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Software Product Engineering – To be relevant and successful in the digital transformation era
Software Product Engineering – To be relevant and successful in the digital transformation era

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In recent years, most companies globally have felt one of the most important if not the most important results of digital transformation: the skyrocketing significance of software. Software products and solutions now directly affect the financial success of a company. The growing importance of software in digital transformation implies a tectonic change for software product companies. A new approach that changes the entire engineering model and practices must align with fast-changing customer needs and increased competition.  Those developers who do not jump on this bandwagon risk falling behind. While user experience and speed are key, they cannot be delivered at the risk of quality and security. For software product companies to be relevant and successful in the digital age, it means,

  • Super high speed of innovation and differentiation – daily release
  • Zero distance to customer – the product to align with varying customer needs and personalization
  • Reduce time to market – faster development and incremental releases
  • Optimize cost
  • Ensure high availability, scalability and performance
  • Meet security and regulatory requirements

However, it’s not enough to adjust existing product engineering processes, technology and infrastructure to adapt to the radical change that digital transformation represents. The short-term approach of responding to current and often past challenges seldom prepares the organization for when the changes induced by digital transformation take root. New technologies, development methodologies and customer needs are being created at an unprecedented pace, introducing a trend of technological heterogeneity. A comprehensive set of capabilities encompassing the product architecture, engineering practices and culture to deliver on key success factors must be developed. Some of the must-have capabilities to be successful in the digital age include,

  • Complex highly distributed architecture adopting cloud-native architecture and microservices principles and serverless technologies:  It provides flexibility and agility for the products. The benefits span parallel development from multiple POD teams with varying programming skills to efficient and optimal deployment and scalability requirements. It also overcomes cloud services vendor locking
  • Low code/No code platform: This platform provides rapid development with built-in capabilities for integration, data ingestion, data processing and visualization to build a complex solution for many industry use cases. It can give up to 10X faster development and resource efficiencies.
  • Adoption of open source:  It is recommended to utilize open source frameworks for better TCO using proven open source qualification frameworks based on support, maturity and quality considerations. Several open source options are available across the ecosystem, right from UI backend, database, integration and data analytics.
  • Product telemetry: Product telemetry and association with applications logs, monitoring logs can provide better insights for enriching the customer experience.
  • Customer in the loop – A/B testing: Best practices such as A/B testing can enhance the customer experience and hence retention and increase page visits. 
  • SAFe model: Proven scaled agile frameworks like SAFe help improve time to market and increase team productivity by actively involving employees.
  • CI/CD: A new set of tools and frameworks are available from the open source or commercial vendors for automating complete development lifecycles covering continuous integration, continuous delivery and continuous deployment. This is crucial for delivering incremental features to customers within short intervals.
  • Secure development life cycle: It is essential to deliver software with security and vulnerability aspects as top considerations. Secure Development Lifecycle delivers value by bringing in security best practices and tools across the development life cycle.
  • Infra-as-code: This involves infrastructure management (networks, virtual machines, product tech stack, load balancers and architecture topology) in a descriptive model through automation, using the same versioning as what the software product engineering team uses for source code. Applying the principle that the same source code generates the same binary, an infra-as-code model generates the same environment every time it is applied with added control for policy, security and compliance requirements. The idea is to create a requirement environment for lab and production that is fully automated and self-serve as much as possible.
  • Site reliability engineering: This brings software engineers in POD structure, modern observability tools in SaaS operations, cloud infra, OS and middleware support to ensure extreme automation, full-stack accountability and a smooth postmortem.
  • OKR driven organization: This framework puts purpose at the center of work by unifying the business around its most important priorities and enabling employees to accomplish them. It changes the focus from delivering on time to delivering right the first time. A mindset change from feature working to feature mapping helps accomplish outcomes for the end customer.
  • AI/ML in software engineering (also known as Infosys’ Live engineering): This integrates and analyzes data from disparate ALM and DevSecOps tools in real-time to provide capabilities to various persona to learn continuously, adjust and respond quickly and deliver efficiently.

The key to staying competitive in the digital age is to stay open to new patterns, process models, technologies and toolchains. Earlier investments in processes that work well in the present should not restrict this readiness for change. To succeed in this journey, product vendors, partners and service providers are investing in making fundamental changes across the required dimensions. These dimensions encompass talent development, training and adoption of modern technology, practices and design patterns, developing AI/ML based platform solution for automation and self-service, and utilizing analytics and insights from data produced in software engineering processes to make it smarter.

 

About the Authors

Krishna Markande

1. Krishna Markande - Associate Vice President, Senior Principal Technology Architect, Software Product Engineering, Infosys Ltd

Uday Kumar gupta

2. Uday Gupta - Associate Vice President, Senior Industry Principal, Software Product Engineering, Infosys Ltd


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