Adding Value with Data Science  -  An Example for Non-Specialists

You may be wondering “What is this data science thing and how does it help my business?” You may have built a career in an established branch of IT. You may be a business professional with a solid grounding in what makes your business work. Many non-specialist articles and presentations have made general statements such as “AI will revolutionise industry [X]”. In other words, “all fluff”.

This article cuts through the hype and provides a concrete example of how data science can be used to add business value. No fluff.

We will walk through a hypothetical scenario that is likely to be profitable if implemented. The terms “AI”, “machine learning” and “data science” are used as synonyms.

Here’s our scenario: We run a sports betting business. Customers can place bets on our website, mobile app or in our physical store. If we can recommend the right sports and bet types, at the right time, to the right customer, then we can increase the number of bets that a customer places. More bets leads to more turnover and lower variance, which lead to higher profits. 

But why use machine learning? If each customer were to bring us a seven figure profit each year, then we can afford a relationship manager for each customer. No need for machine learning. However, each customer only brings in a few dollars of profit. We aim to spend a few cents per recommendation, per customer, to earn an additional dollar per customer. If we get this right, then we can spend a few cents to earn an extra doller per customer. Each customer would spend more money with us and our total profits would increase. So how do we do it?

An end-to-end solution

Our company already has the following components:

  • An existing system that takes bets.
  • Archives of past bets, past pages, past marketing contact activity and anything else.
  • A marketing team who need to track the ROI of this project
  • User facing front ends; desktop, mobile, in store.

Our solution will add the following components:

  • A micro service that will serve recommendations to the front ends.
  • A modification to the front to display the recommendations that they are instructed to display.
  • A machine learning model that decides which recommendations to serve and to whom.
  • Dashboards for reporting campaign performance and ROI.
  • An end-to-end pipeline for building machine learning models, deploying them and tracking their ROI versus control groups. These pipelines are sometimes referred to as MLOps.

How to integrate all of these components together? This is a massive topic that we will start exploring in the next article.

The Enterprise Data Science Architecture Conference will present real solutions that have been deployed in large companies. I invite you to reserve your place now because it is the best place to learn the emerging best practices.

Machine Learning in Production. The Enterprise Data Science Architecture Conference focuses on how to properly productionise data science solutions at scale. We have confirmed speakers from ANZ Bank, Coles Group, SEEK, ENGIE, Latitude Financial, Microsoft, AWS and Growing Data. The combination of presentations is intended to paint a complete picture of what it takes to productionise a profitable data science solution. As an industry, we are figuring out how to best build end-to-end machine learning solutions. As the field matures, knowledge of best practices in end-to-end machine learning pipelines will become essential skills. I invite you to view our list of confirmed speakers and talks at https://edsaconf.io because you must keep you skills current.

Meet the right people and up-skill. The conference will be on the 27th March at the Melbourne Marriott Hotel. A fully catered conference with coffee, lunch, morning/afternoon tea and evening drinks & canapes. I invite you to reserve your place at https://edsaconf.io this is the best place to learn the emerging best practices.

Slava Razbash

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