We share our approach to data science projects, addressing topics such as alignment to business imperatives, project design, project delivery and evaluation of success. Here we have gathered, organised and expanded on those bits of advice to serve as a resource for anyone considering embarking on a data science journey. Much of the content in this guide is derived from lessons we have given to our students.
We wrote this book to share our experiences in hopes that it will help the reader – whether a data science practitioner or a business leader – reduce these risks and design projects that have the greatest chance of success. We have made many mistakes, and in the process we have learned what works well and where the common pitfalls lie. Your authors have designed and delivered hundreds of projects across a wide range of industries. Yet, these uncertainties and risks – for the business leader and the data scientist alike – can be controlled and managed if approached in a sensible manner.
For the data scientist the situation can be equally unnerving, with uncertainties about how to deliver a successful project when the path is not clear. Many business leaders are left feeling unsettled, balancing the need for innovation and the adoption of revolutionary technologies with an uncomfortable degree of uncertainty and risk of failure. Often these projects, while successful from a scientific standpoint, miss the mark in terms of business impact. Yet for these benefits to be realised, data science initiatives must be designed and executed in a sensible way. Data science, machine learning and artificial intelligence (AI) can have game-changing impacts for businesses, empowering them to increase operational efficiency, improve the quality of their services and understand their customers better.
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