Emerging methodologies for Project management in Digital Era

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Srinivasan Mangadu – Sr Director Global Delivery office Analytics & Information ManagementSubramanian Kubendran – Practice Leader – Analytics & Information Management Vinithra Ashok – Sr. Manager – Delivery Excellence– Analytics & Information Management

Cognizant Technologies Ltd

Table of Contents

I. Abstract.............................................................................................................................................................................................................................................. 3

II. Introduction & overall Context........................................................................................................................................................................................................... 4

III. Change in business context in Digital Era........................................................................................................................................................................................... 4

IV. Key drivers & factors driving the overall growth................................................................................................................................................................................7

V. Increasing Importance of QA and Data certification in Digital Delivery............................................................................................................................................10

VI. Project management methodologies for Digital Era.........................................................................................................................................................................14

a. ATDD: Acceptance Test Driven Development...............................................................................................................................................................................16

b. BDD – Behavior Driven Development........................................................................................................................................................................................... 19

c. MDD – Model Driven Development.............................................................................................................................................................................................. 21

d. Dev-Ops Delivery & Dev-Ops QA:................................................................................................................................................................................................ 24

VII. Summary.......................................................................................................................................................................................................................................... 28

I. Appendix........................................................................................................................................................................................................................................... 29

II. References & Appendix.................................................................................................................................................................................................................... 29

III. Author Biography............................................................................................................................................................................................................................. 30

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I. Abstract

With the data explosion (projected 300 times growth of digital content between 2005 to 2020) disrupting & redefining business opportunities across the industries with digital imperatives like AI, Analytics, IoT, Cloud etc, “differentiated positioning”,  “staying ahead among the peers”, “Increased need for Trust on Data”  have become key among the critical success criteria for our customers.  Business Insights and recommendations at real-life speeds, Data not only bring in value but also monetization,  newer forms of cross industry offerings … are becoming common “asks” and are becoming  “need of hour to stay in game”. This triggers the exponential needs for rapid development methodology which can enable teams to build systems with evolving requirements, embrace newer niche technology stack with Best in class quality outputs.

 Agile methodology flavoured with niche variants like Acceptance driven development (ADD), Test data driven development (ATDD), Behavioural driven development (BDD), Model driven development (MDD) are getting more significance.  This coupled with Continuous Integration /Continuous Delivery (CI/CD) seem to demonstrate great results in this Digital context.  In addition to protecting investments and generating remarkable ROI, this has improved overall customer experience and satisfaction.  With customer being part of process from the start, their ‘buy in” is factored at every step of the product development – which becomes one of the key to successful delivery.

 This white paper will detail out how newer opportunities, challenges in product development, testing & QA in Digital context are addressed by flavours of Emerging Agile methodologies – taking real life use cases and benefits.  

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II. Introduction & overall Context

Digital trends have started impacting every citizen in the world & our ecosystem. Thanks to advancements in digital technologies, mobility, social coupled with affordability & reach to everyone. This has started impacting overall business landscape & it’s becoming imperative to leverage this & take advantage. In some scenarios this become mandatory to protect one’s position on the ground. With this background, all the corporates across the globe has started thinking towards this and are embarking on journeys to on board into new Digital era. During this journey technology becomes one of the key ingredient & more focus and spend is towards this. In this white paper we will cover what kind of changes are happening in Digital Era, who are the key influencers & new players, what kind of technology landscape & how do we execute these projects flawlessly, in an agile fashion.

III. Change in business context in Digital Era

We are into Digital era where Digital disruptions are happening at enterprise level impact all the layers – Business, Operations, Technology. Customer becomes the key to digital transformation – effectively engaging them at every touchpont in the experience lifecyle becomes a one of the key asks of the day.

It’s all about the growth of operational processes and business activities augmented by efficient and strategic use of technology. With Digital, technology has climbed up the value chain – while technology used to be enabler for business, with Digital disruptions – from mobile to cloud to social – technology has become business, has been climbing the value chain from enabler to Value creator.

As per Gartner’s report 125,000 large organizations are launching digital business initiatives and CEO’s expect their Digital revenue increase by more than 80% by 2020. Also IDC expects that increase in percentage of enterprises creating advanced digital transformation initiative will be more than double from 22% to almost 50%. Also by 2018, 35% of IT resources will be spent to support creation of new

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Digital revenue streams, however by 2020 this will increase to 50% and will be tied to digital transformation initiatives. Interesingly new players cutting across various industry domains leads to new business models.

With increasing connected world (thanks to IoT, 35 billion connected devices by 2020) interesting trend is business are moving from industry of their core capability into adjacent capabilities leveraging technology & digital transformations. Also in some cases completely new busines models are getting discovered.

For eg

Financial App Moven connecting all of a customer’s financial accounts across multiple banks Apple TV streming video content direct from redbull TV and netflix News aggregator Flipboard filtering preferred content from various publication to its single app Health Gadgets - Our daily living style getting monitored by Fitbit, Jawbone, Apple iWatch Sharing economy – Uber, Aitrbnb, Peers (peer to peer platform that offers home sharing insurance products), Audi (which allows

upto 5 people to buy a car, then cuts a set of keys for each one) Sports drink Gaitorade (smart bottle that will connect with mobile app to manage person’s hydration level) Amazon “Dash button” at house – when pushed will generate “daily orders” automaticaly for day delivery

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Coupled with Data explosing (thanks to Social), real time avaialbity & ease of access by mobile – new business models are well positioned to leveraage this enhance their services.

While Digital disruption impacts all the layers of the enterprise – its important to understand the overall dynamics & key factors impacting each layer. The digital transformation impact will be felt at all the layers – Business, Operations and technology.

At each layer, the technology landscape, KRAs, Value proposition varies based on the stakeholders & overall variation in objectives.

While Business layer focusses on footprint expansion, growth, shared/ integrated Digital services leading to Enhanced Digital Business, whereas operations focuses on processes by Analytics based Segmentation, productivity by remote working and Technology’s main focus is efficiency, automation etc. though all these have varying objectives & expectations, Digital Disruptions happens at enterprise level impacting all the three layers and there by enriching the Customer experience in Selling capabilities & touch points.

This leads to defining enterprise Digital vision and strategy. Adopting bimodal technology solutions, adopting latest Digital tech stack etc becomes key imperatives for Enterprise leaders.

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IV. Key drivers & factors driving the overall growth

In the Pre-Digital era, while technology was playing support / enabler role, Technical groups & Leadership used to be the driving factors. But when it comes to Digital era, technology has started creating direct value for Business. Because the today’s competitive world is won by speedy, which is to bring the routine ability to do the impossible ahead of the competition, it is essential to understand and master the new technologies and enhance the skills. Tools and technologies have the power to fuel the insight and realize the imagination at pace and scale which is previously unimaginable. For example,

• Robot assistants playing secretary roles in scheduling meetings day planner

• Business content authored by machines -Ability to proactively assemble & deliver information thru’ automated composition engines

• Responding to support from non-human personal assistants (connected things)

• Delivery by Drones

• Companies treats Data as a Business asset and there by Data Monetization generates measurable economic benefits

In this context, various new players from Business, Operations become part of Digital journey and act as key influencers. For instance CDO (Chief Digital officer), CMO (Marketing officer) along with COO (chief operating officer) plays a critical role in Digital journey along with CTO, CIO. While Technology innovation, efficiency be the key driver for CIO, CTO – productivity, cost savings, Investment protection / ROI could be key drivers for COO. Primary focus of CDO, CMO could be staying ahead of the curve, leveraging opportunity window and business expansion.

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The cost for companies trying to attack one’s industry and steal market share is quite low these days, and a critical factor in maintaining a good customer experience is delivering stable products with high quality. As organizations invest heavily in digital transformation programs, Quality Assurance and Testing play a critical role.

Some of the key factors influencing these are:

Machines taking active role in enhancing Human endeavor Increased use of computing machines in Decision making process Improved & renovated customer experience

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QualityFoot print Expansion Ahead of the curve Efficiency

Technology

Degrees of complexity 

Operations

Business

Some of the key challenges impacting the Digital transformation journey are: Changes in the customer behavior

Elevated global competition Adoption of social media Emerging trends in Mobile / Cloud / Big Data / Analytics

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V. Increasing Importance of QA and Data certification in Digital Delivery

With the digital disruption fuelled through data explosion outpacing the technology, more and more the next generation competitive advantage on digital solutions are expected such as

Business Insight at Real-life speeds Personalized content that follows you Questions that arise automatically through self/deep learning of data 300x times growth in digital content projected between 2005 to 2020 80% of data is unstructured and growing 15x rate of structured data – leads exploration and continuous learning than defining

boundary Market evolution from traditional descriptive applications to self-learning, self-service applications – is fueled by

Natural Language Processing Machine Learning and Forecasting Big-data & IoT Analytics nXg Device Analytics – sophisticated analytical tool to develop a near real-time insights into device functional status

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With these data explosion, core of QA is more and more focused on data accuracy, data integration and communication means and presenting below increasing QA trends

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Measurement of digital delivery success gets broken down to measuring business metrics, Technical, Delivery and Testing Metrics.

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In the new digital world, users expect a high quality, seamless digital customer experience. Looking into digital behaviours and touch points of your customers helps you succeed by learning where and how to introduce value into the entire customer chain.

The ultimate goal for companies is to ensure a truly consistent experience across all channels of interaction. Applications need to be made available in every category of devices (phones, tablets, notebooks, wearables) and should provide a seamless customer experience based on real user behaviour. QA organizations can use optimized test automation for repeated tests on various devices, testing all browsers and operating system stacks efficiently.

Adapting to new and latest technologies demands higher process maturity. Quality Assurance is becoming an important function for organizations who are effectively embracing the digital transformation.

Quality Assurance plays a vital role in responding to these challenges. It’s essential for organizations that are dealing with digital transformation to have an appropriate digital assurance strategy, with the right tools, methodologies and measures to assure customer experience.

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VI. Project management methodologies for Digital Era

Digital transformation is not only changing our lives and businesses but also have very much reshaping and speeding up the Project Delivery methodologies/ models. The planning and execution of an innovative projects in today’s digital era can no longer be done with the same methodologies, models, & tools and not even with the same pace. As the industry will be in urge to attain the increased time-to market results which requires the following key factors: speed and flexibility so project managers have to adapt their best approaches, methodologies & tools.

For Digital solutions - Signed-off Requirements, Design – No more a reality??!!!

With the digital solutions more leaning towards continuous evolution of requirements and iteration ideas till result, the traditional methodologies and model of QA follows Dev is becoming evidently irrelevant.

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Digital Imperatives that is shifting focus on delivery methodologies:

Digital imperatives like – shorter cycle to release, faster time to market, measure results/feedback and continuous refinement, self-learning systems, continuous trained predictive models warrant the methodologies that move away from software development life-cycle view to follow product culture.

1. Explore & Experiment2. Co-Ideate & Co-Innovate3. Continuous refinements & Co-validations4. Co-Operate

Essence of successful digital delivery team is increasingly relying on

Continuous Ideas to MVP (Minimum Viable Product)

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PSPI (Potentially Shippable Product Increment) after every short iterations

Cross-functional team that works together – no more Chinese wall between Dev and QA

Multi-learning “Team Members” – who do everything.

No upstream/downstream teams

Iterations, stand-ups are relevant – but not the ‘essence’

Following section discusses in brief on emerging methodologies that are increasingly getting adopted given more relevance to the digital imperatives. We will be discussing those methodologies and how QA plays a role in each with real life case in point

a. ATDD: Acceptance Test Driven DevelopmentAcceptance Test Driven Development (A-TDD)– emphasizing focus on real examples of intended results/objectives – there by elaborate requirements and criteria for acceptance.

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This brings in evolving requirements and verification/validation hand-in-hand and a business analyst, developer and QA work together to ensure iterative refinements build to meet the acceptance tests.

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Case-study: Insurance Claims fraud Analytics solution – leveraging ATDD approach to mature models and co-validate

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b. BDD – Behavior Driven Development

Behavior-driven development is an extension and variation of test-driven development: development that makes use of a simple, domain-specific scripting language (DSL). These DSLs convert structured natural language statements into executable tests. The result is a closer relationship to acceptance criteria for a given function and the tests used to validate that functionality.

BDD focuses on:

Where to start in the process What to test and what not to test How much to test in one go What to call the tests

How to understand why a test fails

At the heart of BDD is a rethinking of the approach to the unit testing and acceptance testing that naturally arise with these issues. For example, BDD suggests that unit test names be whole sentences starting with a conditional verb ("should" in English for example) and should be written in order of business value. Acceptance tests should be written using the standard agile framework of a user story: "As a [role] I want [feature] so that [benefit]". Acceptance criteria should be written in terms of scenarios and implemented as classes: Given [initial context], when [event occurs], then [ensure some outcomes].

Principles of BDD

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BDD essentially states that for each unit of software, taking the software intended behavior in consideration what a software developer must:

define a test set for the unit first; make the tests fail; then implement the unit; Finally verify that the implementation of the unit makes the tests succeed.

Behaviour-driven development specifies that tests of any unit of software should be specified in terms of the desired behavior of the unit. Borrowing from agile software development the "desired behavior" in this case consists of the requirements set by the business — that is, the desired behavior that has business value for whatever entity commissioned the software unit under construction. Within BDD practice, this is referred to as BDD being an "outside-in" activity.

Case-study: BDD enabled Analytics solution for supply chain optimization for a Manufacturing & Logistics Customer

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c. MDD – Model Driven Development

Model-driven engineering (MDE) is a software development methodology that focuses on creating and exploiting domain models, which are conceptual models of all the topics related to a specific problem. Hence, it highlights and aims at abstract representations of the knowledge and activities that govern a particular application domain, rather than the computing (f.e. algorithmic) concepts

Overview

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The MDE approach is meant to increase productivity by maximizing compatibility between systems (via reuse of standardized models), simplifying the process of design (via models of recurring design patterns in the application domain), and promoting communication between individuals and teams working on the system (via a standardization of the terminology and the best practices used in the application domain).

A modelling paradigm for MDE is considered effective if its models make sense from the point of view of a user that is familiar with the domain, and if they can serve as a basis for implementing systems. The models are developed through extensive communication among product managers, designers, developers and users of the application domain. As the models approach completion, they enable the development of software and systems.

Some of the better known MDE initiatives are:

Model-Driven Architecture (MDA) which is leveraged by several of their standards such as Meta-Object Facility, XMI, CWM, CORBA, Unified Modeling Language (to be more precise, the OMG currently promotes the use of a subset of UML called fUML together with its action language, ALF, for model-driven architecture; a former approach relied on Executable UML and OCL, instead), and QVT.

The Eclipse "eco-system" of programming and modelling tools represented in general terms by the (Eclipse Modeling Framework). This framework allows the creation of tools implementing the MDA standards of the OMG; but, it is also possible to use it to implement other modeling-related tools.

Model-based testing is an application of model-based design for designing and optionally also executing artifacts to perform software testing or system testing. Models can be used to represent the desired behavior of a system under test (SUT), or to represent testing strategies and a test environment. The picture on the right depicts the former approach.

A model describing a SUT is usually an abstract, partial presentation of the SUT's desired behavior. Test cases derived from such a model are functional tests on the same level of abstraction as the model. These test cases are collectively known as an abstract test suite. An abstract test suite cannot be directly executed against an SUT because the suite is on the wrong level of abstraction. An executable test suite needs to be derived from a corresponding abstract test suite. The executable test suite can communicate directly with the system under test. This is achieved by mapping the abstract test cases to concrete test cases suitable for execution. In some model-based testing environments, models contain enough information to generate executable test suites directly. In others, elements in the abstract test suite must be mapped to specific statements or method calls in the software to

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create a concrete test suite. This is called solving the "mapping problem". In the case of online testing (see below), abstract test suites exist only conceptually but not as explicit artifacts.

Tests can be derived from models in different ways. Because testing is usually experimental and based on heuristics, there is no known single best approach for test derivation. It is common to consolidate all test derivation related parameters into a package that is often known as "test requirements", "test purpose" or even "use case(s)". This package can contain information about those parts of a model that should be focused on, or the conditions for finishing testing (test stopping criteria).

Case Study of MDD leveraged Guided Analytics solution delivery powered by ML algorithms

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d. Dev-Ops Delivery & Dev-Ops QA:

DevOps: Enables Enterprise capability for continuous delivery that accelerate release of applications into production and sustain continuous feedback and improvement across the software lifecycle.

DevOps ensures following key design priniciples:

Collaborative Development – Increased collaboration between teams Continuous Release and Deployment – for increased delivery speed and

frequency Continuous Testing – Integration of software testing with deployment and

operations Continuous monitoring – Improve quality by monitoring performance

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DevOps

DevOps is about methods for improving

COLLABORATIONINTEGRATIONCOMMUNICATION

Across the IT ecosystem

Example of Dev-Ops Pipeline that is powered with continuous integration engines and parallel continuous testing that works with each other for release pre and post validation. Handshake between configuration, build, defect management, release management systems with defined pre and post validations with automated test suite helps to validate a build for automated release/reject workflow as depicted below.

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Case Study:  Dev-Ops with Continuous Testing piplein impelementation for US based bank 

Additional industry based methodologies re-applied with digital context – esp where operational efficiency is in focus:

Kanban: Kanban is the generally well-known”taskboard” method, where a task goes through a number of phases – usually, to do, in progress, done. Using this method you would normally not use sprints as you would prioritize and work on tasks directly based on the most important ones in the backlog. Also in Kanban, there is typically a “work-in-progress” limits so you limit the amount of work that’s in progress in parallel. This increases team focus on getting things done instead of starting new things.

Lean:The main concept of the lean project management method is to get the most possible value with the least possible waste. Furthermore, it tries to avoid bottlenecks, and thus increase growth and productivity. This method is often used in “On-request”-

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productions. Eg, car manufacturing. In the lean whole point is to constantly change the workflow so everything is performed as efficiently as possible.

Value Theme matrix – aligning digital value drivers and use cases with emerging and traditional methodologies:

Value theme Value Driver Program level

Driven by Use Cases / Examples Methodology

Shorter cycle time Technology innovation Dept level Business Amazon “Dash button”, connected cars, Telematics

TDD / BDD

Faster time to Market New Biz Model Dept Level Business Sharing economy (Ride share, AirBNB)

MDD

Faster time to Market New biz Model (fluid reqs/ limited clarity /

more example driven)

Dept level Business AI, Analytics use cases TDD

Better design / User Exp/Fail fast

User Exp/ Dept level / Small

Enterprise

Business / Technology

Mobile BI & analytics, Health gadgets (Fitbits, Apple iWatch)

XP/Scrum XP/ BDD / TDD

Efficiency / productivity Efficiency / operational Excellence

Large entrprise

Operations / Technology

DB upgrades/ Migration / Tool rationalization /

SAFe at Portfolio  / Agile with DevOps (Hybrid)

Waste elimination Productivity / Efficiency / Operational Excellence

Dept level Operations / Technology

DB upgrades/ Migration / Tool rationalization / BI tool

migration

Agile with DevOps / CICD / Kanban/ Lean

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Legacy modernization/ Data offload / greenfield Big Data

lake / DW build

Any scale Technology / Operations

EDW build / Data lake / Data offloading from Mainframe to

Hadoop

Waterfall

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VII. Summary

With Digital transformation disrupting enterprises and with rapid revolution in Digital era – it’s becoming more imperative to leverage this great opportunity while protecting one’s own turf. 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 enabling shorter life cycles, frequent releases, feedback enabled self-learning system evolutions. Also given the data explosion with digital – veracity of data and business assurance focus are becoming critical elements of QA in ensuring success of the digital solutions. Test automation techniques and continuous testing methods are fast emerging. With the real time case studies discussed – it is evident that the emerging methodologies are instrumental in success of digital deliveries. We have covered high level approach of mapping these to various use cases and business scenarios. Though there is no “one size fits all” approach possible in current context, it’s very important to assess fitment of each methodology against value themes.

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I. Appendix

II. References & Appendixhttp://www.gartner.com/technology/topics/trends.jsp

http://www.itworldcanada.com/article/digital-transformation-is-disrupting-quality-assurance-too-capgemini/390418

http://www.cigniti.com/webinars/qa-digital-transformation-changing-organizations/

http://www.cigniti.com/blog/10-emerging-trends-in-software-testing-predictions-for-the-next-decade/

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

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

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

https://www.agilealliance.org/glossary/bdd/

https://cucumber.io/

http://jbehave.org/

https://en.wikipedia.org/wiki/Behavior-driven_development

https://en.wikipedia.org/wiki/Model-based_testing

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https://en.wikipedia.org/wiki/Model-driven_engineering

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III. Author Biography

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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 working as Practice Leader with Cognizant Technology Solutions. Subbu has 20+ years of progressive professional experience with specialization in delivering Data leveraged IT Solutions delivery. 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. Being a technology enthusiast, Subbu has built a large team of quality engineers who specialize in tools/automation based quality assurance for data engagements. He is 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.

 

THANK YOU!

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