How to enable information efficiency on the go with the use of data science teams

Having Started setting-up regular chats with various business units to understand their current process and where we could add value, these provided a marketing opportunity for our small and nimble team with a cross-functional capability.
Machine Learning is not required for every problem; some problems require rudimentary analytics to yield the desired output, while others require a combination of both. We soon realised that we required a team project guideline document to better understand what stakeholders required. This document essentially determines whether the project is more of a business intelligence problem, a robotic process automation task, or a systems integration task. It is also important to make it clear that there is often a trade-off in accuracy depending on the type of approach you implement. Machine learning and data science are often used quite loosely in a business context, with the misconception that employing these may magically solve complex problems. This guideline document aligned expectations around our capabilities.

How we were able to navigate our way through, were through some lessons learnt as stated below:

Change management or enrolling the whole organisation

The reasons behind building the data science capability may differ from company to company. Reasons could include a strategic intent, experimentation, and innovation. Some companies provide a data science service or product as the core of their business offering. Our team is different in the sense that the business can operate without us. What we provide is supplementary to the rest of the business, providing with more freedom to experiment and innovate where possible. In our formative years, our team had regular check-ins with a steering committee, these sessions allowed the senior leadership from various departments to share ideas and help us see the bigger picture. The output of these meetings comprised a short- and long-term roadmap, thus ensuring there is a shared vision for the projects we will work on.

Educate and spread ideas

Every week our department hosts Tech Thursday. A great platform to share ideas with the wider department and potentially interested stakeholders, as we also learn alot from the business. The format is quite informal, which is valuable as presentations or formal meetings: an hour-long slot, anyone from Allan Gray can attend and any tech-related topics are welcomed. As a team, we regularly give updates at this event intending to introduce general data science-related topics and an overview of what we have been working on for the past couple of months. While presenting other potential stakeholders often get an aha moment and join the dots with how our teams could collaborate. A lot of our best ideas came from spontaneous chats in the canteen or while having a coffee.Being non reliant on formal agenda or a business plan, arguably allows for more creative thinking without the constraints of meeting a business requirement.

Think long term

It is important to develop solutions that may be improved incrementally, with a focus on feature engineering and enhancement. The reuse of models is important to ensure quick turn-around time in the future. It is thus important to keep functions as generic as possible with sufficient documentation to streamline the process of jumping between various projects. Some data science objectives may also seem unattainable in the short term; there is value in recognising this. Currently, there may not be sufficient or the right kind of data for a project, but plan and ensure that you do not waste an opportunity now that may be valuable in the future.

Make execution simple

Our team places immense value on augmentation. We acknowledge that automation could improve efficiency and reduce costs. However, keeping a human in the loop and focusing on augmentation has its benefits. There are various tasks a human simply does better than a machine. Providing augmented insights may free up time in a consultant™s day to focus on more valuable tasks and reduce costs in the long run. As a result, new human-defined insights may even be derived from the data.

A problem is as complicated as you make it. We try not to reinvent the wheel. Instead, we use as many open-source packages as possible. We chose Python as our main programming language, mainly due to the many open-source machine learning libraries available. This allows us to focus more time and energy on understanding the problem, feature engineering, and system integration if the project makes it into production.

Executive key takeaways

What does this mean for your business?

• Understanding the type of approach for your business is essential, as highlighted above, the teams have an understanding of ways to communicate implementation within the organisation that allows for you in the business to have accurate information flow.
• How the teams enable the room to do so is through allowing for change management or enrolling whole organisation by understanding data science capability- which in an essence highlights strategic intent of the business, the bigger picture been shown.
• Allowing for education and the spreading of ideas through the organisation, in this potential work collaborations being identified through understanding what is required and the contribution of each team.
• Thinking long term allowing for not only a vision to be in place but a strategic output as to execution, in that allowing for simplicity to be highlighted within execution phase .

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