'Big Data Revolution' studies the effect of number-crunching on business

'Big Data Revolution' IDs some notable business trends, but the revolution's cost is not fully quantified

What will be the effect of vast quantities of usable data on business? That is the overarching question that the authors of "Big Data Revolution" seek to answer.

They start by outlining nine industry-specific examples. These draw on familiar themes: insurance companies able to tailor their policies to individuals; intelligent machines servicing themselves; farmers using data to enhance the yield of crops.

Written by an expert in financial risk and a vice president at IBM, the book harnesses a lot of data of its own. The authors identify 30 data factors, which are then "further deconstructed" into 54 specific big-data patterns.

Large chunks of the book, published by Wiley, divide a specific theme or issue into sections. The history of retail — via a McKinsey study — is split into five distinct stages.

This approach leaves some of the book open to charges of oversimplification, although the authors clearly have to balance their expertise with a need to present information clearly.

Interlaced between lists, the book identifies some notable business trends. In particular, the need to reform information technology departments, too frequently seen as ancillary rather than integral, is a priority across a range of industries — especially large banks.

Despite all the figures, though, the revolution is not entirely quantified after all. The material costs to businesses implied by installing data infrastructure, outsourcing data management to other companies, or storing data, are rarely enumerated.

Given the variety of industries the authors tackle, this is understandable. But it seems the cost of the revolution (something big data itself might be inclined to predict) remains unknown.

The book is perhaps most interesting as a case study of the philosophical assumptions that underpin the growing obsession with data.

Leaders of the revolution will have "the ability to suspend disbelief of what is possible, and to create their own definition of possible," the authors write.

Their prose draws heavily on similar invocations of technological idealism, with the use of words such as enlightenment, democratize, knowledge-based society and inspire.

Part of their idea of progress implies a need to shift from opinion to fact. "Modern medicine is being governed by human judgment (opinion and bias), instead of data-based science," state the authors.

When it comes to the thorny question of empirical objectivity, though, complications remain. Often, the problem of judgment is merely displaced further along the "value chain," into the hands of those managing and sorting data.

There are also complications surrounding the use of language, especially the word data itself.

The fluidity of the word — variously used to describe an asset, a commodity, the new intellectual property and at times merely a number — is such that the reader has the impression that almost any narrative or trend involving any figure at all could be subsumed into the exponentially growing list of big-data case studies.

Hale is a Madrid-based reporter for the Financial Times of London, in which this review first appeared.

Copyright © 2016, Los Angeles Times
74°