
OUR THOUGHTSTechnology
Putting patterns to work
Posted by Gareth Evans . Nov 30.23
We’ve published a new case study that shows how we’ve used patterns to implement a flow-optimised ‘Delivery Ecosystem’ for a fintech client.
Patterns are critical to our work. They’re solutions to typical challenges we observe across our clients – challenges that are common to specific sectors, business domains and technology functions. More importantly, they’re also the improvement actions we need to take to solve these challenges.
Over the years, we’ve learnt how to combine patterns to work in balance in a delivery ecosystem. Patterns can be applied to improve technology, tooling, technical practices, team topologies, architecture, ways of working and specialist skills – all with the goal of accelerating the flow of value to your customers.
We know, however, that using patterns in a point solution won’t work if they prove suboptimal for the system as whole. Which is why, in almost all our engagements today, we act right across a value stream and use patterns which work synergistically to transform outcomes from concept to cash.
We’ve developed our thinking on this and have been putting it into practice for well over a year now. We call it a ‘Reference Ecosystem Implementation’.
Software Delivery Ecosystems comprise social networks that rely on skills, technology and processes to work in balance to accelerate the flow of value to customers through software products. Value flows through – and relies on – interconnections between human and technical elements.
We start by building a ‘reference’ ecosystem for a client based on a flow-optimised set of patterns encompassing architectural style, technologies, tools and practices. The ecosystem is also designed to ensure teams are able to innovate with fast feedback to create an optimal learning experience.
Over time, the ecosystem forms the basis for new teams and value streams to accelerate value to customers and help meet business objectives.
There’s no ‘templated’ solution here. Crucially, the first phase of any engagement is always experimentation. Experiments identified in the discovery process will be undertaken to gain confidence that proposed tools, patterns and practices will work together as expected to form the delivery ecosystem that works uniquely for the business – one that will be relevant, adaptable, long-lived and constantly evolving.
We prove the value of this work through positive changes in Flow Metrics and how those changes help meet business OKRs.
We’ve also found that as we help clients scale delivery ecosystems across other business domains and the teams that work in them, people engagement improves dramatically.
Our new case study has much more on our approach to implementing Delivery Ecosystems and the outcomes we’ve achieved for a fintech client. If you want to hear more about what we can achieve for you, don’t hesitate to drop me a line.
More Ideas
our thoughts
Mastering Model Context Protocol (MCP): how to give your AI Code Assistant tools to use
Posted by Davin Ryan . Jun 30.25
We are regularly reminded that large language models (LLMs) will revolutionise how we work, automate complex tasks and enhance productivity across industries.
> Readour thoughts
Platform engineering principles that actually work for teams
Posted by Reuben Dunn . Jun 24.25
Lately, I’ve been thinking about the concept of principles. I coach basketball based on principles rather than set plays and, like basketball, platform engineering represents complex adaptive systems.
> Readour thoughts
Questions leaders must ask during AI implementation
Posted by Martin Kearns . Jun 18.25
We are regularly reminded by technology experts that AI will transform business operations, reduce costs and create competitive advantages. We agree these opportunities exist, but this blog isn’t just another article promoting AI adoption.
> Readour thoughts
Evaluating strategic AI bets
Posted by Gareth Evans . Jun 12.25
Organisations are increasingly making strategic product bets on Artificial Intelligence, representing both tremendous opportunity and significant risk.
> Readour thoughts
Do YAGNI, KISS and DRY always provide better engineering and product development?
Posted by Davin Ryan . May 19.25
We are regularly reminded by content creators and experts that YAGNI, KISS and DRY reduce ‘dumb work’, are ‘need to know’ and ‘key software design principles’ to get the best out of your software engineering teams leading to better business outcomes.
> Read