Big data is not just for big business

Taking the retail sector as an example Dr Despotakis analysed how businesses of different sizes typically approach data capture, storage, interrogation and application. He started with the uncontroversial statement that

“large supermarkets have lots of data and lots of tools to exploit value from that data while smaller retail chains have less data and fewer tools.”

But are we wrong to assume that our local family-owned corner shop lacks data? Dr Despotakis argued that such businesses do have data, but that they typically don’t exploit it, don’t combine data from different sources, don’t store it in structured ways that makes interrogation possible and too often management is done “on instinct”.

Businesses of all sizes, therefore, possess data. What separates the whales from the minnows is the quality of analysis. And it’s this gap, the failure of smaller businesses to exploit the value in their data, which is going to steadily weaken their competitive power relative to the big boys.

Smaller businesses – not just the corner shop, but your typical SME with less than 200 employees –  face difficulties undoubtedly: they are unlikely to possess the mathematical and statistical skills in house required for data analysis; they may have grown on the basis of ‘tacit knowledge’ (often described internally as ‘gut feel’ or ‘intuition’), which is still impossible to combine with other data sources in a scientific way; they certainly don’t have the budgets or resource of their larger competitors. Most tellingly, the benefits of Big Data: a full and complete understanding of a particular issue such as supply chain management or customer journey, come from the combination and correlation of large volumes of data, which a smaller business just does not possess.

Smaller businesses – not just the corner shop, but your typical SME with less than 200 employees – undoubtedly face difficulties. They are unlikely to possess the mathematical and statistical skills in house required for data analysis and they may have grown on the basis of ‘tacit knowledge’ (often described internally as ‘gut feel’ or ‘intuition’) which is impossible to combine with other data sources in a scientific way. They certainly do not have the budgets or resource of their larger competitors and most tellingly, the benefits of Big Data – a full and complete understanding of a particular issue such as supply chain management or customer journey – comes from the combination and correlation of large volumes of data, which a smaller business just does not possess.

It may seem inevitable then, that SMEs are excluded from the Big Data party. It may even seem a wise decision on their part to avoid grappling with tools and methodologies designed for much larger enterprises. But this is until we appreciate the democratistion that has been brought about by – ironically – the volume pricing strategies of some of the world’s largest software companies. Platform and application providers such as AWS, SAS and Splunk have provided structures and tools that offer greater computing capacity and scalability at a much, much lower entry point than the market has historically seen. Dr Despotakis in fact argued that the usability of such applications essentially allows SMEs to acquire many of the skills of a data scientist, simply through a software subscription.

As the talk was opened up to questions from the floor, business leaders and data analysts from across the Leeds area pondered why SMEs had failed to capitalise on the utility and offerings available to them, and what could be done to increase take-up of Big Data analytics.

Some attendees argued for professional associations to act as data warehouses, enabling members to share their data, access data from similar businesses, and so build the large volumes of data needed for true actionable insight. Others pointed to the value in publicly available data, accessible through organisations such as the Open Data Institute.

All agreed that Dr Despotakis’ key point – that small businesses stand more to gain from data analytics than large businesses – had been made. The percentage points gains for these businesses are typically greater, simply because so little analysis has previously been done, and there has been so little application of analysis to business strategy and operational practice.

Dr Despotakis concluded with the statement that SMEs need to be more predictive, and the ability to do this rests on data analysis.

“Being reactive is a luxury, if you want to survive.”