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Big Data Might be Mainstream, But That Doesn't Mean It's Being Used Well

11/06/2014 12:12 BST | Updated 10/08/2014 10:59 BST

'Big data' is no longer an unfamiliar concept. In recent years, the conversation about big data - what it is, and how it could be used - has become louder and louder, and today both consumers and businesses are familiar with the term. Data in everyday life has reached the mainstream: whether it's the basis for a news story in the media, or fuelling a loyalty card scheme that gives consumers tailored offers and promotions in the shops they regularly visit; these days, everyone is aware of the power of data. But how are business executives using the increasing quantity of data to improve decision-making?

One of the most important roles data now plays is as the basis for business decisions. When deciding whether to remodel a store, offer a new product, increase marketing investments, or change store hours, executives no longer have to guess if the their customers will respond positively to the change. Businesses can use data to accurately understand how customers will respond.

Despite the potential to use data to improve business decisions, many executives still rely on intuition to make decisions. We recently commissioned a study, authored by the Economist Intelligence Unit (EIU), to understand the extent to which executives are using data to make decisions. Decisive action: how companies make decisions and how they could do it better was informed by a survey of 174 senior managers and executives from organisations around the world, as well as interviews with industry experts, and shines a light on the decision-making habits and processes of business people today.

The study exposed a gap between business executives' understanding and beliefs about the importance of data, and the extent to which they actually employ - or are able to employ - data analytics in the decision-making process today. 59% said that their approach to decision-making was 'data-driven' or 'empirical,' meaning that decisions are informed by real world tests. But when asked what they would do if the data available to inform a decision contradicted their 'gut feel', just 10% said they would take the course of action suggested by the data: 57% of those surveyed would re-analyse the data, and 30% would collect more data.

This study shows that, despite understanding the importance of data, decision-makers aren't ready to fully trust their data, and they are falling back on 'gut feel' and intuition - as they would have done in the days pre-data. This could indicate a lack of confidence in the analytics tools they have at their disposal: when asked what they believed would improve decision-making within their organisations, 53% said 'better ability to analyse data' and '22% cited 'running more trials and tests before making a decision'.

Despite huge advances in companies' ability to collect and analyze data, real-world experiments remain the only way to isolate cause-and-effect relationships so that executives can confidently answer: "if I take this action, what will the result be?" This process, called "Test & Learn" involves trying an idea with some customers, stores, employees, or markets before taking the risk of rolling out a failed program across the network.

Though Test & Learn is the most advanced and accurate form of data analytics, it is not a distinct idea from intuition. Individuals' intuitions are the basis for their ideas - or hypotheses - which then can be validated and improved by trying that idea in the real world and measuring its impact.

It's not surprising to see that in the study, the EIU found that 45% of those who said their company was growing faster than the competition also said they could predict decision outcomes by running tests and trials - among those who are not growing faster than their peers, that figure is just 10%. In a world where big data is mainstream, simply having data isn't enough - it's what you do with it that counts. The organisations that succeed will be those that innovatively and effectively use data to inform decisions, not simply store the greatest quantity.