Our Lessons As Data Scientists: A Data Scientists World View (Part A)

Ronald du Toit
June 3, 2022

Our Lessons As Data Scientists: A Data Scientists World View (Part A)

A data scientists world view

Corporates are investing in data science initiatives but very few are seeing day-to-day applications in their attempts. There are different ways in which data scientists enable value creation through the implementation of data used within the business. 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.
Our journey in data science has been no exception. Looking back over the past four years provides a trail of valuable lessons learnt. Although we recognise that every data science team follows an invariably different journey, we believe there are some universal truths on how to ensure that optimal value is delivered.

Valuable lessons that have been learnt are unpacked into the following points below:

Know why you are starting a data science initiative

Allan Gray is an investment management company to create 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 formerly sits in the Product Development department, with a cross-functional capability into the rest of the business in areas such as operations, distribution, R&D, risk/compliance, client experience, and more recently investment alternate data for our offshore partner, Orbis. Our team essentially focuses on identifying areas in the business where leveraging a data science capability can, for example, optimise a process or provide actionable insights. Our projects are therefore a mix between research and development, perhaps more research for development.

Create clear expectation

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 for your business. We started almost entirely delivery-focused, not assessing what distinguishes us from the other IT teams (the team was initially housed in IT). Taking a step back made us realise that there is a lot of value in research-driven tasks.

That being said, Often a lot of emphasis is placed on quick wins; this is important to demonstrate the initial capability of a team, especially when it is a new domain. However, the timeline of various projects may differ drastically. It is therefore important to clearly define a definition of done as early as possible. Data science projects frequently go through many iterations, with no guarantee of success. We found that setting the right expectations is crucial. We use a proof of concept phase to test whether it is feasible to take a project any further. This enables us to determine unsuccesses quickly and not waste valuable resources trying to force a solution. This is also coupled with setting the right expectations upfront; there is no guarantee that a data science approach would yield the desired outcome.

Research problems

Research forms a big part of what we do: we approach problems against the grain and with the intent of extracting novel, unbiased insights from data. Various business units within Allan Gray approach us with a problem and we have realised that a sound approach is to reframe the problem into a research question using the scientific methodology. The first, and arguably, most valuable step is to formulate a hypothesis for the problem. This allows both the data science team and business team to place a yardstick, which acts as a reference point to measure the success or failure of a problem. Doing more research also exposes the team to valuable domain knowledge that could potentially turn into a long-term project in the future.

Executive Key Takeaways

What does this mean for your decision-making?

  • As highlighted above the data scientist teams are well rounded in the sense that understanding the teams’ capability allows maximum value creation for your business.
  • How they allow room to do so is by understanding the reason as to their sense of identity- through that being able to conduct research for your business which essentially contributes to development of your business as a whole by e.g giving accurate insights or processes in accordance to your business
  • Allowing for clear creation of expectations meaning allowing you to be delivery focused on how to maximize performance.
  • Through research problems being an important part as to accurately addressing the problems faced by the business this In an essence ensures effective delivery as efficiently as possible.

We still have a lot to learn as a team, but that is part of the fun!