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Using Adaptive Scrum to Tame Process Reverse Engineering in Data Analytics Projects

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Cognizant 20-20 Insights
August 2021
Using Adaptive Scrum to Tame
Process Reverse Engineering in
Data Analytics Projects
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Using Adaptive Scrum to Tame Process Reverse Engineering in Data Analytics Projects

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Organizations rely on analytics to make intelligent decisions and improve business performance, which sometimes requires reproducing business processes from a legacy application to a digital-native state to reduce the functional, technical and operational debts. Adaptive Scrum can reduce the complexity of the reproduction process iteratively as well as provide transparency in data analytics porojects.

Organizations rely on analytics to make intelligent decisions and improve business performance, which sometimes requires reproducing business processes from a legacy application to a digital-native state to reduce the functional, technical and operational debts. Adaptive Scrum can reduce the complexity of the reproduction process iteratively as well as provide transparency in data analytics porojects.

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Using Adaptive Scrum to Tame Process Reverse Engineering in Data Analytics Projects

  1. 1. Cognizant 20-20 Insights August 2021 Using Adaptive Scrum to Tame Process Reverse Engineering in Data Analytics Projects Organizations rely on analytics to make intelligent decisions and improve business performance, which sometimes requires reproducing business processes from a legacy application to a digital-native state to reduce the functional, technical and operational debts. Adaptive Scrum can reduce the complexity of this reproduction process iteratively as well as provide transparency in data analytics projects. Executive Summary Data analytics helps organizations improve their business performance and achieve business objectives. But legacy application modernization required to keep existing business processes running has saddled numerous organizations with functional, technical and operational debts that create obstacles to deploying the latest data analytics. Many analytics applications must therefore be reproduced — or reverse engineered — from legacy applications and implemented using cutting-edge digital technology.
  2. 2. Cognizant 20-20 Insights 2 / Using Adaptive Scrum to Tame Process Reverse Engineering in Data Analytics Projects The Scrum framework is highly recommended for resolving complex business problems. Reverse engineering projects are inherently complex, and often create uncertainty from the onset. As opposed to the waterfall approach, the Scrum method allows the project team to reduce complexity iteratively, which enables quicker release of the product to users. Several success criteria for reverse engineering projects are fulfilled better using the Scrum approach. The Scrum development process for reverse engineering follows an incremental approach to produce a releasable product. Although Scrum appears to be well-suited for reverse engineering projects, there are several challenges that need to be overcome before the business value can be realized. This white paper discusses proven best practices to mitigate the management and technical challenges, based on a complex project that we delivered to a leading life sciences organization.
  3. 3. Cognizant 20-20 Insights 3 / Using Adaptive Scrum to Tame Process Reverse Engineering in Data Analytics Projects Business value through reverse engineering The mastery of digital domains such as cloud, analytics, the Internet of Things (IoT), digital engineering, machine learning (ML), artificial intelligence (AI), etc. has become essential for businesses to remain competitive. Analytics plays a major role in providing critical information to improve business performance using applications such as big data analytics, predictive analytics, retail analytics, supply chain analytics, etc. During the COVID-19 pandemic, 58% of 1,800 respondents in a 2020 Gartner CIO survey1 said that they would spend more on business intelligence and data analytics in 2021, which was second only to cyber and information security spending. As digitization of key business processes accelerates, data analytics is playing a key role in enabling organizations to make intelligent and informed decisions. Analytics applications allow businesses to analyze, predict and track specific KPIs and thus cap overall costs. In the COVID era, organizations must balance investments to keep legacy applications alive and investments in the latest digital technologies. The practice of reverse engineering involves reproducing the business scenarios and processes from legacy applications in state-of-the-art mobile apps or internet/intranet applications. Reverse engineering of business processes can be defined as the practice of analyzing, extracting and reproducing the business processes in whole or in part from a legacy application to a modern application in order to reduce the functional,technical and operational debts of the legacy application in the business environment. The debts are application activities or practices that are not best-in-class industry standards (e.g., shortcuts,work-arounds or manual activities) and hence need to be replaced with available best in-class practices so the business can remain competitive. For instance, a state-of-the-art application could implement functionalities that are missing in the legacy application in a more sustainable manner. They can be built on top of the core business functions or processes in the legacy application to reduce the functional debts and retain the core and functioning processes within an organization. As an example, a predictive analytics dashboard for the warehouse shows the replenishment orders required based on the ordering process that is currently implemented in the existing ERP system. Technical debts include issues such as scalability, performance, etc. of the legacy application. One example would be a low number of concurrent mobile users who could access the ERP system. Operational debts in the legacy application include business processes that require manual processing or intervention and that keep the operational costs high within an organization. For example, an operational database that need to be restarted once every week to avoid operational disruptions is an operational debt. Reverse engineering of business processes can be defined as the practice of analyzing,extracting and reproducing the business processes in whole or in part from a legacy application to a modern application in order to reduce the functional,technical and operational debts of the legacy application in the business environment.
  4. 4. Cognizant 20-20 Insights 4 / Using Adaptive Scrum to Tame Process Reverse Engineering in Data Analytics Projects Reverse engineering waves There have been three major waves in reverse engineering since the 1990s (see Figure 1). The Y2K problem, or millennium bug, saw the first big wave of code reverse engineering in the early 1990s. Program codes were rectified in source systems to avoid application malfunctioning. The second wave came with the internet boom period from the early 2000s to 2010 when businesses reengineered their applications to improve their web presence and ventured into e-commerce.2 Web applications were reengineered to connect to the enterprise legacy applications. The third and current wave is a result of the ongoing pivot to digital and the proliferation of digital-native (mobile, cloud-native, no-code/low-code, etc.) apps that started in the 2010s. This wave has seen the demand rise for processes to be reverse engineered in state-of-the-art applications that provide better insights by allowing users to develop their own dashboards and thus make smarter decisions. These applications use data from the legacy operational systems or analytics applications that are built using the latest platforms such as Qlik, Tableau, etc. Mobile apps and business services are being redefined, but the business processes are reverse engineered for existing business logic, rules and configurations, and reproduced for various platforms like Apple, Google Play or Microsoft stores. Further, the deployment of no-code/low-code platforms from major analytics vendors provides self-service or on- demand analytical capabilities. These platforms are supported by AI and ML models with the ability to analyze, classify, extract, transform and load data with less user involvement and thus allow the businesses to take intelligent, real-time decisions. Code reverse engineering Application reverse engineering Process reverse engineering • Y2K problem • Period: 1990-2000 • Business value: Keep applications running • Internet boom • Period: Late 1990s-mid-2000s • Business value: Connect legacy applications to the web • Digital disruption and proliferation of mobile apps • Since 2010 • Business value: Reuse key business processes and make intelligent decisions Figure 1
  5. 5. Cognizant 20-20 Insights Figure 2 5 / Using Adaptive Scrum to Tame Process Reverse Engineering in Data Analytics Projects Reverse engineering: Success criteria How to achieve reverse engineering project success Organizations are aiming for specific business goals when they venture into reverse engineering projects, such as increasing revenue from customers, targeting new millennial customers, reducing cost/ inventory, etc.The waterfall approach to building software applications continues to be the dominant project management methodology, since it allows organizations to tackle business requirements in a structured and phased manner.The proliferation of Agile frameworks such as Unified Process, Scrum, etc. in the 2000s allowed organizations to develop applications by adapting quickly to ever-changing business dynamics. Scrum is nowadays the most popular Agile framework,3 with many IT professionals even viewing Scrum as the default.The success of waterfall and Scrum approaches can be evaluated using specific criteria in reverse engineering projects. The success criteria in a reverse engineering project need to be defined well to ensure their fulfilment by the end of the project. These criteria are primarily related to the reduction of functional, technical and operational debts and to how fast the application can be released to the market. Figure 2 defines the success criteria of both waterfall and Scrum projects based on our client engagements. Success Criterion Waterfall Scrum Reduce functional debt Initially determined functional debt can be amortized Initially determined functional debt as well as changing functional needs can be addressed Reduce technical debt Initially determined technical debt can be amortized Initially determined technical debt as well as changing needs can be addressed using enforced engineering practices from day one Reduce operational debt Initially determined operational debt can be amortized Initially determined operational debt as well as changing needs can be addressed Accelerate time-to-market Initially determined timelines can be achieved 20% to 50% faster time-to- market than waterfall projects is possible
  6. 6. Cognizant 20-20 Insights 6 / Using Adaptive Scrum to Tame Process Reverse Engineering in Data Analytics Projects In a waterfall project,the functional requirements are fixed in the beginning and the reverse engineering project could reproduce the functionalities identified by the business.A Scrum project could further look into changing business needs and implement more functionalities than initially planned in the waterfall project within the same timeframe,which brings value to business; the product owner (PO) can prioritize the dynamic requirements of the business throughout the project based on the value they generate for the business. Technical debt in terms of nonfunctional requirements (such as scalability, performance, usability, reliability, security, etc.) that are in scope from the outset can be fulfilled by the waterfall project; Scrum could further address changing needs, such as security requirements, if emerging threats are anticipated. Operational debts (such as data quality, maintainability, accessibility, extensibility, etc.) can be amortized in both waterfall and Scrum projects; Scrum projects can have improved data quality as the PO will continuously verify the data model and rules as the project progresses. Further emerging operational changes can be addressed in Scrum projects; however, it is highly unlikely that major operational changes will emerge during the project. The product team (which includes the PO, Scrum master and the development team) focuses on the smaller scope of Sprint goals (goals within the Sprint, a time-boxed duration of one month or several weeks) rather than the larger scope in waterfall projects, which allows them to deliver against impending deadlines. This focus in Scrum projects results in better productivity and thus allows 20% to 50% faster time-to-market of the product than in waterfall projects, depending on the Agile team’s self-managing skills. More success criteria can be determined based on specific business goals.Each data analytics project has its own business goals covering finance, management and quality.While both Scrum and waterfall projects can achieve the initially determined specific business goals, Scrum is better suited to resolve uncertainties involved in reverse engineering projects.
  7. 7. Cognizant 20-20 Insights 7 / Using Adaptive Scrum to Tame Process Reverse Engineering in Data Analytics Projects How Scrum development processes boost reverse engineering Scrum projects have fixed budgets and schedules, whereas the scope is variable. On the other hand, waterfall projects have fixed scopes and variable budgets and schedules. The complexity and uncertainty of requirements are the main criteria that determine the choice of Scrum over waterfall in a data reverse engineering project. Requirements that exhibit low uncertainty and change are better managed through the waterfall approach. A waterfall project takes a linear-phased approach, coursing through sequential phases — from requirements and analysis, through design, develop, test and deploy (see Figure 3). This linear approach is often highly challenged for reproducing business processes, especially when the required documentation and the SMEs of legacy applications are no longer with an organization. Scrum is usually recommended for generating “value through adaptive solutions for complex problems.”4 The adaptive approach is best suited to projects that exhibit high rates of change and uncertainty in their requirements and technical degrees.5 Process reverse engineering is a complex problem that can be resolved well using Scrum, as the method’s iterative and incremental development reduces the complexity as the product evolves. It provides business value through feedback loops and frequent deliveries. Figure 4 (next page) illustrates how the Scrum reverse engineering process results in improved business value, nearly end to end. The product vision and the high-level design of the application will be developed in the design Sprint. In the Sprint planning meeting, the product team will ideate about the core business processes to be reproduced from the legacy application as well as new business processes to be implemented. The initial discussion of functional, technical and operational debts in this Sprint could also involve primary business and technical stakeholders who are affected by the legacy system. Based on the product vision, along with prioritization of the product backlog developed by the PO, the product team will analyze and finalize the architectural and functional design. Software engineering aspects such as test automation, continuous integration (CI) and continuous deployment (CD) will be set up in this Sprint. This Sprint also lays the foundation for the Agile execution. The core business processes and immediate user stories (features from the user perspective) that will be implemented in the upcoming Sprints will be elaborated with their definition of done (DoD) as well as definition of ready Waterfall phases Final project outcome Requirements & Analytics Design Develop Test Deploy Figure 3
  8. 8. Cognizant 20-20 Insights 8 / Using Adaptive Scrum to Tame Process Reverse Engineering in Data Analytics Projects (DoR) and finalized. The development team will receive an overview of the business and technical requirements of the product with the inputs from the business analyst (BA). The user stories groomed in the design Sprint provides a solid basis for the development team to implement in the development Sprints. The high-level design, product scope and functional dependencies will then be reviewed in the Sprint review meeting. In the development Sprint,the product team will go through several iterations to produce an increment that could potentially be released to the users.In the requirements and analysis phase,the development team assesses business requirements that are formulated as user stories by the PO in the product backlog. It is key that as much business and technical understanding of the process be reverse engineered for the development team to commit to the user story in the Sprint planning meeting. During the design phase, the developer will work closely with the BA to translate the functional specifications into technical design specifications with their DoD. This involves information related to the main data sources, entities and fields to start work with. Further, the developer will look for business logics/rules, relationships and data scenarios to develop the first version of the data model. As Figure 4 depicts, the design, develop and test phases will be repeated until the DoD is completed for the user story. Reverse engineering the Scrum process Figure 4 Plan & Ideate Review Analyze F i n a l i z e Design Sprint Development Sprint N-1 Development Sprint N T e s t T e s t S c r een S c r een Fee d b a c k Fee d b a c k ( I n f o r m a l ) ( I n f o r m a l ) Bui l d Bui l d Requirements & Analysis Requirements & Analysis Design Design Develop Develop Test (Formal) Test (Formal) Deploy Deploy Sprint N’s cumulative outcome Sprint N-1’s outcome Data Model Data Model
  9. 9. Cognizant 20-20 Insights 9 / Using Adaptive Scrum to Tame Process Reverse Engineering in Data Analytics Projects The iterative design-develop-test phases encompass the development of initial data model testing using production data extracts and involving the BA to informally test the data model. It is important that the PO or BA who has access to the productive source system, of which the process is reproduced, is involved to provide the latest data extract and scenarios to test the data model. Once the data model is tested informally by the developer, the PO screens it against business requirements and provides feedback, which will then be integrated into the model. Based on the production data and iterative design- develop-test cycles, new business rules will be added and/or data model changes will be made to produce the next build. Developers will check the business rules and data model several times with the PO before the formal testing and deployment will be conducted, after which the user story will be formally closed in the Sprint review meeting. There are a few key differences in reproducing business processes using reverse engineering compared with normal Scrum development.As clear design specifications and the full picture of functional dependencies are missing from the outset, the design-develop-test phases will involve several cycles — in a trial-and-error manner—to develop and refine the business rules and data model.The complexity will subside gradually as the data model will be verified incrementally by the PO. In order to test with real scenarios, access to production data examples is critical. Developers need direct access to the production data or production environment in the development environment. Reverse engineering data analytics challenges and recommendations Depending on the complexity of business processes and the information asymmetry regarding the business processes between the source and the to- be-implemented processes, it can be a daunting task to get the Sprint increment released to end users on time.There are several management and technical challenges that need to be managed well to meet the business requirements. Figure 5 describes the best practices to mitigate the challenges, based on our analytics project client experiences. Overcoming reverse engineering obstacles Figure 5 Challenges Best Practices for Mitigation Missing SMEs to walk through key business processes or scenarios ❙ PO and development team to work together using key business rules and production data ❙ PO and BA to leverage existing documentation and data and define the DoD before the Sprint planning Missing special cases or specific processes in the initial Sprint or iteration ❙ Assign requirements having high uncertainty with corresponding story points Difficulties in announcing release dates ❙ MVP to be defined; announce enhancements in the upcoming release for known issues ❙ Continuous or frequent showcasing to end users to get feedback Continuous reworking of program code and data model ❙ DoD to be clarified before the start of Sprint with technical aspects ❙ Work using key examples to capture all the possible scenarios Availability of production data that is in sync with all the data sources in the development environment ❙ Get business approval and maintain a dedicated DevOps team
  10. 10. Cognizant 20-20 Insights 10 / Using Adaptive Scrum to Tame Process Reverse Engineering in Data Analytics Projects The PO may not be thoroughly familiar with all the business processes or scenarios to be reproduced from the source system, and could become overwhelmed by the situation. He or she will have to check with several process experts who may fully or partially know the processes to be rebuilt. Because of the lack of SMEs with mastery of business processes in the organization, the PO, BA, technical experts and developers need to work closely using key business rules and production data. The DoD of the product backlog item needs to be defined by the business and, whenever possible, also from the technical perspective before the Sprint planning meeting. The backlog review and grooming meeting needs to be used effectively to discuss the required details such as the DoD and DoR. Nevertheless, there will be several special cases or specific processes that will not be captured in the design Sprint. From the project management perspective, the product team should be encouraged to consider difficulties for covering complex requirements, of which dependencies are not fully understood, and assign the corresponding risks and efforts as story points in the user story. Special cases remain a challenging part in unravelling the business processes and the product team will need to re-plan a Sprint as soon as such cases are found during a Sprint. Agile budgeting in an Agile organization allows the PO to secure the funding required to create the next increment in a project. However, because of the inherent uncertainty regarding whether the business processes are recreated correctly in a reverse engineering project, the announcement of release dates could be difficult. To mitigate this challenge, the product team should work toward a minimum viable product (MVP) and announce the release date only if there is enough confidence to meet the deadline. The PO will decide whether the increment will provide enough value for users and decide about the release, particularly when there are open issues. Continuous and frequent showcasing of developed artifacts to end users allow the developers to adapt the product on time. Users need to be informed about known issues during the release, and enhancements could be promised for upcoming releases. From the developer’s perspective, reverse engineering is a daunting task since build activities can be completed only once the DoD is fulfilled. Reproducing complex business processes requires continuous reworking of the program code and data model compared with a typical Agile or waterfall approach. This rework cannot be predicted well; however, the PO, BA and technical experts could help to reduce uncertainty by helping to define technical aspects as much as possible and bring the DoD of the product backlog item in a healthy state. Appropriate examples using production data covering most scenarios or processes will help developers to avoid rework. A critical success factor to rebuild the data model from the source system is the availability of production data that will be synchronized for development. The development environment needs to have all the latest production data associated with business processes. Access to the production data requires approvals from data owners in organizations and may require anonymization, depending on industries. In the banking industry, for example, the customer data shall not be used for testing, and in the pharma industry, all personally identifiable data needs to be masked. Ideally, a DevOps team needs to be formed to ensure that the production data is constantly available.
  11. 11. Cognizant 20-20 Insights 11 / Using Adaptive Scrum to Tame Process Reverse Engineering in Data Analytics Projects How Reverse Engineering Reduced Stock-Outs at a Life Sciences Organization A leading global life sciences company needed to reduce the supply chain risk of stock-outs and improve the predictability of stocks. The operational data sources provided the data required to manage the stocks using Excel sheets that were extracted manually on a regular basis from its SAP ERP system. However, the data could not provide information related to stock-outs and the need for replenishments in a major business division of the client. A data analytics dashboard application — comprising mobile and desktop versions based on QlikSense technology — was proposed to address the functional and technical debts and thus address various customer segments. The application could then follow individual orders of customers across countries starting from the order until delivery. It also highlights inventory and factories that run the risk of zero stock, which means lost revenues or delayed business depending on other products from competitors. The first step in building the analytics application was to reproduce the business processes from the source system,which proved to be highly complex.As developers tried to make sense of the data in a trial-and-error manner,many unknown business scenarios were found.No one in the organization had a full picture of the business processes as the product team did not have access to the documentation and SMEs with such knowledge were gone from the organization. The PO was supported by a team of managers from different departments who helped to define the user requirements.The BA developed the functional specifications of the application based on the initial understanding of the source system.Further,based on the analysis of Excel sheets and behavior of the source system,the rules of data model were developed. On the implementation front, developers took the initial inputs from the PO and worked together with the BA and technical architect to develop the data model of the application. Developers analyzed the Excel sheet and used the process inputs from the BA and PO to develop the initial build. The build was verified by the PO who further sought the inputs of Quick Take
  12. 12. Cognizant 20-20 Insights 12 / Using Adaptive Scrum to Tame Process Reverse Engineering in Data Analytics Projects the team of managers to provide feedback to the developers. This development process involved build, test, screen and feedback cycles that were iterated until the PO had fully verified the business processes involved. The quality of application code improved with the continuous feedback from the PO. Access to the production data in the development environment allowed developers to verify the business processes using key examples. The feedback from the PO,who further integrated inputs from other stakeholders,was critical to finalize the data model.At times,the development process has frustrated the development team as the data model was iterated multiple times.This rework was required as the PO iteratively found inconsistencies with the existing processes or data issues, even shortly before the announced release date. However,the development team was committed to fulfill the defined Sprint and product goals and the results were highly satisfying for the client. It took five months to reverse engineer the core business processes of order tracking and stock management from the source system in the data analytics applications using the Scrum approach; it would have taken roughly eight months to do this using the waterfall approach. Once the data model was developed, other value adding features like a management data dashboard and e-mail notifications for informing users about deviations of stocks or order deliveries from any given threshold were implemented in the following three months. This reverse engineering project has fulfilled the business case of saving $600,000 annually through the implementation of the data analytics application to manage stock-outs. The addition of predictive features to manage the stock situation allowed the business to take smarter decisions in terms of stock replenishment. This reverse engineering project has fulfilled the business case of saving $600,000 annually through the implementation of the data analytics application to manage stock-outs.
  13. 13. Cognizant 20-20 Insights 13 / Using Adaptive Scrum to Tame Process Reverse Engineering in Data Analytics Projects Looking Forward Today’s digital business build-out will only accelerate in the coming decades.6 During this phase, the demand for analytics applications will increase in all industries. Advanced analytics applications with innovative features could be built on top of existing business processes in legacy applications. Although we recommend Scrum as the Agile framework for analytics projects because of its flexibility and simplicity, organizations having experience with other Agile frameworks such as Crystal, eXtreme Programming (XP), Agile Unified Process, etc. could assess their suitability for initial implementation of Agile reverse engineering. For large-scale projects involving multiple teams, Nexus, the scaled version of Scrum, is recommended. Here again, other scaled frameworks such as scaled Agile framework (SAFe), large scale Scrum, disciplined Agile, etc. could be checked for organizational suitability. As a result of improved team productivity in Scrum projects, applications can have up to 50% faster time-to-market than in waterfall projects,7 depending on Agile organizational and team maturities. Based on our project experiences with leading clients to generate faster time-to-market through reverse engineering,we believe IT organizations could also save up to 50% of their development time and budget. The skills of team members play a big role in Agile execution as the team self-management is a key factor for successful execution. The availability of skilled resources with Scrum skills in the market makes Scrum the Agile framework of choice in the near future to realize business goals through reverse engineering projects.
  14. 14. Cognizant 20-20 Insights 14 / Using Adaptive Scrum to Tame Process Reverse Engineering in Data Analytics Projects About the authors Dr. Tom Philip Senior Consultant, Digital Strategy, Cognizant Dr. Tom Philip is a Senior Consultant in Cognizant Consulting’s Digital Strategy Team. He has worked in the telecommunications, life sciences and aviation industries, and his works have appeared in leading publications. Tom’s interest areas include digital transformation project management, and project failures. Tom holds master’s and PhD degrees in information systems from the University of Zurich. He can be reached at Tom.Philip@cognizant.com | https://ch.linkedin.com/in/dr-tom-philip. Andrea Colavita Data Analytics Manager, Life Sciences Practice, Cognizant Andrea Colavita is a Data Analytics Manager at Cognizant Digital Business & Technology who works in the business unit’s Life Sciences Practice. He has worked in healthcare, energy and public sector industries, and his interest areas include big data, machine learning and cloud technologies. Andrea holds a bachelor’s degree in computer science and a master’s degree in IT security. He is big data (Hadoop and Spark) and cloud (AWS) certified. Andrea can be reached at Andrea.Colavita@cognizant.com | https:// it.linkedin.com/in/andrea-colavita-27a3b21a. Endnotes 1 “Executive Pulse: Plans for Analytics Spend Continue Through Crisis as Data Use Issues Also Persist,” Oct. 1, Gartner website, 2020, https://www.gartner.com/en/documents/3991286/executive-pulse-plans-for-analytics-spend-continue- throu. 2 Teodoro Cipresso,“Software Reverse Engineering,” Handbook of Information & Communication Security, Springer, 2020. 3 15th State of Agile Report, https://explore.digital.ai/state-of-agile/15th-state-of-agile-report. 4 Ken Schwaber and Jeff Sutherland (2020),“The Scrum Guide,” www.scrum.org. 5 “Agile Practice Guide”, PMI, 2017. 6 Malcom Frank, Paul Roehring and Ben Pring,“What to Do when Machines Do Everything,” Wiley, 2017. 7 Delta Matrix,“Why is Agile Time to Market (TTM) Delivery 50% Faster?” https://www.deltamatrix.com/why-is-agile-time-to- market-ttm-felivery-50-faster/.
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