For several years now, data has been a master key unlocking many doors in marketing. The days are long past when large data volumes were the big thing, a more considered approach now holds sway. It is not about Big Data anymore but about Deep Data, Smart Data and, importantly, more and more, about Human Data. However, as practice shows, organizations in Poland and many Central and Eastern European countries are still a long way from a data-driven culture.
In a survey conducted in November 2018 by Data Tribe for OVH and Intel, only 60% of companies collecting digital data said they use it for business analysis. This is almost two thirds, a pretty high figure one might say, but it should be kept in mind we live in an era when ‘data is the new oil’ to quote the 2006 words of Clive Humby, a mathematician and creator of the Tesco loyalty card.
Over the past 13 years, however, Humby’s argument has become truncated in the popular imagination. In fact, it holds that data, like oil, should be properly located and processed in order for its full potential to be unleashed. This aspect has yet to be grasped by managers lacking a process-based vision and patience to carry out complex analytical projects requiring labor-intensive testing. This seems to be one of the reasons for Gartner’s last year’s prediction that 60% of Big Data projects now underway would ultimately fail. Despite so much buzz around the topic, research indicates that adoption rates for Big Data solutions at Polish companies are still low. On top of that, only 2% of unconvinced companies declare they want to use Big Data in 2019.
Slow as it is, digitalization will inexorably bolster the use of data also beyond commerce, telecommunications, and media where many well-considered solutions are already in place. The possibility of learning more about customers, their behavior and motivation are too valuable to be ignored. Even small email campaigns are backed by analysis. The availability of data sources, its volumes and, importantly, the description of actual responses and buying choices, which avoids the problems associated with declared intent, all provide a case for relying on data. The crucial thing, however, is what data it is and how it is collected and processed.
Personally, I am an advocate of informed Big Data, where the benefits of the analysis are known and the human perspective is taken into account at every stage of the project. After all, Big Data is not analyzed by people but by machines which are oblivious to the cultural context. What is more, computers can only be used to identify phenomena, but it is often a lack of them that is an important insight. There is also a growing debate on whether algorithms, which are human-made, are burdened with human errors, or even prejudices. Ethnographer Tricia Wang is known for her criticism of the tendency to process large volumes of data. In her excellent TEDx Cambridge talk, she describes Big Data as a ‘new oracle’ playing the same role as the well-known ancient ones. Investing in data processing systems means that employees rely on results based on an ‘old order’, and do not come up with breakthrough ideas, which stifles innovation. Innovation is also addressed by world-famous branding expert Martin Lindstrom, who in his best-selling book, Small Data: The Tiny Clues that Uncover Huge Trends, focuses on the power of qualitative observations. In his view, Small Data is behind 60‒65% of the greatest discoveries of our time. Big Data makes it possible to identify relationships, but it is Small Data that helps discover their causes and reasons.
The term ‘data-driven culture’ is used to refer, most of all, to the use of large volumes of data and complex DMP platforms. As Data Science develops, the focus is increasingly on the use of Smart Data, i.e. properly processed data sets. So if we see Big Data as crude oil, Smart Data would be a fuel ready to be pumped into your tank. If you do not handle it properly, it can cause an explosion, but if you do, you can go a long way.
To answer the titular question: No, data is not (always) right. It is the person using data that can be right or wrong. They are responsible for the right interpretation of data and putting it in the right context. It is them who supply organizations with ‘fuel’ so they become data-driven and get as much mileage out of it as possible.