How to measure software engineering teams’ effectiveness
C-level executives often ask how to manage large teams and ensure their productivity. Measuring productivity requires having the right data/metrics and understanding team dynamics, celebrating successes, and learning from failures. Reinforcing messages through behavior and actions is crucial – individuals will only believe in a culture of learning and improvement when they see it in practice
The lifecycle of change is comprised of Idea, Code, Build, Deploy, Manage, and Learn. Some KPIs worth considering are:
- Deployment frequency: understanding the number of deployments to production measured in days, weeks, months
- Delivery lead time – all about how long it takes to get from code to deployment – measured in days
- Change volumes – number of story points that are actually packed into releases
- Meantime to recovery – how long it takes to figure out there is a defects, and how long once a defect found in production, to deploy a fix i.e. deploy to deploy ie measuring things such as “change success rate percentage”, Time to restore from defect and/or incident, number of Problems in production and age of these problems in production
- Continuous improvement: Are teams conducting retrospectives and learning from their experiences? Sharing learnings across all teams is essential for overall improvement.
No individual data point should be looked at in isolation. For example, Jira can help measure predictability, velocity, and productivity through burn down charts. However, comparing these metrics between teams is not productive. Instead, encourage teams to use these charts as guides to identify areas for improvement. Ask Scrum Masters to explain any anomalies, such as delayed testing, scope increases, or team member absences, to better understand the context behind the data
The making of XOOTS Part 27 | What technologies to bet on in the next 3-5 years
C-level executives often ask what technological innovation will drastically change businesses in the next few years. Tech trends matter, as they influence IT and business strategies.
- AI, ML, deep learning: Utilizing deep learning to drive personalization and automation.
- AI: Leveraging computers to mimic human problem-solving and decision-making
- ML, a subset of AI: Computers learn from data using algorithms to perform tasks without being explicitly programmed
- Supervised learning: Labelled data, e.g., estimating output value through regression techniques.
- Unsupervised learning: Unlabeled data sets, processed without human intervention.
- Deep Learning, a subset of ML: Complex algorithms modeled on the human brain enable processing unstructured data such as documents, images, and text.
- Cloud Native platforms: Beyond “lift and shift,” cloud-native platforms optimize for cloud computing, accelerating, and enhancing capabilities. This involves re-architecting or re-writing applications using modern languages and frameworks.
- Serverless: With serverless, the cloud service provider automatically provisions, scales, and manages the infrastructure required to run the code,
- Composable applications: Building modular, autonomous, orchestrated, and discoverable applications drives consistency in customer experience through reusability. These applications, built on cloud-native platforms, create efficiency and consistency by packaging business capabilities for use in multiple applications.
- Distributed workplace and distributed experience: In the post-pandemic era, remote work and service consumption are the new norm. This has led to the emergence of new digital services.
- For employees: Digital collaboration capabilities, location independence, and one-click connections.
- For customers: More distributed engagement, such as remote healthcare and remote fulfillment (e.g., drones).
- Total experience: Interconnecting four key experiences – employee, customer, user, and multi-experience. Multi-experience combines web, mobile, chatbots, wearables, and immersive technologies, transforming both business operations and service delivery. Experience is a key differentiator.
- Blockchain, Crypto, NFTs: NFTs (non-fungible tokens) are unique digital tokens that, like cryptocurrencies, exist on the blockchain as cryptographic assets. Unlike interchangeable cryptocurrencies, NFTs are singular and unique.
- Low code platforms: Low-Code Platforms, e.g., RPA, Power Apps, and Apple’s SwiftUI for iOS development: These tools use 60% less code and simplify the development process
At XOOTS, we believe that growth can only happen with the innovations listed above, but a solid foundation based on cloud-native platforms is essential.