Our Lessons Learnt as a Data-Science Team It is widely quoted that to date over 80% of data science related projects generally do not make it past an experimental phase into production. Nevertheless, it is also reported that executives place continued value on these projects despite their low implementation rate. An obvious question to ask is why such a disparity between implied value and tangible results exist?
provide actionable insights. Our projects are therefore a mix between research and development. It took us a while to get to a state of congruence, some of our valuable lessons learnt are summarised below
Establish a sense of identity
As a new team in the business, it was important to take a step back and reflect on where we could add the most value. Being performance driven, we initially put a lot of pressure on ourselves to deliver something tangible that could be used by business. We started off almost entirely delivery focused, not assessing what distinguishes us from the other IT teams. Taking a step back made us realise that there is a lot of value in research-driven tasks. We started setting-up regular chats with various business units to understand what they were struggling with and where we could help. Soon word was out that there was a nimble team with a cross-functional capability. While establishing a sense of identity is important, we found that setting the right expectations is crucial. Machine Learning
31 SYNAPSE
OUR JOURNEY as a data science team has been no exception. Looking back over the past three years provides a trail of valuable lessons learnt to unlock true value as a data science team within an established corporate. Although we recognise that every data science team follows an invariably different journey, we believe there are some universal truths to ensure optimal value is delivered. To start off, it is important to provide some context for why and how the data science team at Allan Gray was formed. Allan Gray is an investment management company with the aim of creating long-term wealth for our clients, while placing immense value on client service. The data science team was formed to innovate in the retail space within the business. The team formally sits in the retail IT department, with a cross-functional capability into the rest of the business in areas such as: operations, distribution, product development, risk, and client experience. Our team essentially focuses on identifying areas in the business where leveraging a data science capability can for example optimise a process or
4TH QUARTER 2020
By the Allan Gray Data Science Team
