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Big Data In Practice: How 45 successful companies used big data analytics to deliver extraordinary results


When offered the opportunity to review a book from a few available options for The Actuary magazine, I settled upon this, and it has proved to be an interesting and informative read, covering the topical and fast-moving area of big data analytics 

It presents case studies of diverse companies embracing the use of big data analytics, leading to gaining valuable insights that are used to enhance their product and service offerings, with clear benefits for both customers and the companies. Some examples of the benefits of big data mentioned in the book include the provision of more personalised service, performance optimisation, safety enhancement, crime identification, and natural disaster prediction.

Marr introduces big data as “a movement that will completely transform any part of business and society”. He makes the bold statement that: “I am convinced that big data, unlike any other trend at the moment, will affect everyone, and everything we do.”

The case studies are well laid out in the form of narratives of the big data projects undertaken within the studied companies, and Marr has successfully managed to write each case study in a succinct and engaging manner. For each company, he provides background information, outlines the problem that big data helps to solve, explains how big data is used in practice, describes the results of the project, and outlines the data used. In addition, technical details of the platforms and systems used are provided, along with comments on challenges that had to be overcome. Each case study ends with the key learning points and takeaways, and references for further reading.

Rather disappointingly, none of the case studies were of companies (such as insurers) that make extensive use of actuarial analysis and are early collectors and users of large datasets. This absence is not a reflection of the lack of diversity in Marr’s selected studies, but it perhaps demonstrates that data analytics is more passionately publicised elsewhere and has progressed so far and fast such that, regrettably, some of the earliest data-centric industries and professions like ours were not considered or did not make the cut for a case study.

Could this be a reflection of actuaries not being as visible and vocal as we could be in the data science space, which is acutely closely connected with actuarial science?

As has been discussed before in this magazine, it may be that other professions market their skill sets better, and there may be a case for us to raise our profile and image. After all, many actuaries and non-actuaries would say our work is pioneering at the heart of the field of data science. One wonders whether a different compilation on the same theme of the use of big data analytics would acknowledge actuarial work and actuaries’ prolonged penchant for data.

Marr points out that improvements in technology and techniques mean that there is now an increased ability to capture, store and analyse data. Some of the companies he writes about are taking big data very seriously and investing heavily in this area, with machine-learning platforms and large teams of data scientists dedicated to providing analysis that can be translated to meaningful actions, some of which are deployed in real-time.

Marr talks about the key data issues connected with privacy and the need to gain customers’ trust, and his narrative highlights companies acknowledging data protection; however, privacy is described as “a murky area in the big data world” and not all companies are transparent about what data they hold.

While there is some mention of the danger of placing too much blind faith in data itself, I feel that this point is mentioned too infrequently and does not get the emphasis it deserves. What determines the usefulness of data is not the raw data itself, but rather how it is used and analysed, and the subsequent decisions made. Given that this book is not an academic text, the author has focused on success stories and has not needed to balance these with a set of case studies of failed uses of big data, which would make for an interesting read.

Finally, big data analytics necessitates clear communication. It is valuable to be able to articulate technical work to adequately

convey the analysis carried out, the insights gained from it, and make recommendations, while also expressing the limitations and judgements applicable to the work.

As an actuary working in the actuarial documentation arena, I am very pleased to note that Marr often highlights the importance of communication. He emphasises that “pure number-crunching talent is not always enough… communication ability is also a vital skill”.

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