Nav Dhuti, Data Engineer, talks about some key lessons – from start-ups and product development best practices – that helped Carbon to shape and develop its bespoke data intelligence platform, Graphene.
One can equip an army with enough weapons to have a great chance of winning a war but – as Sun Tzu described in the Art of War – without a plan of attack, things can flip on their head in a very short space of time.
When given the right tools, resources and support to turn an idea into reality, execution plays an important role. In the era of cloud computing where we have enough technological capacity to solve many of the known/unknown business problems, using the right blend of tools to target a realistic use-case is key.
While deciding on how to build the foundational pillars for our in-house technology at Carbon, we spent a great deal of time researching and taking lessons from the variety of experiences everyone brought to the table – it was a true collaboration across the team. Perfection was never an option – the goal was always to find the optimal set of resources and to give us enough space for natural evolution.
Value Over Effort
“Start with the customer and then work backwards” – Jeff Bezos
Sometimes it is good to take a pause and think about whether we need to code something or not. Over the last six months, Graphene has grown and matured into a data analytics suite used in every facet of the business – enabling deep, insightful data-driven decisions. The journey continues to be led by users allowing us to launch a product in a very short period of time without leaving large amounts of technical debt or legacy behind.
Using the Agile methodology, we were able to calculate Value over Effort metrics for each suggested feature. We all focused on maximising the value to the user. At times when we had features that did not prove to be of immediate value, these were not removed from the list, rather placed into a backlog to be released and re-prioritised at a later date.
Make it work
“Make it work, make it right and make it fast” – Kent Beck
Valuable ideas need to turn into reality as well. We were able to do so by starting with filtering down to the most basic version of the task and building gradually with proactive feedback from users.
An example is IQ Search. It is a simple search bar in Graphene that allows users to search for data insights using natural language as you do in a Google search. We broke this down to a very basic level by providing users with a fixed set of questions in a dropdown list with predefined answers.
A “minimum viable product” as defined in The Lean Startup by Eric Ries is what we aimed for. As the users engaged with the product, we built the real intelligence behind the scenes, taking active user feedback into account. As a result, we have an AI-based insight search engine that is influenced by the users of Graphene. Also, the development is never finished – there is always room for continuous improvement.
We followed the mantra “start small, iterate fast” to build a user-centric platform. From intelligent dashboards to RAPID Insights, where users get proactive insights with a touch of hyper personalisation, the innovation has been a gradual journey.
Build an Ecosystem
“At the end of the day, your job isn’t to get the requirements right – your job is to change the world.” – Jeff Paton
Quick tweaks, feature rollouts and bug fixes are easy when you are a small team with all the access you require. When it comes to creating reliable products, any cut corners quickly become apparent with potentially large ramifications. A more sensible approach has to be formulated which serves as a solid foundation for future development without adding any friction on the route to innovation.
While developing all the shiny features of Graphene, we focused on their long-term reliability. From code structure to alignment of business offerings via Graphene, we made sure everything is seen as part of one – in other words, a common ecosystem.
A good example is our RAPID Insights framework. Initially, it was simply an API endpoint to provide some basic predictions to the user. As we got into platform build mode, we focused more on developing an insights development framework within Graphene. It has now matured into a fully functional framework where super users of Graphene can create personalised insights with a few clicks and allow it to communicate with other micro-services within Graphene’s ecosystem.
At the end of the day our ecosystem enables us to adhere to and implement the excellent advice offered by Dave McClure –“Customers don’t care about your solution. They care about their problems.”
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