Continue # Infosys POV
 PROCESS OWNERS CHANGE MANAGEMENT  EXPERTS LEADERSHIP SUPPORT People Process Compliance with legal and  regulatory requirements, such as  data privacy laws.  PROCESSES Well-defined processes with  documented workflows and  procedures.  Availability of the required  hardware or software  infrastructure to support Process  Mining activities Access to event logs or other  data sources that capture  process data.  Alignment with the organization’s  strategic objectives and goals.  DATA CAPTURE  TECH INFRASTRUCTURE STRATEGIC OBJECTIVES COMPLIANCE AGILITY Organization’s ability and culture  to adopt new frameworks for  continuous improvement.

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 7 Oil & Gas transformation | © 2023 Infosys Consulting System Visualization software to create  dashboards and reports.  ACCESS TO DATA Access to digitized processes  and/or processes with  event/case data and relevant  data sources. This includes,  event logs, databases, and  other data repositories.  Data cleaning, transformation,  and normalization tools to  prepare data for analysis.  Process Mining software to  extract and analyze process  data.  Process modelling software to  create process model.  PROCESS DATA DATA TOOLS PROCESS MODEL VISUALIZATION INVESTMENT Continued investment in  technology platforms and  relevant features.

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 8 Case studies The following case studies cite instances where Process Mining helped a US-based Oil  and Gas major realize efficiencies and optimize resources using Celonis.<|endoftext|>Oil & Gas transformation | © 2023 Infosys Consulting Approach • Key AS-IS process flows for these processes were modeled in ARIS to begin with.  This provided an understanding of the current pain points and areas of improvement.  • This process model was leveraged to identify the data availability in applications  across each of the steps.<|endoftext|>• The journey: A case was created through all its states and the data captured from the  previous step was mapped against this to ensure data consistency.<|endoftext|>• This data was imported into the Celonis Execution Management system to create a  data model.  • Based on this data model, multiple process analysis dashboards and components  were created to track various metrics and KPIs across key dimensions such as time,  vendors, locations.  Process Mining in upstream logistics

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 9 Oil & Gas transformation | © 2023 Infosys Consulting Business/process  area Common  challenges Potential process  mining gains Value levers  impacted Standard enterprise  processes (order-to- cash, procure-to-pay) Manual interventions Reduction of TAT Operational efficiency Form corrections Improve no-touch  processing Regulatory  compliance Rate changes, data  mismatches Automation Customer satisfaction Supply chain  management High complexity of  supply chain Reduction of process  lead time Operational efficiency Visibility is limited  among all  stakeholders  Reduction of cost by  removing bottlenecks Optimized  maintenance Best practices are not  well-defined  Full transparency of  process Customer satisfaction Vessel schedule  optimization Multiple rigs covered  by same vessel Effective route planning  to reduce fuel costs and  optimize time Operational efficiency Route planning done  at the last minute Operational safety Optimized  maintenance  Helicopter schedule  optimization High cost due to over  utilization  Incorporate best  practices for utilization  Operational efficiency Unnoticed  maintenance risks  Monitoring risks Regulatory  compliance Operational safety Optimized  maintenance  Warehouse  management Warehouse layout  inefficient  Root cause analysis for  layout  Operational efficiency Lack of process  automation  Forecasting data for  inventory utilization and  avoiding stock outs Customer satisfaction Warehouse inventory  inaccuracy Enhanced customer  management Warehouse utilization  inaccuracy  Fleet management High fuel cost Improved fleet efficiency  and routing  Operational efficiency Under-utilized assets  KPI monitoring to  improve utilization Customer satisfaction Vendor management Manual processes,  poor automation  SLA improvement Operational efficiency Rental costs high and  equipment under- utilized  Contract visibility and  optimization Customer satisfaction End-to-end system  integration not  available

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 10 Oil & Gas transformation | © 2023 Infosys Consulting Reference industry use cases Large integrated Oil & Gas major One of the largest Oil and Gas companies in the world has been using Process Mining  to improve the efficiency of its drilling operations.  By analyzing data from drilling rigs, this company was able to identify inefficiencies and  areas for improvement, such as reducing idle time and optimizing drilling parameters. As  a result, the firm was able to reduce drilling time and costs while improving safety and  environmental performance.<|endoftext|>A European Oil & Gas company This company used Process Mining to optimize its maintenance processes for offshore  platforms.  By analyzing maintenance data, the firm was able to identify patterns and trends which  improved the reliability of its equipment, reduced downtime, and lowered maintenance  costs. The company also used Process Mining to identify opportunities for process  standardization and optimization, resulting in further improvements in efficiency and cost  savings.<|endoftext|>A large National Oil Corporation (NOC) This NOC used Process Mining to improve its customer service processes.  By analyzing customer service data, the NOC was able to identify areas where it could  improve its service levels, such as reducing response times and increasing the accuracy  of billing. The company also used Process Mining to optimize its meter reading  processes, resulting in significant cost savings.<|endoftext|>

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 Process Mining  encourages  sustainable growth Oil and Gas companies operate in a  complex environment with multiple  interconnected processes, making it  challenging to identify inefficiencies  and areas for improvement. Process  Mining provides a valuable tool for  these companies to gain insights into  their operational processes by  analyzing data from various sources.  By applying Process Mining  techniques, Oil and Gas companies  can identify bottlenecks, reduce  costs, improve efficiency, and  enhance the quality of their  products and services.  The benefits of Process Mining  include improved compliance,  enhanced decision-making, and  increased operational efficiency.  Therefore, implementing this  technology can help Oil and Gas  companies stay competitive and  achieve sustainable growth in an  ever-changing industry.<|endoftext|>11 Oil & Gas transformation | © 2023 Infosys Consulting

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 MEET THE EXPERTS SACHIN PADHYE Associate Partner, SURE Sachin.Padhye@infosys.com 12 Sachin works with large Oil and Gas companies in the upstream, midstream, and  downstream areas to frame their digital strategy across customer and employee  experiences. He helps clients quantify value, beginning with industry opportunities  and ending with decisions built with big data, analytical tools and visualizations and  narratives. His current focus is digital data monetization, where he helps companies  put a monetary value to the data that is used to execute their digital strategy.  NAVEEN KAMAKOTI Principal, SURE Venkata_Kamakoti@infosys.com Naveen has over 18 years’ experience in digital business transformation initiatives,  focusing on process consulting, re-engineering and mining, as well as business  architecture and consulting across information and professional services, plus Oil &  Gas (upstream) domains. He leads the process consulting and transformation  community of practice for Infosys Consulting.<|endoftext|>SHRUTI JAYARAMAN Senior Consultant, SURE Shruti.Jayaraman@infosys.com Shruti has four years’ experience in business process improvement and digital  transformation initiatives with a focus on process modelling, analysis, and mining.  She’s worked with upstream Oil & Gas clientele, across financial planning, process  design and optimization, third-party hiring and government reporting areas for the  last two years. She has administered trainings in process modelling using ARIS and  has worked in Agile methodologies.  SOHINI DE Consultant, SURE Sohini.De@infosys.com Sohini has over four years’ experience in process transformation initiatives focusing  on business process improvement, process design, modeling and mining. She has  two years’ experience in the upstream energy industry in marine logistics, and  integrity inspection. She has conducted trainings in ARIS Designer platform for  process modeling and has hands-on experience working in Agile methodologies.  Oil & Gas transformation | © 2023 Infosys Consulting

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 consulting@Infosys.com InfosysConsultingInsights.com LinkedIn: /company/infosysconsulting Twitter: @infosysconsltng About Infosys Consulting  Infosys Consulting is a global management consulting firm helping some of the world’s most recognizable brands  transform and innovate. Our consultants are industry experts that lead complex change agendas driven by disruptive  technology. With offices in 20 countries and backed by the power of the global Infosys brand, our teams help the C- suite navigate today’s digital landscape to win market share and create shareholder value for lasting competitive  advantage. To see our ideas in action, or to join a new type of consulting


Continue # Infosys POV
 firm, visit us at  www.InfosysConsultingInsights.com.  For more information, contact consulting@infosys.com  © 2022 Infosys Limited, Bengaluru, India. All Rights Reserved. Infosys believes the information in this document is  accurate as of its publication date; such information is subject to change without notice. Infosys acknowledges the  proprietary rights of other companies to the trademarks, product names, and other such intellectual property rights  mentioned in this document. Except as expressly permitted, neither this document nor any part of it may be  reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, printed,  photocopied, recorded or otherwise, without the prior permission of Infosys Limited and/or any named intellectual  property rights holders under this document.

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# Infosys Whitepaper
Title: Trends in Performance Testing and Engineering – Perform or Perish
Author: Infosys Limited
Format: PDF 1.7

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 WHITE PAPER TRENDS IN PERFORMANCE TESTING  AND ENGINEERING – PERFORM OR  PERISH Hemalatha Murugesan

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 One is spoilt for choices in today’s high  consumerism and materialistic world  leaving the end users both highly  excited, vulnerable as well as extremely  demanding.  The array and diversity of  choices is not just limited to technology,  gadgets, smartphones, wearable devices,  sports vehicles, FMCG goods, white goods,  tourism, food choices etc. but is extensible,  penetrating in every single aspect of  one’s day to day life. In today’s world, no  business can survive if one’s product/ service/training or any item is taken to  the market without being online – aka  Digitization, Mobilization.  Stepping two  decades back, one wonders how business  was being done and reached various parts  of the globe! With intense competition and aggression  to extend the market footprint, every  organization is launching multiple  products or services catering to different  user groups, age sectors, geo’s based  launches, customization, personalization  or rather “mood based” coupled with  analytics, user preferences, predictions, etc.   Businesses and IT organizations are moving  at rapid pace to roll out their launches  using the latest cutting edge technology  and migration to newer technologies with  the sole objective to ensure that they do  not only retain their existing customers  but also add to their base and be market- dominant leader.<|endoftext|>As such, every application that is catering  to diverse populations 24x7, 365 days a  year must be Available, Scalable for future  growth, Predictable and Reliable, lest the  end user lose their tolerance.  What was  earlier 8 seconds being the norm for a  page to be rendered has now reduced to  less than 2 seconds or milliseconds and  almost all launches going the “app” way,   the response time expected is to be in  milliseconds.<|endoftext|>Devops, Agile, SMAC, migration to cloud,  VPN, Big Data, MongoDB, Cassandra,  - phew – the list is endless with newer  technology, tools being launched by  the day to address to ever expanding  technology landscape.  The rush to absorb  these technologies is also increasing  leading to high vulnerability on the  application’s performance.  There is a  significant change in the way Performance  Testing and Engineering including  monitoring is being performed which is  continuously evolving and becoming more  complex.<|endoftext|>With increased digitization and  mobilization being the norm, data  analytics and testing to scale would play  a major role in application performance  to ensure better customer experience is  provided. DevOps and Agile development  will “shift left” forcing Performance Testing  and Engineering teams to make early  assumptions on customer behaviors and  their needs, viable experiences and growth  spikes to run tests quickly and validate  those assumptions. Early predictability  leveraging analytics on the application  performance will change gears in the way  we do or approach performance testing  and engineering.<|endoftext|>External Document © 2018 Infosys Limited

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 Performance Driven Development (PDD)  i.e. PE focus right from requirements to  production rollout to post production,  monitoring are one of the key trends  noticed as it helps in early bottleneck  identifications and tuning of the same.  With Devops adoption having a close  handshake between Development  and Operations teams to address real  production volumetric, PDD helps achieve  it to a large extent.  This in turn demands  adoption of APM tools and methodologies.<|endoftext|>The focus has now shifted for early  involvement of Performance Engineering  in the application lifecycle – Early  Validation of PE proactively and not  leaving it to be addressed prior to roll out  which was a reactive approach followed  earlier.  Due to this, the industry is seeing  increasing launches of tools/processes  supporting proactive PE approach.<|endoftext|>Businesses are demanding launches  at faster pace with high Availability  and Resiliency, yet no compromise  on quality and security.  All this at less  TCO!   Automation is the key across all  SDLC and widely prevalent in testing  types.  As such every activity be in NFR  gathering phases, scripting, modeling, test  environment setup, releases, configuration  management, etc. is getting automated  which is inclusive of performance testing  and engineering activities through these  phases as well. Performance Engineering  framework adapting to the agile/CI CD  methodology for Web-based, Thick Client,  batch job-based apps etc. needs to be  developed, suited to the ever-changing  technology landscape.<|endoftext|>Financial institutions have been one of  the front runners in IT adoption where  they would need to meet the regulatory  compliances inclusive of performance.    With multiple banking packages/products  to choose from trading platforms and  investment management products  like Appian Way, middle and back- office products like Alteryx, Analytical  Workbenches, etc., clients are looking for  standard benchmarks/baselines of these  products and its impact on PE before  rolling out full-blown implementation. With  almost all the apps on Mobile channels  to interact with their systems, there is an  intense need to do PE at every stage, at  every component and layer and across all  stacks.  Few impacting trends seen are: Omni-channel retail customer experience  – Performance testing and engineering to  ensure consistent user experience across  various touch points for application rewrite  or new application development projects.<|endoftext|>Technology and infrastructure  rationalization – Mostly driven by cost  optimization and compliance requirements  PT&E  done to ensure zero disruption in  service and user experience in data center  migration/consolidation, technology stack  upgrade or movement from on-premise to  cloud.<|endoftext|>External Document © 2018 Infosys Limited

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 Conclusion For any organization to survive in today’s  competitive world, it is important that  the products/applications are Scalable,  Predictable, and Available exciting the  user, thereby ensuring loyalty as well  as converting to business.   However  rich the application features are, when  functionally tested, if the application is  not responding to the expectations of the  user, it is but natural to lose the customer.   With changing and demanding trends, it  is important that Performance testing and  Engineering are considered at all layers and  components as well for successive use of  the products launched.<|endoftext|>Hemalatha Murugesan is currently heading  the Performance Testing and Engineering  in IVS at Infosys.  She has been involved  in setting up, pioneering, incubation and  evolving emerging testing services like  cloud testing, test data management,  Infrastructure testing, virtualization, TEMS  and other upcoming specialized services,  etc., at Infosys. Hemalatha has been  instrumental in developing the Enterprise  Performance Testing Solutions which  offers performance testing solutions &  services and has also setup the state-of-art  performance testing lab, both at Infosys.<|endoftext|>Bulk customer data handling-Retail and  institutional customers are given more  control to deal with their data. As a result of  which interfaces such as dashboard, search,  profiles, and homepages are becoming  more interactive and data-heavy. PT&E is  ensuring the performance SLAs are within  acceptable limits in all user interactions.<|endoftext|>Tackle Integration Challenges – PT&E has  been carried out to deal with scalability  and performance issues arising due to  enterprise, partner integration, middleware  upgrades, etc.<|endoftext|>With intense pressure to reduce cost,  banks are looking at embracing Clouds,  DC consolidation and solutions around  it.  Consolidation of their LOB’s, tools by  encouraging COE/NFT factory is setup  to reduce cost.  Banks are also moving to  deploying software’s like SAP, PEGA, and  Siebel etc. due to their low maintenance  cost and better predictable quality  compared to home-grown solutions.    Besides, PE for apps hosted in Cloud and  Virtualized environments is also picking  up due to the on-demand resource  provisioning and sharable hardware  infrastructure that minimizes TCO.   Performance simulation and Engineering  of Day in the Life, for e.g., in a line of  business through end-to-end PT of  disparate systems analyzed.  An example  work-flow of a Mortgage loan, Mutual  Fund, Credit rating process etc. is assessed  for performance simulation.<|endoftext|>While the request has always been to have  the exact production environment with  right volume for Performance testing but  the move is to Test Right with the Next  Best which has predictable performance  on the production environment replayed.  Hardware Capacity planning and seamless  integration to PT/PE framework especially


Continue # Infosys Whitepaper
  for new Automated Infrastructure  spawning through CHEF/RECIPE,  automated platform build-outs and other  large Infragistics-related programs owing  to Merger & Acquisition, Data Center  migration etc.<|endoftext|>DB virtualization for PT&E also seems to  be another emerging trend, though not  implemented on a large scale today as  compared to service virtualization. Service  virtualization and Agile or component  PT or layered performance testing and  engineering also are gaining prevalence  as there will be so many components  and interfaces in financial products and  Production Monitoring, Capacity Prediction  Modeling based on that.  Another trend we are seeing in Retail  space is that applications built using  Microservice, Docker Container etc. which  requires tweaking monitoring and analysis  approach. An interesting emerging trend  is Hybrid datacenter approach i.e. part of  the system is hosted in Cloud and while  part of it is hosted in a Permanent data  center.  This would require expanding the  list of performance KPI to cover all aspects  of both the DCs.In some cases, we are also  seeing hybrid datacenter approach e.g.  part of system in Cloud and part of it in on  permanent data center. Again we need to  expand list of performance KPI to cover all  aspects of two DCs.<|endoftext|>An upcoming hot trend seen is front-end  Performance testing and engineering due  to RIA/Web 2.0 popularity and to provide  same personalized user experience across  various media.<|endoftext|>© 2018 Infosys Limited, Bengaluru, India. All Rights Reserved. Infosys believes the information in this document is accurate as of its publication date; such information is subject to change without notice. Infosys  acknowledges the proprietary rights of other companies to the trademarks, product names and such other intellectual property rights mentioned in this document. Except as expressly permitted, neither this  documentation nor any part of it may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, printing, photocopying, recording or otherwise, without the  prior permission of Infosys Limited and/ or any named intellectual property rights holders under this document.  For more information, contact askus@infosys.com Infosys.com | NYSE: INFY Stay Connected

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# Infosys Whitepaper
Title: Performance testing Internet of Things (IoT)
Author: Infosys Limited
Format: PDF 1.7

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 VIEW POINT PERFORMANCE TESTING INTERNET  OF THINGS (IOT) - Yakub Reddy Gurijala     Senior Technology Architect

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 External Document © 2018 Infosys Limited Internet of things (IoT) is a network of  systems, devices, and sensors which are  connected and this connectivity enables  these objects to share the data. It is a  platform which allows to manage the data  and controls the devices remotely based on  the requirement.<|endoftext|>The IoT has gained momentum in the recent  years due to Internet availability, cloud  evaluation, and micro services evaluation.  According to Gartner, IoT connected devices  are growing at 30 percent year-on-year and  there will be 20 billion devices connected  by 2020 (more than human population).  IoT business is growing at 22 percent year- on-year and will reach US$3010 billion.  People, government, and business will be  hugely affected by IoT in the coming years  resulting in smart cities, smart homes, smart  hospitals, and so on.  IoT devices produce data continuously. This  data needs to be saved and analyzed for  future decisions and these decisions may be  immediate or may be taken later by using  business intelligence (BI) analytics. IoT helps  in improving the operational performance  and cost optimization. So achieve this, IoT  systems must be built for high performance  and scalalability. To measure these two  key attributes of an IoT application, it is  important to understand the business value  for which it is built. In addition, to measure  performance, it is necessary to simulate  real-world workload models, which can  be created using business requirements,  historic data and future growth require- ments, type of devices, network conditions,  usage patterns, and geographic spread.  Application usage patterns are arrived  by analyzing the IoT application logs for  peak hours and normal hours. Using these  data points, different workload conditions  (real-world load test / simulation) can be  created for peak usage, normal usage, future  growth, and daylong / multiday simulations.<|endoftext|>IoT performance testing (PT) is little different  from traditional performance testing.  Following table illustrates differences  between traditional PT vs IoT PT.<|endoftext|>Because of these differences, IoT PT poses a lot of challenges to performance engineers. Below sections will describe different challenges  posed by IoT applications and Infosys solution elements for each of the challenges.<|endoftext|>Some of key differences between traditional PT and IoT PT: Key diferences Simulation Simulation of users Simulation of devices / sensors Scale Few hundred users to few thousand users Few thousand devices to few million devices Amount of data Sends and receives large amount of data per request Sends and receives minimal data per request but data is shared continuously with time interval Protocols Uses standard protocols to communicate Uses non-standard and new protocols to communicate Requests / responses In most of the cases, users create the requests and receive the response Generally IoT devices create the requests and receive response as well as request, and provide response BI Only few applications have BI as part of testing BI will be a part of IoT; needs to measure performance by applying loads on IoT app Traditional PT IoT PT

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 External Document © 2018 Infosys Limited Performance testing  challenges Protocols and performance testing tool  IoT does not have standard protocol set  to establish the connectivity between IoT  application and devices. IoT protocols used  range from HTTP, AllJoyn, IoTivity, MQTT,  CoAP, AMQP, and more. These protocols  are still in the early phases of development  and different IoT solution vendors come  up with specific protocol standards (sets).  These protocols are continuously evolving  with IoT applications. Since these are new  technologies / protocols, and current  performance testing tools may or may not  support them.  Geographical spread and network  conditions IoT devices / sensors are spread across  the world and use different networks to  connect to the IoT servers to send and  receive the data. As part of performance  testing, there is need to simulate devices  from different locations (to simulate  latency) with required network technolo- gies like 2G, 3G, 4G, Bluetooth, etc.<|endoftext|>Load conditions It is necessary to load test the applications  by simulating real-world conditions. These  patterns are complex in nature and it will  be extremely difficult to collect and predict  the data. To recreate real-world load  conditions, we may land up simulating  millions of devices.<|endoftext|>Real-time decision making Some IoT implementations may require  the data from a device that needs to be  processed at runtime and based on the  data received, the corresponding decision  is taken. These decisions are generally no- tifications / requests to different devices /  sensors or different systems which perform  particular action. As part of testing,  these notifications / requests need to be  monitored for performance (time taken to  generate the notification / request from  the data received by IoT application).  IoT application monitoring and BI  processing Monitoring is essential for any application.  It helps understand the system behavior  under real-world conditions. For IoT ap- plications, both the application and the  backend BI systems need to be monitored.  This will help understand data processing,  both in terms of the volume and accuracy.  Infosys IoT PT solution Infosys created a comprehensive  framework using JMeter to support all the  needs of IoT PT.  Protocols and performance testing tool  Infosys selected JMeter as performance  test tool to conduct PT. JMeter already  has support to most of the IoT protocols  like HTTP, CoAP, AMQP, MQTT and Kafka.  As IoT is an emerging area, new protocols  are being developed over the time. To  on-board new protocols Infosys has come  up with a protocol framework using  protocol SDK and extending the JMeter.  Using these JMeter extensions, scripts  can be prepared to simulate new protocol  requests and devices.<|endoftext|>Geographical spread and network  conditions To simulate geographical spread, JMeter  is integrated with cloud solutions like  Amazon web services (AWS) to setup  the load generators across different  geographies. Using AWS integration,  JMeter is able to generate the traffic from  different locations of the world to IoT  application to mimic the geographical  spread and network latency.  Infosys has in-house IP-based solution,  Infosys Network Simulation tool (iNITS),  to simulate different network conditions  required for any requests which use  transmission control protocol (TCP). We  have integrated iNITS solution with JMeter  to simulate different network conditions  required by IoT PT.<|endoftext|>Load conditions To collect the accurate real-world scenarios,  Infosys developed different tools /  frameworks like non-functional require- ments (NFR) questionnaire, workload  modeling tools, and others. These tools  / frameworks reduce the requirement  gathering and collect the information more  accurately.  To simulate millions of devices, JMeter  integrated with cloud using automated  scripts. These scripts will create required  number of load generators in cloud, setup  the JMeter, copy the scripts, test data,  execute the results, collect the results,  shutdown the LG’s which are created, and  process the results.<|endoftext|>Real-time decision making Notifications, which are sent to other  devices / sensors / systems, need to be  monitored using stubs / service virtualiza- tion technologies. IoT application logs  are collected and analyzed for processing  time and response time of the real-time  processing and decision making scenarios  under different load conditions.<|endoftext|>IoT application monitoring and BI  processing Infosys created predefined process /  performance metrics collection to monitor  the systems (Web / app / database layers)  deployed in cloud and data center. These  metrics are analyzed to uncover possible  performance bottlenecks. If BI systems  were built using batch jobs, then enough  test data needs to be created using  performance test scripts and the batch  jobs executed to monitor the BI system.  If real-time BI systems were implemented  using hot channels then, BI systems need  to be monitored as a part of different  performance tests by generating different  amount of data per second / minute /  hour. Using this approach, IoT applica- tions are comprehensively monitored and  performance results are benchmarked  against different load conditions.<|endoftext|>IoT PT resources Infosys presently has 1200+ performance  testing resources having experience in  testing different types of applications,  technologies, and tools. And more than  500 employees have working experience  on JMeter.  Infosys has dedicated resources who  are trained on IoT performance test  frameworks (JMeter, new protocols,  network simulation, and IoT monitoring).  These resources continuously explore the


Continue # Infosys Whitepaper
 opportunities to improve the framework,  tool, and protocols supported.<|endoftext|>

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 © 2018 Infosys Limited, Bengaluru, India. All Rights Reserved. Infosys believes the information in this document is accurate as of its publication date; such information is subject to change without notice. Infosys  acknowledges the proprietary rights of other companies to the trademarks, product names and such other intellectual property rights mentioned in this document. Except as expressly permitted, neither this  documentation nor any part of it may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, printing, photocopying, recording or otherwise, without the  prior permission of Infosys Limited and/ or any named intellectual property rights holders under this document.  For more information, contact askus@infosys.com Infosys.com | NYSE: INFY Stay Connected Supports diferent network simulations for all  type of Protocols.<|endoftext|>Framework is available to onboard new  protocols.<|endoftext|>As solution is based on open source tool, no  License cost for performance test tool and cost  for network simulation only.<|endoftext|>No need to have hardware as device simulation  can be done from Cloud.<|endoftext|>Features Benefts Support for diferent communication protocols  such as HTTP, REST over HTTP, MQTT, AMQP,  CoAP,  Kafka and Web Sockets.<|endoftext|>Supports cloud based load generation.  Automated scripts available to generate the  load from cloud.<|endoftext|>Faster time to market.<|endoftext|>Quick onboarding of new protocols.<|endoftext|>01 02 04 03 Infosys IoT PT – Key features and benefits Conclusion Infosys created a compressive solution for IoT performance  testing, which covers specific needs / demands of IoT.  Currently, solution supports all leading IoT protocols and  network simulations. Infosys IoT performance solution  is very cost-effective when compared to any standard  performance test tool.<|endoftext|>We have dedicated workforce trained on IoT performance  testing to support the growing demands of IoT PT. Using  Infosys IoT PT solution, clients can save 80 to 90 percent tool  cost and reduce go-to-market time by 20 percent.<|endoftext|>References http://www.gartner.com/newsroom/id/3165317 https://www.infosys.com/IT-services/validation-solutions/white-papers/Documents/successful-network-impact-testing.pdf

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# Infosys Whitepaper
Title: QA Strategy to Succeed in the Digital Age
Author: Infosys Limited
Format: PDF 1.7

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 VIEW POINT QA STRATEGY TO SUCCEED IN THE  DIGITAL AGE

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 “Digital” is the new buzzword for  organizations.  Across industries,  organizations are at various stages of their  digital transformation journeys. For some,  this might mean reimagining their entire  businesses around digital technologies;  and for others, incorporating aspects of  digital into their existing ways of working.<|endoftext|>Examples of this are abundant across  industries. In retail, the margin between  online and offline is blurring. Retailers  are incorporating technologies such as  augmented reality, beacons to provide  interactive in-store experiences to users.  Targeted campaigns are being delivered  to customers based on their proximity  to a store. On one hand, customers have  the option to order online and pick up  in-store; and on the other, they can pay  in-store and have it delivered at home.  In  the insurance industry, companies are  enabling customers to purchase insurance  anytime, anywhere, using pre-populated  information from their Facebook profiles.<|endoftext|>What this means is that IT is now at the  forefront of business transformation.  Technology is the driver for business  improvements and hence, IT departments  have a greater role to play in business  success. Consequently, the stakes are  higher than ever, for IT to deliver better  value, faster and more efficiently.<|endoftext|>However, the path to a successful digital  transformation is wrought with multiple  challenges, of which, a key challenge is  one that is inherent to the most important  aspect of digital transformations – the  changing nature of customer interactions.  While the digital revolution brings to  organizations, newer models and channels  of interaction with customers, the success  of businesses is also becoming increasingly  dependent on the quality of these  interactions. The end customer experience  is now the single most important factor in  a business’ success. Customers today are  more demanding, and much more likely to  switch loyalties if the customer experience  is not up to their expectations. Thus, the  most important factor for success of digital  transformations is ensuring a superlative  end customer experience through the  quality assurance function.<|endoftext|>However, can traditional testing  organizations that follow age-old ways,  be able to provide quality assurance in  the new scheme of things? To answer this,  let us look at some of the imperatives of  digital assurance.<|endoftext|>Focus on Customer  Experience As discussed already, the nature of  customer interactions has undergone a  great transformation in the recent past.  Businesses are increasingly engaging with  customers through a multitude of channels  such as web, mobile, and social media,  in addition to the existing traditional  channels. A single customer transaction  can now span across multiple online  and offline channels. Hence, customer  experience across each of these channels is  important; but so is providing similar and  seamless experiences across all channels,  as well as maintaining consistency in  messaging all throughout.  This also  requires a change in the approach to  quality assurance. QA needs to shift focus  from the traditional functional validation,  to more of customer experience validation,  across the digital landscape. This requires  a 360° view of quality, encompassing  functional and non-functional aspects, and  cutting across channels and technologies.  The anytime-anywhere nature of customer  transactions pose challenges in all aspects  of testing. With the increase in online  transactions, usage of cloud infrastructure,  the multitude of interconnected  applications and devices, and the advent  of big data analytics, there are newer  challenges to application security and  data privacy. Ensuring the security of  applications from any breaches, along with  adherence to security and data privacy  guidelines is essential for ensuring a  good customer experience, and business  continuity. Comprehensive security  assurance is thus a key component of  digital assurance.  Application performance is another key  determinant of success. Users are much  more likely to uninstall an app or abandon  an online transaction with the slightest  of reductions in application performance.  Unlike traditional QA, performance  evaluation needs to be incorporated at  all stages of the application development  lifecycle.  Performance evaluation needs  to be augmented with performance  monitoring in production to ensure  availability of business critical applications.  Strategies for compatibility, usability,  and accessibility testing should also be  optimized to cover multiple customer  touch points and technologies like  desktop, mobile, and other connected  devices.  There is also an increasing focus on  providing personalized experiences to  customers. In addition to functional  validation, personalized content validation  across channels, and validation of digital  content and assets also needs to be  incorporated.  Another aspect of the digital world is the  constant customer feedback and inputs,  which have become important drivers  for business decisions. Companies are  co-creating products with customers, or  using customer inputs to improve existing  products. This is also now extending  to using customer inputs to improve IT  platforms and services. In this constantly  evolving landscape, a continuous feedback  mechanism is also important for QA  organizations to understand the end  customer requirements and preempt  customer issues.  End customer feedback,  learnings from production,  and findings  from previous testing cycles can all serve  as inputs to continuously improve testing  effectiveness and efficiency, and provide a  truly 360° view of application quality.  External Document © 2018 Infosys Limited

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 Manage Complexity An important challenge that digital  transformation brings about it is the  increasing complexity of the application  landscape. The IT landscape now needs  to support multiple newer applications  built on disparate technologies. The  interconnectedness of applications, as  well as the requirement to test them on  different device configurations, pose  {{ img-description : a group of people sitting around a table working with a laptop, in the style of light teal and crimson, uniformly staged images, light gray and navy, iso 200, yankeecore, focus on joints/connections, gravure printing  }} Increase Agility With the digital revolution, newer  technologies are being adopted at a much  faster pace. Organizations are now trying  to pilot newer and sometimes unproven  technologies, in a bid to enhance their  business. For QA teams to support this  effectively, they have to be extremely  nimble and quick to learn. Teams should  be tuned-in on technological changes,  be able to innovate quickly, and come up  with optimal solutions for new testing  challenges.  In general, development cycles are getting  progressively shorter, with businesses  vying to provide better features, faster.  Development methodologies are moving  to Agile, and DevOps. Consequently, there  is an increasing pressure on QA teams to  reduce the turnaround time and deliver  the code to production. It also has to be  balanced with the requirement to support  more and more devices and platforms.  This needs a two-pronged approach to  optimize testing requirements, as well as  increase the speed of testing. With limited  time to test, it is crucial to adopt methods  to optimize testing requirements, so that  the time is well spent on validating critical  functionalities.  While automation has been the key enabler  to increase testing effectiveness, it should  not be limited to test execution alone. It  should also encompass the entire testing  lifecycle – from requirements analysis to  reporting. Efficiencies need to be built  into the testing process by a combination  of tools, accelerators, and reusable test  artifacts. Early automation strategies  can be deployed to ensure availability of  automated test scripts for system testing.  To conclude, an assurance strategy in the  digital world has to address the following: • Focus on customer experience, rather  than functional validation • Provide a 360° assurance, encompassing  different aspects of testing as well as  end-to-end validation • Focus on continuous learning and  innovation • Continuously optimize and accelerate  testing additional challenges for the QA teams.  On one hand, assurance needs to be  provided for all application layers, from the  database, to the UI, to isolate issues; and  on the other hand, end-to-end business  process assurance encompassing multiple  applications is equally crucial. The testing  strategy should be able to balance these  requirements and provide optimal test  coverage, ensuring early isolation of  issues.  A well planned approach involving  service virtualization, judicious mix of  automation tools, test data management  and optimized testing scope should be  implemented.<|endoftext|>Thus, the need of the hour is a holistic  assurance strategy encompassing all  aspects of validation, which is also 	 optimized for the changing application  landscape.<|endoftext|>External Document © 2018 Infosys Limited

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 © 2018 Infosys Limited, Bengaluru, India. All Rights Reserved. Infosys believes the information in this document is accurate as of its publication date; such information is subject to change without notice. Infos


Continue # Infosys Whitepaper
ys  acknowledges the proprietary rights of other companies to the trademarks, product names and such other intellectual property rights mentioned in this document. Except as expressly permitted, neither this  documentation nor any part of it may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, printing, photocopying, recording or otherwise, without the  prior permission of Infosys Limited and/ or any named intellectual property rights holders under this document.  For more information, contact askus@infosys.com Infosys.com | NYSE: INFY Stay Connected

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# Infosys Whitepaper
Title: Quantifying Customer Experience for Quality Assurance in the Digital Era
Author: Infosys Limited
Format: PDF 1.7

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 WHITE PAPER QUANTIFYING CUSTOMER  EXPERIENCE FOR QUALITY  ASSURANCE IN THE DIGITAL ERA Abstract Post the pandemic, the new normal situation demands an increased  digitalization across all industry sectors. Ensuring top class customer  experience became crucial for all digital customer interactions  through multiple channels like web, mobile, chatbot, etc. Customer  experience is an area in which neither the aesthetics nor the content  can be compromised as that will lead to severe negative business  impact. This paper explains various automation strategies that  can enable QA teams to provide a unified experience to the end  customers across multiple channels.  The focus is to identify the key  attributes of customer experience and suggest metrics that can be  used to measure its effectiveness.<|endoftext|>{{ img-description : rateyourexperience with a palm and pen with four stars, in the style of interactive, smartphone footage, digital  }}

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 External Document © 2022 Infosys Limited Introduction Customer experience has always been a dynamic topic as it is becoming more personalized day by day and varies according to individual  preferences. It is hard to measure customer experience which make the work even more difficult for Quality Assurance teams. The factors  which amplify the customer experience not only include the functional and visual factors like front end aesthetics, user interface, user  experience, etc., but also include non-functional and social aspects like omnichannel engagements, social media presence, customer  sentiments, accessibility, security, performance, etc.<|endoftext|>Enterprises encounter various challenges  in providing a unified experience to their  end customers across multiple channels  such as: • Lack of information or mismatch in  information • Quality of content is not up to the  standard • Lack of usability in cross navigation  to make it intuitive and self-guided • Consistent look and feel and  functional flow across various  channels • Improper content placement • Inappropriate format and alignment • Performance issues across local and  global regions • Violation of security guidelines • Nonconformance to Accessibility as per  the Web Content Accessibility  Guidelines  (WCAG) guidelines • Lack of social media integration Why do we need to measure the Customer Experience? Quality Assurance is required in all these areas of functional, nonfunctional, and social aspects of Customer Experience. Since, Customer  Experience is hyper personalized in the digital era, a persona-based experience measurement is required. Conventional Quality Assurance  practices need to be changed to evaluate all aspects of customers journey across multiple channels, comprehensively.<|endoftext|>Figure 1 Challenges in Quality Assurance of Customer Experience Lack of  single view of  factors afecting customer  experience.<|endoftext|>Traditional Testing fails to  adapt to real time learning,  lacks feedback loop Lack of persona based test strategy Quantifable CX  measurements not  available Adapting experience  unique to each customer Testing is inward focused  rather than customer  focused Testing based on biz/  technical requirements  resulting in gaps in  customer’s expectations Vast sea of social  messages  and user  feedback data from  social media platforms

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 External Document © 2022 Infosys Limited Experience Validation Needs to Cover Multiple Areas of a Customer Journey  While organizations try to focus on enhancing the customer experience, there are various areas need to be validated and remediated  independently for functional, nonfunctional, and social aspects. The current testing trend covers the basic functional and statistical aspects,  emerging testing areas will cover behavioral aspects and focus more on providing customer centric approach like using AI for enhancing  the quality of digital impression with personalized customizations. Below table provides information on areas where quality assurance is  required along with the popular tools for automation.<|endoftext|>Sr No Area Key Aspects / Metrics Current Testing Trend Emerging Testing Trend Tools 1 Visual  Conformance  Webpage content alignment,  font size, font color, web links,  images, audio files, video  files, forms, tabular content,  color scheme, font scheme,  navigation buttons, theme  etc.<|endoftext|>A/B testing, Style guide  check, Font check, Color  check, Usability testing,  Readability testing Persona based testing Siteimprove  Applitools, SortSite 2 Content Checking whether the image,  video, audio, text, tables,  forms, links etc. are up to the  standards.<|endoftext|>A/B Testing, Voice quality  testing, Streaming media  testing, Compatibility  testing, Internationalization/  Localization testing Personalized UX Testing,  CSS3 Animation testing,  2D Illustrations, AI  powered translators Siteimprove, SortSite 3 Performance  of webpage Loading speed, Timeto
Title,  DNS lookup speed, Requests  per second, Conversion  rate, TimetoFirstByte,  TimetoInteract, Error Rate  Performance testing,  Network testing, cross  browser testing, multiple  device testing, multiple OS  testing Performance Engineering,  AI in performance testing,  Chaos Engineering GTMetrix, Pingdom  Tool, Google  Lighthouse, Web  Page Test, etc.<|endoftext|>4 Security Conformance with security  standards across geographies.  Secured transactions, cyber  security, biometric security,  user account security Application security testing,  Cyber Assurance, Biometric  testing, Payment Testing Blockchain testing, Brain  Computer Interface BCI  testing, Penetration  testing, Facial recognition Sucuri SiteCheck,  Mozilla Observatory,  Acunetix, Wapiti 5 Usability Navigation on website,  visibility, readability, chatbot  integrations, user interface  Usability testing, Readability  testing, Eye tracking, Screen  reader validation, Chatbot  testing AI led design testing,  Emotion tracking,  Movement tracking Hotjar, Google  Anaytics, Delighted,  SurveyMonkey,  UserZoom 6 Web  Accessibility Conformance to web  accessibility guidelines as per  geography Checking conformance to  guidelines [Web Content  Accessibility Guidelines  (WCAG), Disability  Discrimination Act (DDA)  etc.) Persona based accessibility  testing Level Access, AXE,  Siteimprove, SortSite.<|endoftext|>7 Customer  Analytics Net Promoter Score, Customer  Effort Score, Customer  Satisfaction, Customer  Lifetime Value, Customer Turn  Rate, Average Resolution  Time, Conversion Rate,  Percentage of new sessions,  Pages per session Sentiment Analytics, Crowd  testing, Real time analytics,  social media analytics, IOT  testing AR/ VR testing, Immersive  testing Sprout Social, Buffer,  Google Analytics,  Hootsuite.<|endoftext|>8 Social Media  Integration Clickthrough rate, measuring  engagement, influence, brand  awareness Measuring social media  engagement, social media  analytics AR/VR testing, Advertising  Playbook, Streaming Data  Validation Sprout Social, Buffer,  Google Analytics, etc.<|endoftext|>Table 1 Holistic Customer Experience Validation and Trends

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 External Document © 2022 Infosys Limited Emerging Trends in Customer Experience Validation Below are few of the emerging trends that can help enhance the customer experience. QA team can use quantifiable attributes to  understand where exactly their focus is required.  Telemetry Analysis using AI/ML in  Customer Experience  Telemetry data collected from various  sources can be utilized for analyzing the  customer experience and implementing  the appropriate corrective action. These  sources could be the social media feeds,  various testing tools mentioned in Table 1,  web pages, etc. Analytics is normally done  through custom built accelerators using  AI/ML techniques. Some of the common  analytics are listed below: • Sentiment Analytics: Sentiment of  the message is analyzed as positive,  negative, or neutral • Intent Analytics: Identifies intent as  marketing, query, opinion etc.<|endoftext|>• Contextual Semantic Search (CSS):  Intelligent Smart Search Algorithm which  filters the messages into given concept.  Unlike the keyword-based search, here the  search is done on a dump of social media  messages for a concept (e.g Price, Quality,  etc.)  using AI techniques.  • Multilingual Sentiment Analytics:  Analyze sentiment based on languages • Text Analytics, Text Cleansing,  Clustering: Extracting meaning out of the  text by language identification, sentence  breaking, sentence clustering etc.<|endoftext|>• Response Tag Analysis: To filter pricing,  performance, support issues • Named entity recognition (NER): To  identify who is saying what on social  media posts and classify  • Feature Extraction from Text: Transform  text using bag of words and bag-of-n- grams • Classification Algorithms: Classification  algorithms assign the tags and create  categories according to the content.   It has broad applications such as  sentiment analysis, topic labeling, spam  detection, and intent detection.<|endoftext|>• Image analytics: - Identifying the  context of the image using image  analytics, categorizes the image and  sort them according to gender, age,  facial


Continue # Infosys Whitepaper
 expression, objects, actions,  scenes, topic, and sentiment.<|endoftext|>Computer Vision Computer Vision helps to derive  meaningful information from images,  objects, and videos. With hyper  personalization of customer experience,  we need an intelligent and integrated  customer experience which can be  personalized by the people. While AI  plays an important role in analyzing the  data and recommend the corrective  actions, Computer Vision helps to capture  the objects, face expressions, etc. and  the image processing technology can  be leveraged to interpret the customer  response.<|endoftext|>Chatbot A chatbot is an artificial intelligence  software that can simulate a  conversation (or chat) with a user.  Chatbot has become a very important  mode of communication and most of  the enterprises use chatbots for their  customer interactions, especially in the  new normal scenario.<|endoftext|>Some of the metrics to measure  customer experience using a  chatbot are: 1. Customer Satisfaction: This metrics  will determine the efficiency and  effectiveness of chatbot. Questions  which can be included in this can be:  • Whether chatbot was able to  understand the query of the  customer?  • Was the response provided to the  specific query?  • Whether the query was  transferred to the specific agent  in case on non-resolution of the  query 2. Activity Volume: How frequently  is the chatbot used? Is the usage of  chatbot increasing or decreasing? 3. Completion Rates: This metric  measures the amount of time the  customer took. Also, the levels of  question asked by the customer.  It will measure the instance  when the customer opted to get  resolution from an agent and left  the chatbot. This will help identify  the opportunities to improve the  chatbot further, improving the  comprehension, scripts and adding  other functionalities to the chatbot.<|endoftext|>4. Reuse Rates: This metric will  provide the insight on the reuse of  chatbot by the same customer. This  will also enable to dive deep into  the results of customer satisfaction  metric, help us understand new  user v/s old user usage ratio and  allow us to conclude on re-usability  and adaptability of chatbot by  customers.  5. Speech Analytics Feedback: In this  speech analytics can be used to  examine customer interactions with  service agents. Some of the specific  elements to be noted include  tone of the call, frustration level  of customer, knowledge level of  customer, ease of use etc.<|endoftext|>Measuring Tools  Even though there are various  tools available from startups like  BotAnalytics, BotCore, CharBase,  Dashbot, etc., most of the QA teams are  measuring the Chatbot performance  parameters through AI/ ML utilities.<|endoftext|>

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 External Document © 2022 Infosys Limited Alternative Reality Alternative Reality includes augmented  reality (AR), virtual reality (VR) and mixed  reality.  AR is in many ways adding value to  the customer experience of an enterprise  by providing an interactive environment  and helps them to stay ahead of their  competitors. The data points used to  measure it overlap with those of website  and app metrics, with addition of a few  new points to be measured.<|endoftext|>Some of the additional metrics to  measure customer experience in  Alternate Reality: 1. Dwell time: Total time spent on the  platform. More time spent on platform  being the positive outcome 2. Engagement: Interaction with the  platform. More the engagement better  is the outcome.<|endoftext|>3. Recall: Ability to remember. Higher  recall rate indicates proper attention  and guides us on the effectiveness of  the platform 4. Sentiment: Reaction. Positive,  Negative and Neutral. This will assist in  understanding the sentiment.<|endoftext|>5. Hardware used: Desktop, laptop, tablet,  mobile etc.  Measuring Tools There is not much automation done in  AR/ VR experience validation. Custom  built utilities using Unity framework  can be explored to measure the AR/ VR  experience.  Brain computer interface A brain computer interface (BCI) is a system  that measures activity of the central nervous  system (CNS) and converts it into artificial  output that replaces, restores, enhances,  supplements, or improves natural CNS  output, and thereby changes the ongoing  interactions between the CNS and its  external or internal environment. BCI will  help in personalizing the user experience by  understanding the brain signals from a user.<|endoftext|>Metrics to measure customer  experience in BCI: 1. Speed - Speed of the user’s reaction.  Higher the speed, more is the user  interest on digital print.<|endoftext|>2. Intensity - Intensity of user’s reaction  towards a digital presence will help  understanding the likes and dislikes of user.<|endoftext|>3. Reaction - This will help understand  the different reactions on digital  interaction.<|endoftext|>Measuring Tools Open-source tools like OpenEXP,  Psychtoolbox, etc. can be leveraged  to build custom built utilities for  measurement of the above metrics {{ img-description : a person is holding an ipad with location & shopping app in the view, in the style of photobashing, rounded shapes, light emerald and violet, blink-and-you-miss-it detail, precision, david brayne, sharp focus }}

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 External Document © 2022 Infosys Limited With multiple channels to interact with  the end customers, companies really  looking at ensuring the digital quality  assurance in a faster and in a continuous  way. To reduce time to market, customer  experience assurance should be automated  with more and more infusion of AI and  ML. Further, quality assurance should  be in an end-to-end manner, where the  developer can ensure the quality even  before the application is passed to QA.  With the adoption of DevSecOps, customer  experience assurance should be an  ogoing process which goes beyond the  conventional QA phase  Some of the technical challenges in  automation are: • Services offered by company should have  a seamless experience with all distribution  channels (Web, mobile, Doc, etc.).<|endoftext|>• Early assurance during development • Ensure regulatory compliance • Collaboration environment for  developers, testers, and auditors with  proper governance  • On demand service availability • Automating the remediation and  Continuous Integration • Actionable insights • Scoring mechanism to benchmark • Integration with Test and Development  tools The above challenges will call for a fully  automated customer experience platform  as depicted below: Automation in Customer Experience Assurance Figure 2 Automation approach for evaluating holistic customer experience An automation approach should be  comprehensive enough to provide a  collaboration environment between  testers, developers, auditors, and the  customers. It needs accelerators or  external tools to measure and analyze  various aspects of customer experience.  Cognitive analysis to ensure continuous  improvement in customer experience is  a key success factor for every enterprise.   As shown in the picture, complete  automation can never be achieved as  some assistive or manual verification is  required. For example, JAWS screen reader  to test the text to speech output. Also, the  platform needs to have the integration  capabilities with external tools for end-to- end test automation.<|endoftext|>IDE plugins for shift left  remediation Online Experience Audit  Services APIs and CI/CD plugins Accessibility Analyzer Sentimental Analytics Visual Consistency checker Google APIs Intelligent application  crawler Cognitive analysis  Dashboards & Reports Cloud Environments with  multi browser & device  PCloudy Applitools ALM JiRA Subscription &  Administration Scheduler Tool adapters External IPs Accelerators Usability Analyzer User touch  points Platform  component Accelerators/ tools Others  Manual Assistive  technologies

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 Conclusion As the digital world is moving towards  personalization, QA teams should  work on data analytics and focus on  analyzing user behavior and activities,  leveraging various available testing  tools. They should also focus on  adapting new and emerging testing  areas like AI based testing, Persona  based testing, Immersive testing, 2D  illustration testing etc. These new  testing areas can help in identifying  the issues faced in providing the best  customer experience, quantify the  customer experience and can help in  improving it.<|endoftext|>Since there is considerable amount  of time, money and effort are put  into QA., for ensuring good ROI, QA  team should start taking customer  experience as a personality-based  experience and work upon all major  aspects mentioned above. QA teams  should look beyond the normal  hygiene followed for digital platforms,  dig deeper and adapt a customer  centric approach in order to make  digital prints suitable to the user in all  the aspects.<|endoftext|>{{ img-description : hands holding a phone with a star rating on it, in the style of interactive experiences, futurist claims, expert draftsmanship, meticulous attention to detail, precision and detail-oriented }}  External Document © 2022 Infosys Limited

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 © 2022 Infosys Limited, Bengaluru, India. All Rights Reserved.


Continue # Infosys Whitepaper
 Infosys believes the information in this document is accurate as of its publication date; such information is subject to change without notice. Infosys  acknowledges the proprietary rights of other companies to the trademarks, product names and such other intellectual property rights mentioned in this document. Except as expressly permitted, neither this  documentation nor any part of it may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, printing, photocopying, recording or otherwise, without the  prior permission of Infosys Limited and/ or any named intellectual property rights holders under this document.  For more information, contact askus@infosys.com  Infosys.com | NYSE: INFY Stay Connected About the
Author Saji V.S     Principal Technology Architect References 1. Customer Experience Validation - Offerings | Infosys 2. https://www.gartner.com/imagesrv/summits/docs/na/customer-360/C360_2011_brochure_FINAL.pdf 3. The Future of CX 2022, a trends report by Freshworks

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# Infosys POV
Title: Reinventing the CSP Product Lifecycle Management for Digital Ecosystems
Author: Infosys Consulting
Format: PDF 1.7

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 An Infosys Consulting Perspective By Sagar Roongta, Kiran Amin and Thiag Karunanithi Consulting@Infosys.com | InfosysConsultingInsights.com REINVENTING THE CSP  PRODUCT LIFECYCLE  MANAGEMENT FOR  DIGITAL ECOSYSTEMS How to sustainably manage product portfolio  complexity in the digital age?

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 CONTENTS 2 1.<|endoftext|>Introduction 2.<|endoftext|>Unified PLM Framework 3.<|endoftext|>Components of Unified PLM 4.<|endoftext|>Recommendations for CSPs 5.<|endoftext|>Infosys PLM Maturity Model Reinventing the CSP Product Lifecycle  Management for the Digital Ecosystems © 2023 Infosys Consulting

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 Generating new sources of revenue & free cash  flows are the top priorities for CSP CEOs in 2023 In our regular interactions with Communication  Service Providers (CSPs) across Asia Pacific,  increasing revenue growth is a key priority for  executive leadership. The traditional revenue  sources have diminished while any price  increase will prove to be an extremely sensitive  subject for price savvy customers. The COVID- 19 pandemic allowed CSPs to emerge  unscathed, or at least to re-think how they now  commit substantial investments to expand  their 5G networks and open additional revenue  sources.<|endoftext|>As per Gartner’s 2023 Board of Director’s  Survey, 46% of boards wanted to expand new  product lines, to create new growth  opportunities.  In this environment, CSPs have  expanded to occupy the role of digital  ecosystem gateways, acting as a marketplace  operator where consumer and enterprise  customers can buy bundled service offering(s)  within an enclosed ecosystem and CSP  partners. These ecosystem partners vary from  digital content providers, gaming fintech,  financial services, cybersecurity, insurance, and  health-tech companies seeking to access the  digital ready customer portfolio of CSPs.  Through these mass-personalized offerings,  CSPs could increase customer stickiness and  reinforce core business goals. For the end- consumer, their CSP becomes not just a  connectivity provider but an incumbent one- stop-shop for digital services. Consequently, as  per a recent IDC report, one in three CSP is  expected to generate more than 15% of their  overall revenue from new digital products and  services, compared to one in six in 2020. 1,2 INTRODUCTION 3 Did You Know? As per a recent IDC report, one in  three CSP is expected to generate more  than 15% of their overall revenue from  digital products and services, compared  to one in six in 2020.<|endoftext|>Reinventing the CSP Product Lifecycle  Management for the Digital Ecosystems © 2023 Infosys Consulting How should CSPs manage their product portfolios  as they launch plethora of new 5G, digital  and innovative offerings?

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 INTRODUCTION 4 As an ecosystem provider, CSPs would be  operating in a dramatically different business  model. Newer, open, and more complex  offerings must be introduced to markets  quickly, which requires more investments and  collaboration, while the lifecycle of each  product must be better controlled from  financial and technological perspectives.  However, the existing CSP product portfolio is  already overly complex, and the product  development process is highly bureaucratic as  it currently functions on legacy systems,  processes, and historical ways of working.  Furthermore, mistakes or shortcomings  perceived in the products or product designs  reach the market because the company cannot  react to market changes quickly enough. The  slowness of an end-to-end process means that  companies are unable to bring its products to  market in rhythm with customers’ wishes,  market changes, and set timetables or to  collect the greatest possible product margin.  Subsequently, CSPs are forced to undergo  expensive product rationalization exercises to  cull out redundant or non-profitable offerings.  Hence, to succeed in the dynamic ecosystem  era, CSP would need to reimagine how they  develop new products whilst innovating and  managing their lifecycles. This requires a deep  dive into their existing product lifecycle  approach from a piecemeal activity to a next  generation product lifecycle management  approach. How and at what level of each  company conducts its product lifecycle  management implementation depends on  several factors that can be explained through  the Unified PLM Framework.<|endoftext|>For sustainable digital ecosystem success, CSPs  need to re-imagine their PLM activities as a  strategic initiative Did You Know? According to Bain’s Digital GPS  Benchmark, more than half of respondents  from telecom industry, said that automation  of back-office operations like PLM, as their  top digital priority3 Reinventing the CSP Product Lifecycle  Management for the Digital Ecosystems © 2023 Infosys Consulting

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 The Unified Product Lifecycle Management  (Unified PLM) is a comprehensive approach to  implementing product lifecycle strategy in a  telecom organization. It integrates a  comprehensive PLM strategy, a modular  product architecture, an efficient PLM process  design and enabling data & technology  architecture that saves time, reduces product  complexity, and excels in a multi-party  environment. It creates a framework to capture  insights across lifecycle phases to rapidly  create new offerings while having an  automated process to right-size unused  offerings.4 It starts with setting up a PLM strategy and  governance structures that defines the  stakeholders critical to implement a product  architecture relevant for the digital age which  need not be complex but simplified and  modularized. Subsequently, the enabling  processes and technology are implemented to  execute the product portfolio and lifecycle  strategy.  The unified PLM framework has five key  components: Unified PLM is a holistic framework for PMs to  save time and reduce costs in a multi-party world UNIFIED PLM FRAMEWORK 5 • Strict process stage gates • IT change & configuration  management • Process improvement  initiatives • Product retirement process • Process support systems • Decision support system • Product data management  and policies • Process efficiency tools • PLM maturity  assessment • Product portfolio &  PLM alignment • PLM governance  framework • Incorporation of  product variants • Organizational structure • Roles and responsibilities • Skill & Resources  allocated • Multi-party collaboration • Modular marketing  product structure • Product rules governance • Modular process design  • Reusability of  components PLM Strategy PLM Process Excellence Data & Technology Product Design Organization & People Unified PLM Framework Reinventing the CSP Product Lifecycle  Management for the Digital Ecosystems © 2023 Infosys Consulting

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 The PLM strategy is the foundational rail on  which the organization embarks on  transforming its PLM activities. It is an optimal  alignment between the need for innovation  and marketing priorities with a governance  mechanism that effectively addresses the PLM  priorities. It allows for seamless  synchronization of product development,  market management, and retirement  processes. It also sets the necessary  governance and control mechanisms to detect  and mitigate potential threats. However, CSPs  should be wary of implementing a one-size- fits-all approach to all products in the portfolio;  they should treat various products differently  based on their operating model, product  complexity and lifecycle behavior.<|endoftext|>For example, a device bundled with mobile  plans have a limited shelf-life; hence the PLM  Strategy should be agile to respond faster to  any type of change while enterprise products  have a longer shelf-life to allow for market  development and sales cycles. Hence, an  effective PLM strategy creates a framework for  managing distinct product variants within the  strategic priorities.  Unified PLM begins from strategy to create the  rails on which people & products are organized COMPONENTS OF UNIFIED PLM 6 PLM Strategy 1 PLM Governance 2 Alignment to Product Portfolio  Strategy 3 PLM Process Variants PLM Strategy Reinventing the CSP Product Lifecycle  Management for the Digital Ecosystems © 2023 Infosys Consulting

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 COMPONENTS OF UNIFIED PLM PLM activities cut across different stakeholders  such as product marketing, sales, technology,  customer management, business intelligence  and finance teams. And in an unstructured  environment, these activities are conducted on  an ad-hoc basis by individual functions. And  consequently, conflicts between stakeholders  are quite common. Various case studies have  demonstrated that more conflicts lead to a  higher probability of final product failure.  Hence, for a sustainable PLM success, an  organizational structure must


Continue # Infosys POV
 be present where  all the relevant departments & teams can  efficiently collaborate and coordinate.<|endoftext|>CSPs have traditionally implemented a  divisional structure or a matrix structure where  group managers from distinct functions  collaborate to develop new products. However,  with the number of parties spreading across  multiple organizations, new organizational  models must be considered. Implementing new  structures include offering management  squads or agile product teams to independently  execute the PLM strategy within their  organizations. These squads are accountable  for the entire lifecycle of an offering, including  validating the need in the marketplace and  conducting the impact analysis on engineering,  sales, support, and budgeting. In addition, these  squads have the mandate to work across  business units and disciplines to harness the  company’s entire arsenal of talent and  knowledge base. These structures could be  customized based on process variants and the  strategic necessities of the company.  7 Organization & People 1 PLM Organization 2 Responsibility Assignment 3 Employee Empowerment Organization & People Reinventing the CSP Product Lifecycle  Management for the Digital Ecosystems © 2023 Infosys Consulting

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 What is modularization? Modularization is an activity of dividing a  product or process into logical and  interchangeable modules. The objective is to  create a flexible system that enables creation  of different configurations, while reducing the  need to create unique building blocks each  time.6 Benefits of a modular system are  numerous: • Higher efficiency as modules can be  consolidated across different products • Higher agility as changes & modifications  can be isolated to specific modules, enabling  others to remain unchanged • Higher flexibility as mass customization on  individual module can be achieved at scale The product design component aims to enable  product component reusability by defining the  constraints and rules for decomposing the  product functionality into meaningful modules  with a coherent product data model. For CSPs,  the product structure includes breaking down  the product offer and reusing the individual  service modules from a market, technical and  operational perspective. A modular market  perspective includes cross-linkage between  aspects like tariff plans, fees, market segments,  etc. A technical perspective includes  decomposing the product offering into  individual product, service, and resource  modules. Finally, an operational perspective  includes reusing process modules such as  fulfillment, assurance, and billing processes  independent of the product type. Important  advantages of a modular product design  include faster time to market, more efficient  development, better innovation ability and  lesser operational costs as complex systems  become easier to manage; parallel activities  can operate independently, reusability of  existing components, and faster fault  localization.  The product model defined as per the SID  framework by TM Forum is a classic example of  a modular product architecture.5 COMPONENTS OF UNIFIED PLM 8 Product Design 1 Modular Marketing Product  Design 2 Product Rules Governance 3 Modular Process Design Product Design LOW HIGH HIGH No. of product variants offered  to customers Cost efficiency of  product portfolio LOW Customization Standardization Modularization Relative cost efficiency with the increase  in product variants in the market Reinventing the CSP Product Lifecycle  Management for the Digital Ecosystems © 2023 Infosys Consulting

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 A  typical PLM process includes six phases - ideation, design, development, go-to-market,  sales, and finally, retirement. Most CSPs have a  well-documented product lifecycle  management process of end-to-end activities  however, the presence of a process excellence  framework is rarer or if one does exist, it needs  to be more effectively applied.<|endoftext|>PLM process excellence embeds the  continuous improvement in the PLM process,  aligning with the strategic PLM goals. It  includes unambiguously defining relevant  activities, their sequence, data requirements  and configuration. In addition, it also defines  the roles and responsibilities of the product  organization throughout the value chain to  ensure proper execution. The critical elements  within PLM process excellence includes  definition of stage gates that ensure that the  product satisfies the minimum criteria to move  to next lifecycle phase, standardization of  various process variants and a regular  retirement process.7 A regular rules-based product retirement  process is critical in making the product  offerings more targeted and manageable and  eliminates the need for product rationalization  activities every few years.  COMPONENTS OF UNIFIED PLM 9 PLM Process Excellence 1 Continuous Process  Improvement PLM Process Excellence 2 Strict Process Stage Gates 3 IT Change & Configuration  Management 4 Retirement Management                                                                                Standard PLM Process Reinventing the CSP Product Lifecycle  Management for the Digital Ecosystems © 2023 Infosys Consulting

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 The objective of Data & Technology component  is to provide frameworks that increase the  efficiency of the PLM process execution while  making it more efficacious. Informed by the  product design and PLM process components,  it helps in implementing these components in a  business environment.  The product data management forms the  backbone for managing and controlling the  lifecycle of a product. It starts from the market  research and business planning data to the  subsequent product performance and eventual  retirement justification. A well-constructed  PDM framework enables all stakeholders to  capture, communicate and disseminate all  heterogeneous data throughout its lifecycle. It  helps in speeding up product development,  reduce errors and increase efficiency of  resources.  The technology component includes two key  elements – process support systems and  decision support systems. Process support  systems include RPA or workflow management  system for automated process implementation,  decision gate evaluation, process compliance  and automated triggers. Decision support  systems include machine learning and AI-based  tools that improve the decision-making  capabilities to identify new product  opportunities and proactively retire products  that are underperforming.  COMPONENTS OF UNIFIED PLM 10 Data & Technology 1 Process Support Systems 2 Decision Support Systems 3 Product Data Management Data & Technology Reinventing the CSP Product Lifecycle  Management for the Digital Ecosystems © 2023 Infosys Consulting

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 More than ever, customers are expecting more  for less. To manage now and future needs,  CSPs must understand their own core  capabilities and have the expertise, agility, and  flexibility to react to meet demand and  ultimately remain relevant.  Developing a strategy that balances internal  barriers and constraints, whilst integrating  organization goals and vision is by no means an  easy task. This coupled with external  competition has intensified the entire PLM  playing field, thus essential for CSPs to  understand the ‘real’ opportunity within that if  managed and executed well, will lead to create  value across the board.<|endoftext|>CSPs across the world have approached their  PLM initiatives that vary on a broad spectrum  of parameters from ad hoc manual activities to  the use of AI & machine learning algorithms to  recommend product lifecycle actions. The  effectiveness of these initiatives naturally  would be determined against internal as well as  external factors. However, implementation of  any PLM project requires an extensive change  in intra and inter-organization processes, new  types of skills and capabilities, and more than  that, an organization wide cultural and  strategic transformation. Hence, any PLM  initiative would require strategic commitment  and resources. 8 As a first step towards that initiative, a maturity  model can help CSPs to assess as-is while  giving a guided path to advance their PLM  capabilities for the future.  Evaluate your as-is PLM capabilities by  determining your organization’s PLM maturity RECOMMENDATIONS FOR CSPs 11 Reinventing the CSP Product Lifecycle  Management for the Digital Ecosystems © 2023 Infosys Consulting

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 PLM Maturity Model Infosys Consulting has developed the PLM  Maturity Model based on industry research,  best practices and external trends to assess the  relative CSP performance on PLM.  It is a scientific method to rate the performance  of individual component against the best-in- class industry standards and identify  opportunities for improvement. It considers not  only existing PLM initiatives but also the  relevant market trends, customer readiness  and competitive profile to rate the  performance of a CSP on their maturity state Infosys’s PLM Maturity Model categorizes CSPs  into 5 maturity levels: 1.<|endoftext|>Ad-hoc: Ad-hoc is the preliminary maturity  state where there is no evidence of a PLM  strategy and vision. This stage is often  characterized by inconsistent processes,  monolithic product structure and absence  of enabling process and technological  framework. The lifecycle activities often are  executed on a case-by-case basis by  individual functions in the organization for  a specific need.  The PLM maturity model assesses relative  performance to recommend next course of action INFOSYS PLM MATURITY MODEL


Continue # Infosys POV
 12 Infosys PLM Maturity Model Reinventing the CSP Product Lifecycle  Management for the Digital Ecosystems © 2023 Infosys Consulting

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 INFOSYS PLM MATURITY MODEL 13 2. Structured: In a structured state, a high- level PLM strategy and governance is  present which defines PLM objectives and  participating stakeholders. This structured  state incorporates basic process and  governance framework to increase  efficiency with a singular focus on reducing  time to market.  3.<|endoftext|>Integrated: An integrated maturity state  increases the PLM coverage with different  product variants and parties included in the  PLM activities. Considering disparate  product variants, integrated state CSPs are  able to modularize their product  architecture that is scalable across different  product offerings and operating models.<|endoftext|>4. Automated: In an automated maturity  state, CSPs incorporate multiple systems  and tools to automate their PLM  implementation. This includes use of a  centralized product data management  capability and use of process support  systems to track and manage product  lifecycle stage.  5. Adaptive: CSPs in the adaptive maturity  state, increasingly use AI and Machine  Learning algorithms to analyze and predict  the performance of the marketed products.  AI-based tools help CSPs to analyze  customer behavior to recommend product  offering ideas and process automation  opportunities for better PLM outcomes.  Common Pitfalls On their path to achieving PLM  transformation, CSPs need to avoid certain  common pitfalls: • Lack of executive commitment - PLM cannot  be treated as a siloed initiative, its  sponsorship must have the right  organizational backing.<|endoftext|>• Fragmented governance - where PLM is not  defined or limited with no real controls.<|endoftext|>• Limited process standardization across  departments, making it more a free for all  rather than centralized and cohesive  • Lack of empowerment and resources  allocated to the PLM organization to  implement change  • Visibility of product data – to support  decision making and product performance • Inability to utilize new emerging technologies  – use newer technologies to support PLM  process e.g., advanced data analytical tools,  AI, ML etc.  • Proliferation of products –With the  improvement in product go-to-market  timelines, avoid creating new superfluous  offerings  Although daunting, it is not an impossible task  to move away from more traditional CSP  behaviors, the key to progression and moving  forward is to first understand where you  currently are and how incrementally you can  move in the right direction.<|endoftext|>Hence, it is now very important for CSPs to  consider self-maturity assessment.  Reinventing the CSP Product Lifecycle  Management for the Digital Ecosystems © 2023 Infosys Consulting

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 Take the Maturity Health Check NOW The Infosys consulting maturity health check  will allow CSPs to:: • Evaluate PLM health and performance  against best-in-class operationalized  processes and technology  implementations • Quickly identify and pinpoint areas with  biggest improvements and business gain • Execute a plan of how to take current as- is and “upgrade” to the next maturity  state  • Use a scientific approach to objectively  assess performance against PLM  company wide objectives • Implement a PLM monitoring framework  to assess different components on a  continual basis INFOSYS PLM MATURITY MODEL 14 SAGAR ROONGTA Consultant Singapore +65 8264 6036 Sagar.Roongta@infosysconsulting.com MEET THE EXPERTS AUTHORS KIRAN AMIN Senior Principal Singapore +65 9742 7657 Kiran.Amin@infosysconsulting.com Reinventing the CSP Product Lifecycle  Management for the Digital Ecosystems © 2023 Infosys Consulting THIAG KARUNANITHI Associate Partner Australia +61 4064 2736 0 Thiag.Karunanithi@infosys.com

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 1.<|endoftext|>https://www.gartner.com/en/articles/see-the-key-findings-from-the-gartner-2023-board-of- directors-survey 2. https://www.idc.com/getdoc.jsp?containerId=prAP49619722 3.<|endoftext|>https://www.bain.com/insights/digital-transformation-what-matters-most-in-your-sector- interactive/ 4. https://www.researchgate.net/publication/204100092_Next_Generation_Telco_Product_Lifecy cle_Management_- _How_to_Overcome_Complexity_in_Product_Management_by_Implementing_Best- Practice_PLM 5. https://www.tmforum.org/oda/information-systems/information-framework-sid/ 6. https://www.modularmanagement.com/blog/all-you-need-to-know-about-modularization 7.<|endoftext|>https://www.productfocus.com/product-management-resources/infographics/product- management-lifecycle/ 8. https://link.springer.com/article/10.1007/s00170-013-5529-1 REFERENCES 15 Reinventing the CSP Product Lifecycle  Management for the Digital Ecosystems © 2023 Infosys Consulting

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 consulting@Infosys.com InfosysConsultingInsights.com LinkedIn: /company/infosysconsulting Twitter: @infosysconsltng About Infosys Consulting  Infosys Consulting is a global management consulting firm helping some of the world’s most recognizable  brands transform and innovate. Our consultants are industry experts that lead complex change agendas  driven by disruptive technology. With offices in 20 countries and backed by the power of the global Infosys  brand, our teams help the C-suite navigate today’s digital landscape to win market share and create  shareholder value for lasting competitive advantage. To see our ideas in action, or to join a new type of  consulting firm, visit us at www.InfosysConsultingInsights.com.  For more information, contact consulting@infosys.com  © 2023 Infosys Limited, Bengaluru, India. All Rights Reserved. Infosys believes the information in this  document is accurate as of its publication date; such information is subject to change without notice. Infosys  acknowledges the proprietary rights of other companies to the trademarks, product names, and other such  intellectual property rights mentioned in this document. Except as expressly permitted, neither this document  nor any part of it may be reproduced, stored in a retrieval system, or transmitted in any form or by any means,  electronic, mechanical, printed, photocopied, recorded or otherwise, without the prior permission of Infosys  Limited and/or any named intellectual property rights holders under this document.

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# Infosys Whitepaper
Title: The right approach to testing interoperability of healthcare APIs under FHIR
Author: Infosys Limited
Format: PDF 1.7

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 VIEW POINT THE RIGHT APPROACH TO TESTING  INTEROPERABILITY OF HEALTHCARE  APIs UNDER FHIR

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 External Document © 2020 Infosys Limited External Document © 2020 Infosys Limited Abstract Historically, the lack of mutual data  exchange on patient health between  entities in the healthcare industry has  impaired the quality of patient care. This  has led to poor health outcomes and  increased costs for patients. The Trump  administration’s MyHealthEData initiative  has the stated objective of placing the  patient at the center of the US healthcare  system. The initiative aims to promote  interoperability of patient health data  between entities using latest technologies  such as cloud and APIs.<|endoftext|>This paper outlines the test approach  to ensure compliance with the CMS  (Centers for Medicare and Medicaid  Services) payor policies mandate effective  July 2021. The paper explains 3 simple  steps for interoperability of cloud/on- premises healthcare APIs between payors  and providers as per Fast Healthcare  Interoperability Resources (FHIR)  guidelines using the Infosys FHIR testing  solution. This solution has been built on  the proven Infosys Interoperability Test  Automation Framework that leverages  open-source tools, cloud components, and  test servers.<|endoftext|>

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 External Document © 2020 Infosys Limited External Document © 2020 Infosys Limited Introduction All healthcare payors, providers, and  stakeholders need to ensure that their  systems are truly interoperable based on  Fast Healthcare Interoperability Resources  (FHIR) guidelines. Organizations are seeking  to minimize the manual effort involved  in ensuring data integrity and real-world  testing as per these guidelines. The need  of the hour is test accelerators that can  seamlessly integrate with FHIR cloud/ on-premises servers to run automated  conformance and data validation tests.<|endoftext|>Fragmented data consolidation Currently, organizations have non-standard  data consolidation architecture due to  the nature of their source systems. To be  CMS (Centers for Medicare and Medicaid  Services) certified, organizations must  implement FHIR-compliant microservices- based architecture for API enablement  including authentication and security  policies. This adds additional complexities  for testers to validate data accuracy and  performance at the FHIR layer (cloud/ on premise) in addition to specification  conformance validation.<|endoftext|>Any test strategy to achieve FHIR  compliance must be carefully structured  to mitigate all known challenges. To begin  with, organizations must finalize their  API end-to-end operating model, cloud/ on-premises implementation strategy,  consent management, and intermediate  data aggregation strategy so that we  understand the various stages that need  FHIR testing.  Key challenges Adoption and interpretation  of FHIR standards FHIR standards have base rules,  constraints, and other metadata that  make them complex to interpret and  derive conformance scenarios. FHIR  regulated payors and providers will be  required to implement FHIR compatible  cloud/on-premises based healthcare APIs  considering different market segments  like Medicare, Medicaid, and any other  state-specific inclusions. They also need  to enable various FHIR resources such as  diagnostics, medication, care provision,  billing, payments, and coverage to  be accurate and accessible as per the  standard SLAs for patient and provider.<|endoftext|>Real world testing This is a gray area at this initial stage  because of the lack of a SMART application  (a user-facing application that connects  to payors or providers for a patient’s  health records). This limits the process of  simulating and validating complete end- to-end testing for FHIR conformance. A  payor or provider must qualify themselves  as FHIR compliant both internally and  externally through acceptance from  consumers. To achieve acceptance, a  payor needs to conduct multiple tests to  cover data integrity, conformance, and  consumer registration and consent as per  FHIR guidelines. Tests need to be conducted  between peer-to-peer servers, client to  server, and from a standalone server to a  proven FHIR-compatible test framework.<|endoftext|>Infosys 3-step process for  interoperability testing Infosys recommends these 3 simple steps  to achieve interoperability of cloud/on- premises healthcare APIs between payors  and providers as per FHIR guidelines Step 1 – Functional/non- functional testing  The primary focus areas of functional  testing are: • Conformance (structure and behavior)  validation based on FHIR specifications • Data validation of cloud/on premise- based healthcare APIs through the  original source or from intermediate  data aggregation or virtualization. If  an organization considers any data  aggregation before FHIR mapping, then  additional testing around data quality  should be done to ensure source data is  aggregated correctly at the FHIR layer  Non-functional testing such as  performance and security testing are  equally important given that APIs are  exposed externally using OAuth2.0 or  OpenID connect protocols.<|endoftext|>

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 External Document © 2020 Infosys Limited External Document © 2020 Infosys Limited Step 2 – Regulatory  compliance testing This validation should ensure payor or  provider complies with the guidelines  defined by CMS and FHIR. The primary  focus of regulatory compliance testing  is on features mandated by HealthIT  standards which includes self- discoverability, capability statement,  authentication/authorization, FHIR  conformance and so on. Every payor must  undergo this testing so that they can pass  the CMS certification within the given  deadline.<|endoftext|>

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 External Document © 2020 Infosys Limited Step 3 – End-user beta testing There are several third-party healthcare  applications (cloud/on-premises) that are  under development by Apple, Amazon,  Google, and other payor/vendors that are  registered in their respective developer  portals. Our strategy involves partnering  External Document © 2020 Infosys Limited with these vendors and using their beta  applications to integrate with payor FHIR  servers. This will provide payors early  confidence in terms of usability and  conformance with regulation.<|endoftext|>Figure 1 below depicts an end-to-end  automated test-driven development flow  of Infosys Interoperability Test Automation  Framework covering the 3-step testing  procedure. The framework leverages  Infosys FHIR Testing Solution, open-source  tools, cloud components, and test servers  to achieve FHIR compliance seamlessly  across various healthcare entities within a  short time to market.<|endoftext|>Figure 1: Infosys Interoperability Test Automation Framework

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 External Document © 2020 Infosys Limited External Document © 2020 Infosys Limited The road ahead Today, organizations are focused on  getting their FHIR-compliant cloud/on- premises based patient access API and  provider directory API up and running.  However, organizations must also think  of other policies specified in the CMS final  rules. These include payor to payor data  sharing, improving user experience for  the beneficiary, and admission/discharge/ transfer event notifications. With such  requirements in mind, payors and vendors  need to think about developing a scalable  test automation framework that not only  works between payors and consumers but  also across payors.  Infosys is enhancing the capabilities of its  FHIR testing solution so that it is future  ready.<|endoftext|>

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 External Document © 2020 Infosys Limited Conclusion  Establishing end-to-end testing for FHIR  guidelines compliance across healthcare  payors and third-party vendors comes with  its own challenges and complexities. The  only way forward is based on the final CMS  interoperability rule.  A solution that ensures fast and effective  delivery of FHIR rules to consumers should  focus on two key aspects: • Building configurable FHIR accelerators  with the ability to support cloud/on- premises FHIR servers • Identifying FHIR-compliant consumers for  continuous testing practices The end goal is to build a test approach  that can be scaled rapidly based on the  rate of increase in FHIR adoption by payors,  providers and consumers.<|endoftext|>External Document © 2020 Infosys Limited

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 © 2020 Infosys Limited, Bengaluru, India. All Rights Reserved. Infosys believes the information in this document is accurate as of its publication date; such information is subject to change without notice. Infosys  acknowledges the proprietary rights of other companies to the trademarks, product names and such other intellectual property rights mentioned in this document. Except as expressly permitted, neither this  documentation nor any part of it may be reproduced, stored in a retrieval system,


Continue # Infosys Whitepaper
 or transmitted in any form or by any means, electronic, mechanical, printing, photocopying, recording or otherwise, without the  prior permission of Infosys Limited and/ or any named intellectual property rights holders under this document.  For more information, contact askus@infosys.com  Infosys.com | NYSE: INFY Stay Connected https://www.cms.gov/Regulations-and-Guidance/Guidance/Interoperability/index https://inferno.healthit.gov/inferno/ https://www.aegis.net/touchstone.html https://projectcrucible.org/ https://www.logicahealth.org/solutions/fhir-sandbox/ https://fhir.cerner.com/smart/ https://apievangelist.com/2019/09/18/creating-a-postman-collection-for-the-fast-healthcare-interoperability-resources-fhir-specification/ https://hapifhir.io/hapi-fhir/docs/validation/introduction.html About the
Author  Amit Kumar Nanda, Group Project Manager, Infosys References:

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