Leading innovation in engineering teams
Innovation is difficult. To empower innovation and transform how teams work, you need the right mix of culture, technology, processes, and inspiration – and this takes time: in the typical case, it requires an ambitious, long-term transformation program.
In the context of software business, this program could face additional challenges: as engineers focus heavily on technical excellence – which is great – they frequently find themselves disconnected from the commercial world or the end-users of their products. To become truly innovative, engineering teams need to also capture the ‘big picture’ and fully understand the purpose and the commercial aspects of the business.
Novelty, innovation, and success could all mean different things and have various priorities in an engineering team; hence the need to adjust your innovation strategy, the communication style and tactics.
In the corporate world, innovation could be anything novel that creates value for its users and has potential for commercial success. Innovation could be incremental or breakthrough; it can happen at any time — following a ‘call to innovate’ like a hackathon or design sprint — or randomly, driven by original ideas, genuine talent and inspiration.
Innovative thinking typically leads to ideas and concepts regarding products or product features. In an engineering context, the outcome of innovation may also take other forms – from abstract technical concepts, patents, algorithms, data models to architecture patterns and functional prototypes. It can also trigger process improvements, for instance, novel ways and tools to speed up your overall development lifecycle and boost DevOps KPIs.
Engineers as innovation leaders
In many cases, software engineers innovate ‘silently’, inside their own microcosm — at a lower level (the actual design and implementation) or at a higher level (components and system’s architecture). But, engineers can play a more important role in leading innovation. In fact, they have a great advantage in driving innovation efforts: they are the ones with a deep understanding of technology and they are often passionate about it. This allows engineers to easily spot both the opportunities and the underlying constraints of technology. Moreover, engineers are expected to understand the principles of experimentation and be ready to take risks following a ‘fail fast, fail-safe’ approach.
Either as part of wider multidisciplinary team or as an independent software development unit, skillful engineers can thrive in leading real innovation, beyond the hype. What is needed is the right culture and steering – the key signals and a clear direction from the leadership.
How to empower innovation
Set the right context
The culture in engineering teams is special; engineers tend to have limited engagement with over-hyped business terms and marketing buzzwords — and innovation is one of them.
To change the perception, engineering leaders need to define innovation in the context of the company and with the right language for the team. The discussion on innovation must be supported by solid definitions, examples, programs and tools. Teams, need to relate to innovation success stories – and understand what innovation success means in the specific setup. Examples and references could come from the public domain, or ideally from the recent history of the company.
Establish the right culture
Engineers need to see themselves as innovators, creators, builders: they need to realize that they don’t just implement a feature, a component or a process in isolation; they are part of something bigger which typically drives significant financial, or other important corporate KPIs.
As an engineering leader, you need to encourage teams and fellow engineers to share their ideas and thoughts – not only in their domain of expertise but literally on anything in your business reality. And you need the mechanisms to capture these thoughts and enable effective discovery and collaboration scenarios.
Your engineers need to feel trusted, also in terms of time and focus; they need to have some autonomy on when and how to pursue their ideas. And this is where you need a strategy and a framework to ensure that you maintain the right balance between productivity and ‘dreaming’.
Having productivity baselines along with metrics on innovation activity, can help you monitor, understand and steer the innovation efforts. Depending on the current workload and velocity of the team, you can set the appropriate balance between productivity and innovation – for example, by deprioritizing innovation-related work/ideas or reducing the frequency of ideation sessions. Technology can provide great support here – intelligent ideation platforms can easily capture and automatically handle, ideas, thoughts, problem statements and even connect this innovation stream to your product backlog; with relevance and intelligent prioritization.
Invest in technology
Modern teams need an always-on channel for ideation and collaboration – which could also get connected to your engineering processes. Imagine if your product’s backlog entries were seamlessly matched with incoming ideas; or if ideas were automatically scored against backlog relevance; or having an intelligent process, grouping and summarising all known-issues, bugs, performance limitations, system constraints, negative user feedback etc. as well-defined, prioritized ‘problems to be solved’. Innovators from within your team or across the organization, could discover relevant-to-them content and respond with novel ideas on potential improvements and fixes; or innovative ways to solve the problems stated.
Enable rapid prototyping and experimentation
Failure is part of the overall innovation process – as soon as it is fast and safe, it provides important learning opportunities through insights and findings. Following practices like ‘rapid prototyping’ and ‘fail fast’, ideas can be quickly tested with real users, to support informed decisions, as early as possible and at minimum cost.
Rapid prototyping is also a key principle: in contrast to the ‘production mode’ where engineers need to follow certain processes, coding principles and protocols, ‘rapid mode’ allows engineers to use shortcuts, make assumptions, hard-code if necessary and use artificial data to quickly build functional instances of an idea. This rapid mode, focuses on building prototypes, that can be exposed to selected users and capture feedback towards possible next steps in the development process.
‘Rapid prototyping’ requires both ‘a different mode of operation’ for your developers, but also the right technology readiness. You need to maintain, reusable components, APIs, UI elements, standardized data sets and other assets, to enable quick assembly of realistic, functional prototypes, without wasting effort on functionality which is not directly relevant to the core idea – the one to be tested or validated.
Quantifying innovation is difficult. Measuring innovation activity is easier. You need to introduce a framework to capture events which are relevant to innovation – this could be ideation, contribution to ideas, prototyping efforts, patent filing etc. Having a rich, reliable history of such activity is fundamental in defining related baselines and reference points. Innovation activity metrics could then be linked to certain KPIs and analyzed against product and financial performance.
Qualitative assessment regarding the overall innovation performance within your team and the entire corporation, can also be very useful. As an additional set of innovation indicators, domain experts could assess the novelty and level of innovation in specific deliverables or product releases, against the corresponding state of the art.
All the above could be linked and presented in a solid Innovation Performance dashboard for both management purposes and for achieving a general awareness related to innovation performance.
Technology Innovation Leader, Inventor and Product Architect, with more than 20 years of experience in designing, engineering and managing data-driven software products. Holds more than 20 patents on Artificial Intelligence, Analytics and IoT; founder of 3 technology startups, including ‘Datamine decision support systems’.
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