Methodologies & Digital Engineering for Delivery

Importance of QA and Data Certification in Digital Delivery

Intelligent QA Platforms & Smarter Analytics/ML Driven QA

Summary

Reference

Author Biography

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Abstract

Digital transformation revolutionizes the way business is done. Future proofing the organization from continuously changing internal factors, external competitors needs:

• Differentiated positioning• Staying ahead among the peers• Increased need for Trust on Data • Improved Operational efficiencies

In this context its imperative for organizations to embark on Digital strategy roadmap. As part of this its very critical to understand various methodologies and engineering practices from Project management perspective.

This white paper details out how newer opportunities, methodologies & engineering practices in digital delivery & intelligent QA solutions in Digital context aligning to the methodologies are addressed by taking real life cases & results

Changing Business Context in Digital Era

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Our every action on the internet shapes companies’ business decisions

90% Of all data generated today was created in last two years

3.7 Billion World Internet population in 2017

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Introduction - Business context in Digital Era

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Value theme Use CasesNew age players

Technology innovation“Dash button”

Connected cars

Faster Time to market

User Experience

iwatch

New age business models

• Enriched Customer Experience

• Actionable Insights• Real time analytics

• Foot print expansion• Shared Economy models• Data is new air

• Breakthrough innovations • First of its kind products• Shared economy models

Digital natives edge on technology innovation to disrupt Digital aspirants business !!!

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Key Drivers & Factors driving the Overall Growth

Degree of Complexity Footprint Expansion Ahead of the Curve Efficiency Quality

Business

Operations

Technology

Key Challenges impacting the Digital transformation journey: o Changes in the customer behaviour o Elevated global competition o Adoption of social media o Emerging trends in Mobile / Cloud / Big Data / Analyticso Multi speed architecture / approaches (Conventional business)

Emerging methodologies & Digital Engineering

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Ideation workshops

Pilot and refine

Validate design

DeliverEssence of successful digital delivery team is increasingly relying on o Continuous Ideas to MVP (Minimum Viable Product) / PSPI (Potentially Shippable Product Increment)every short

iterationso Multi Skilled Cross-functional team that works together – no more Chinese wall between Dev & QAo Analytics driven QA driving/powering CI/CD o Co-Ideate, Co-create … Value delivery in shorter cycle time (days/weeks instead of months)

Shift in Delivery Models for Digital…

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Imperatives for Delivering Digital Solutions….

Examples Tests

Requirements

Becomes

Elab

orat

es

Verif

y Written before Coding & shared

RequirementsWorkshop

Explore & Experiment

Co-Ideate Co-Innovate Co-Operate

Discover Proof of Value

Contextualize Productionize

Idea Maturation

+ Feature

Refinement

Continuous Refinements

+Co-Validation

Validate Design Approaches

CorroborateOutcomes

Conference RoomPilots

IdeationWorkshops

Focus on break-through innovation first of its kind (Ideas to Monetization) Evolving requirements and verification/validation together Staying ahead of the curve & footprint expansion Derive efficiencies

…. Embark on engineering practices & methodologies to cater to Digital imperatives !!!

Acceptance Driven (ATDD)

BehaviorDriven (BDD)

Model Driven (MDD)

Design Thinking Continuous Integration

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Changing SDLC Paradigm: Co-Innovate, Co-Create & Co-operate

User StoryConsolidate Acceptance Criteria for

Story (BA/Dev/QA)

Create Cucumber feature file which defines reqts. in

structured English

Create automated step definition using

Java (QA), which tests the feature behavior

Develop (Dev)

Execute Feature Files

(Dev/QA)

UserAcceptance

The business owner & business

analyst have a conversation about

what is needed

The BA, Dev & QA will elaborate the

requirements together

Development will be considered done when all scenarios

are passed

The scenarios guide the

developers and acts as

automated tests

The automated tests provide feedback on

progress and help document the

application

1 2 3

4

5

6 7

Analytics Insights DeliveryData

Structured & Unstructured Data

Smart Connectors

Pre Built Models

Playground for Data Scientists

Data Discovery

Pre Built Insights Dashboards &

Reports

Governance and Security

For seamless ingestion & management of complex data & quick-start business analytics

Technology options across the platform stack

Smart Connectors across data types

Ready for Digital

Prebuilt Artifacts

Industry specific & cross-industry Biz Apps

Algorithms for advanced analytics

Digital Apps DigitalSmart Connectors

New DigitalCapabilities

Leveraging engineering practices & systems of intelligence for faster delivery….

Syst

ems o

f Eng

agem

ent

Syst

ems o

f Rec

ord

Systems of intelligence are redefining the nature of actions and decisions for businesses and consumers

ERP

Database

CRM

Mobile

Web

Wearables

SocialMedia

Cloud

Systems of Intelligence

Understanding

DataOrganization

Insights

Inte

llige

nt A

ctio

ns

VA

LU

ET I M E

Decision andImplementation

Data Generation

System of Intelligence Provides Additional Value from Insights

AI Cognitive Computing

Pre-Built Biz-Apps

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End to End System of Intelligence Platform accelerating Data to Insights & Deliver Value..

BRAVO

BigDecisions® BIGFrame OneRetail Customer360

Platform for Information Value Management TM JuPITER RAPIDIntelligent Digital QA

Systems

Platform Solutions

Increasing Relevance of QA

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Increasing Importance of QA & Data certification in Digital Delivery

How to ensure the veracity of

our ‘Digital Transformation’

?

How to improve value

of data monetization?

I need a Comprehensive

Automated Next-gen Business

QA solution

CXOSPEAKS

CIO/CDO

CEO IT QA HEAD

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Data “Fitness” is Key for in Digital Delivery SuccessWith data explosion, core of QA is becoming more focused on ensuring data accuracy, data integration and communication means and presenting below increasing QA trends

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Greater needs for QA Platforms for automation and diagnostic QA

Data AnalyzerSimplifies data analysis by rapidly profiling data & validating rules to certify data fitness

Data GeneratorSimplifies data generation by rapidly generating/extrapolating data for faster data readiness

Data ComparatorSimplifies data comparisonby rapidly comparing large volumes of source-target data & generating detailed mismatch reports

Automated QA EngineSimplifies the QA life cycle by speeding up validation execution & generating insightful reports for an improved QA ecosystem

Report ComparatorSimplifies report comparisonby rapidly exporting & comparing migrated source-target reports &Identifying mismatches

CLI & Integration Test Case and simple maps

SQLs Generator Command Line Utility CI/CD Cucumber

Integration

Cognizant’s Proprietary Platform for Information Value Management TM

Enables automation at multiple key touch points in the information management QA lifecycle

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Greater needs for QA Platforms for automation and integrated QA

Cognizant’s Proprietary BRAVO

A COMPREHENSIVE QA solution to help clients be successful in DIGITAL Transformation journey

END-END DIGITAL QA PLATFORM -

Test Reports-Adv.

VisualizationDashboard

AES- 128 Bit

Encryption

Hadoop/ NOSQL/ SPARK

Connectivity

Text Mining QA

Source Data Analyzer

Data Lake Processing QA

Data Ingestion QA

Analytics QA Image ProcessingAnalytics /

ML Driven QA

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Smarter Analytics Driven QA that leverages ML and AI techniques

Identifying data patterns like correlation to raise alerts

Periodic Dataset Data Preprocessing

Anomaly Detector

To study data penetration and report impacted assets

BRAVO – Data Revelator

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Smarter QA that leverages ML and AI techniques

Defect Clustering

Identify defects (bugs) and frequency of their occurrence based on defect data by employing

Natural Language Processing Algorithms

Regression Scenario Selection

Drive Optimum Testing via Analytics Driven Regression Scenario Selection

RAPID – Analytics Driven QA

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Emerging platforms for continuous testing & Dev Ops QA

Cognizant’s Proprietary JUPITER

Test Status?

PassedBuild Failed

Final Prod Pass

Build Passed

Build Triggere

d

Continuous Integration

C I - ServerContinuous Integration

Continuous Deployment

Continuous Deployment

CICD Dashboard

JUPITER

Feature File Templates

Reusable Libraries(DB Connectors.

Comparators, Parsers)

Customizable Reporting

Defect Management/DevOps tools Plugins (SCM/CI/CD)

Latest code deployment check

Jobs Availability

Supporting files/scripts availability

Capture and compare Source Record Count

Input Files/Table definitions

Pre Validations

Output Files creation with Latest Timestamp

Record Count Match between Source & Target

Duplication Check

Archival Process completion

Output Files/Table load

ATDD driven – Continuous Testing Automation

Framework

Automated Test Suite

Post Validations

Build Manager

Code Check in

UAT PRODQA

Automated Testing Hub

Automated Testing Trigger

3 Amigos

30 %

QA CycleAutomated

execution to reduce the QA

cycle efforts

70 %

ReusabilityJUPiTER

Marketplace Library can yield ~ 70% reusability

70 %

Automation Coverage70 %

Automation Coverage of

QA Test Scenarios

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Helps to align and deliver towards Digital successAn End to End Quality Assurance Platform which enables complete Automation in Digital delivery

Library Market Place Connect

to SCM

Connect to QC

Data Generati

on

Code Reviewer

Automate RegressionData

Revelator

Analytics Driven

QA

Defect Clusterin

g

Data Parsers

Data Ingestion

Data Lake QA

QA as a Service

BRAVO

PLATINUM

JUPITER

RAPID

Continuous Data testing for Continuous Integration

Analytics Driven QA helps optimize QA systems

Platinum automates at multiple key QA touch points

Platform for an end to end Big Data QA

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QA as a service in Digital transformation Journey

Key HighlightsBusiness Drivers Large Teradata footprint leading to high cost of operations driving the need for ooffloading batch and analytical workloads from

Teradata to Hadoop for better analytics, faster Turn Around Time and lower cost

Develop an Hadoop based analytical platform using data from legacy operational systems

Reduction in overall Teradata OPEX and CAPEX

Solution Highlights Proposed BIGFrame offering – to migrate Teradata workloads to Hadoop

Tool aided automated conversion of BTEQ scripts to equivalent Hive scripts

Detailed application assessment on Teradata applications and jobs to qualify right candidates for Hadoop

Tool aided reverse engineering and rewrite of functionality using BIGFrame

Re-architected process to leverage target technology capabilities (parallel processing, mass data IO capabilities etc.)

Business Outcome Teradata equivalent/better performance with commodity servers

Accelerated conversion process leveraging cognizant In-house tools and accelerators

Scalable platform for larger analytical processing

Extensibility of analytical platform

Technology Stack: Teradata |Syncsort | Hortonworks 2.4 | DataStage | GreenPlum

3000+ BTEQ scripts converted to Hive

using BIGFrame

500+GB of data ingested on daily

basis

30+ enterprise Teradata jobs converted to Hadoop

BRAVO

Data Ingestion QA Data Lake Processing QA

Establish connection to Hadoop (Edge node)

And Teradata via scripts

• Reconciling Teradata data against Hive tables using Edge node reconciler

• Defect analysis using BRAVO logs

• Import test cases using bulk import feature for the files extracted using BRAVO scripts

• Regression test execution • Defect analyzing using BRAVO Execution logs

Customer

BusinessNeeds

• A private mutual company that focuses on property, casualty, and auto insurance, and also offers commercial insurance, life, health, and homeownerscoverage as well as investment and retirement-planning products

• A critical Data Migration of 5000+ dataset from different source systems to Hadoop• Need to identify avenues for possible automation as the project timeline was stringent

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QA as a service for Continuous Testing

QA Cycle

Reusability

Key Highlights

Solution Highlights• Jupiter was employed over ATDD tool Cucumber to run automated

acceptance tests• Adopted ATDD model and created acceptance tests at the

beginning of the project based on agreement of all key stake holders

Key Benefits • Automation on the go helps to kick start automation from day 1 of

the project• Integrated end to end test automation• Single repository for acceptance Criteria /test Scenarios, test

Scripts and test results

Automated execution to reduce the QA cycle efforts

Marketplace library can yield ~ 70% reusability

CoverageAutomation Coverage of QA Test Scenarios

Customer

BusinessNeeds

• For the Worlds largest Consumer Technology Provider

• Extensive Automated Regression Testing & Acceptance tests

User Story

Consolidate Acceptance

Criteria

Execute Feature

Files (Dev/QA)

Create Automated Step Definition using

JAVA (QA), which test the behavior

of the feature

Development (Dev)

User Accepta

nce

The business owner and the

business analyst build req

The BA, Dev and QA will elaborate the requirements

together

Create Cucumber feature file which

defines the requirement in plain structured

English language

1 2 3

4

5

6 7

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Summary

o Digital transformation disrupting enterprises and with rapid revolution in Digital – mandate is to leverage this great opportunity while protecting one’s own turf

o With the emerging imperatives – like explore and evolve, continuous refinement and co-validation, co-operate – more appropriate and new variants of agile delivery methodologies like ATDD, BDD, MDD etc are prominent

o Engineering & platform based solutions are enabling shorter life cycles, frequent releases, feedback enabled self-learning system evolutions

o Veracity of data and business assurance focus are becoming critical elements of QA in ensuring success of the digital solutions. Leverage of Intelligent QA systems for diagnostic and integrated delivery, automation first and continuous testing methods are fast emerging.

o With the real time case studies discussed – it is evident that the emerging methodologies are instrumental in success of digital deliveries

o Essential for QA to play Quality Engineering and Continuous validation and leverage intelligent QA systems for assuring veracity of digital data

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Q&A

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Author Biography

Srini Mangadu is Practice leader for Global Delivery Organization - Digital Business - Analytics & Information Management. Seasoned IT leader with over 25 years of total experience spanning across development & management of IT systems spanning across various industries across various geographies. He has managed complex programs / projects involving niche technology stack involving Analytics, Big Data and traditional data warehouse / Business Intelligence suite - leveraging both traditional waterfall & Agile methodologies.

Subramanian Kubendran (Known as Subbu) is Practice Leader @ Cognizant Digital Business. Subbu has 20+ years of professional experience and enthusiast in Data leveraged IT Solutions. He has worked as trusted advisor for a number of Fortune-500 customers – helping to build solutions that leverage data and insights for customer business solutions. Wiley certified Big-data Specialist, Advanced Analytics professional, Stanford certified on Advanced Portfolio Management – with active PMP Certification

Vinithra Ashok is a seasoned QA Process specialist in Analytics & info management. She has 16 years of IT experience covering wide range of project management across various industries. Her specialization is in Analytics & Business intelligence.

Jayasree Prabhakaran is Practice Leader with Cognizant Digital Business. Jayasree has 17+ years of rich experience in building solutions in Information Management space across domain including Telecom, Manufacturing and Retail Customer Services, Life science. She is leading Data QA practice championing inteligenDigital Data QA systems build and leverage. Jayasree holds a Mphil in Computer Science and is a Certified Scrum Master and PMP.

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References & Appendix

o http://www.gartner.com/technology/topics/trends.jspo http://www.itworldcanada.com/article/digital-transformation-is-disrupting-quality-assurance-too-

capgemini/390418o http://www.cigniti.com/webinars/qa-digital-transformation-changing-organizations/o http://www.cigniti.com/blog/10-emerging-trends-in-software-testing-predictions-for-the-next-decade/o https://www.forbes.com/forbes/welcome/?toURL=https://www.forbes.com/sites/danielnewman/2016/

03/01/exploring-the-future-of-digital-transformation-and-disruption/&refURL=&referrer=#528387fb5bdc

o https://www.cio.com/article/3149977/digital-transformation/8-top-digital-transformation-stories-of-2016.html#tk.cioendnote

o https://www.forbes.com/sites/benkerschberg/2017/03/01/how-digital-disrupts-operations-and-business-processes-as-well-as-customer-experience/#a77ab4054667

o https://www.agilealliance.org/glossary/bdd/o https://cucumber.io/o http://jbehave.org/o https://en.wikipedia.org/wiki/Behavior-driven_developmento https://en.wikipedia.org/wiki/Model-based_testingo https://en.wikipedia.org/wiki/Model-driven_engineering

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Thank You!!!