The Accidental Data Scientist* by Amy Affelt is a clarion call to librarians and other information professionals to immerse themselves in the world of Big Data. As such, it is a solid introduction, emphasizing how the traditional skills of librarians are crucial in ensuring that Big Data are reliable, properly prepared, indexed, and abstracted, and intelligently interpreted.

Affelt reassuringly shows that the ‘problems’ of Big Data are not new, but very familiar to librarians, and indicates ways that librarians can add value to Big Data projects, by ensuring such projects deliver what is expected and anticipated. Data and Computer Scientists are good at writing algorithms to process data mathematically, but may not be trained in asking the right questions or knowing where to look for biases and flaws in data sets, and a Big Data project that fails in these aspects could prove an expensive disaster for an organization.

Chapters outlining the tools and techniques currently available for processing and visualizing Big Data, and applications and initiatives in various industry sectors are informative for those new to the issues, and a helpful guide for experienced librarians to demonstrate how their skills are transferable.

Affelt gives examples of specific projects and describes how the input of librarians – especially when ‘embedded’ in data project teams – is extremely beneficial. She suggests ways of proving the value of librarians in modern corporate settings and gives tips and suggestions on career development.

For information professionals unsure about how to engage with the opportunities Big Data offers, this is a wide-ranging and clear overview, and a great starting point.

With increasing media reports of algorithmic bias and amidst a deluge of fake news, it is more important than ever that Big Data projects include professionals with the skills to recognize and identify problematic sources and skewed datasets, and I hope that librarians and information professionals step up and hear Affelt’s call to action.

*Presumably named in the tradition of The Accidental Taxonomist by Heather Hedden.