BUSINESSES are in the middle of an information explosion. In 2012, IBM estimated that 90 per cent of the data in existence had been created in just the two prior years. The International Data Centre, meanwhile, forecasts that the market for big data will reach $16.1bn (£9.57bn) this year, growing six times faster than the wider IT market.
But while the very largest companies are racing ahead with the use of data analytics to boost sales (with the likes of Amazon and Netflix using advanced recommendation algorithms to push products to the most receptive customers), small and medium-sized enterprises (SMEs) are lagging behind. A report by analytics software company SAS and e-Skills last year estimated the adoption rate among SMEs at less than 0.2 per cent, compared to 25 per cent for businesses with over 1,000 employees.
The difficulty of obtaining large, useful data sets is one obstacle, as is the evident lack of in-house technical expertise (57 per cent of recruiters surveyed by e-Skills said they had difficulty finding people with the required skills and experience). But increasing numbers of smaller businesses are finding solutions to such challenges, and incorporating big data strategies into their business models to help drive growth.
SIZE DOESN’T MATTER
Many of the model cases of data use take place on an extremely large scale – Google’s use of search terms to track the spread of influenza in the US is a case in point. But a growing number of SMEs are making use of statistical analysis on a far smaller scale, argues Peter Simons, technical specialist at the Chartered Institute for Management Accountants.
The requisite information already resides in many businesses. What time of day do you tend to make the most sales? Do you have a dip in certain seasons? The use of electronic payments systems, and the falling cost of data storage in the cloud (see below), means many SMEs can process such queries, and adjust operationally to act on the conclusions. And for tech-heavy organisations, services from companies like Splunk can analyse the mass of performance data produced by machines, helping to identify and resolve IT and security issues before they arise. The nascent field of wearable technology, meanwhile, is expected to add to the deluge of data available to businesses, and professor Andy Neely of the Cambridge Service Alliance at Cambridge University has argued that SMEs in particular will need to address how they can use the information from these new sources to enhance their products and services.
In other cases, external data sets can be a necessity. Sweaty Betty, the retailer specialising in active wear for women, used Experian’s “mosaic” demographic data to work out the best customer segments to target, and then used this information to determine where it should be expanding geographically.
But the services of analytics companies don’t come cheap, and the growing availability of less expensive solutions is making data analysis accessible to even smaller firms. Google Analytics is widely-used by web-based SMEs to analyse internet traffic. And services like Kaggle can go some way to solving the problem of not having data scientists stationed in-house. The website allows companies to post challenges, with freelance data scientists competing to deliver the project at the lowest price. Jetpac, a company with an app that turns holiday photos into a travel magazine for friends, ran a competition to design an algorithm that could identify the photos that users were likely to prefer. With just a $5,000 prize, it attracted 212 teams. According to Kaggle, all of the top ten teams achieved at least 85 per cent accuracy by the end of the contest.