Data analytics and search

The systematic application of data to business management had its first flowering a century ago, with the birth of disciplines such as “scientific management” and market research.

Yet an explosion of the amount of information available online – and a similar rapid increase in the data that could theoretically be captured and analysed for business use – has ushered in a new golden age in the twin ­disciplines of information retrieval and data analysis.

Making sense of all the digital information has already produced one of the business success stories of the age in the form of Google. By helping internet users find the needle in the haystack, search has also proved a boon for other businesses, making it easier for them to be discovered amid the online crush. Search advertising, barely invented a decade ago, now accounts for nearly a half of all online ad spending as a result.

Meanwhile, thanks largely to the plummeting costs of collecting and storing information, the volume of raw data being produced has increased exponentially. About 90 per cent of the data currently in existence was created in just the past two years, according to IBM.

That has made “big data” the latest buzzword in the information technology world, as businesses look for new insights about their customers and markets. Data scientists – business analysts with the skills to extract meaning from the deluge of information – have become highly sought-after as a result.

The application of big data analysis to business is starting to bring a more scientific dimension to many aspects of decision-making as the suppositions on which managers were once forced to rely are being put to the test.

At the same time, the ability to draw on data from areas that once seemed unrelated is starting to yield valuable business intelligence. How people behave on social media sites, for instance, is now being studied for clues about their creditworthiness by some online lenders.

The latest advances in data ­analytics involve the application of real-time analysis to very large bodies of ­information, turning business decision-making into a far more dynamic process. In some markets, for instance, pricing is rapidly becoming a moment-to-moment decision – with algorithms rather than human beings making the split-second decisions about when to raise or lower prices in response to demand and other factors.

Richard Waters