Big-data analytics – where next?
Big-data analytics is the hot topic of 2012, with many predicting that the future success of organisations – big or small, public or private – will depend on their ability to capture, analyse and gain insights from it. The buzz stems from the fact that recent technological innovations have given us the ability to analyse extremely large and complex data sets that were previously beyond our capabilities. Examples of big data include the vast and ever-changing amounts of data generated in social networks, video footage, internet search and browser logs, as well as the data generated by the sensors and chips in our smartphones and tablets.
Companies that are able to effectively leverage big data will benefit from massive competitive advantages. While some companies, such as Facebook, Google, Amazon and Apple, are already able to leverage big data to inform their decision-making and gain competitive advantages, most businesses are miles away. In fact, most are still struggling with collecting, analysing and interpreting traditional data, let alone big data.
One UK-headquartered company that is able to leverage big data is Tesco. It invested greatly in its Clubcard loyalty card at the time when many of its rivals abandoned theirs arguing that collecting and analysing all the data would be madness.
Today, Tesco operates one of the most successful loyalty programmes ever created. With more than 14 million Clubcard holders, the programme allows stores to collect detailed transaction information on two-thirds of all the goods processed through their cash registers. However, for the scheme to remain useful, it was critical that Tesco was able to turn the data it collected into customer knowledge it could act on.
This knowledge includes a detailed understanding of ever-shifting customer behaviours and buying preferences. Tesco uses these insights not only to change its product offerings, but also to sell and share the information with suppliers and partners.
When Tesco launched the Clubcard it realised it didn’t have the capability to systematically analyse the mass of data gleaned from its customers. So it decided to outsource the data analysis to dunnhumby – a company that specialises in data analysis. Later, Tesco decided to buy a 53% stake in the company. Today, dunnhumby has more than 1,500 employees throughout the world and annual sales in excess of £150 million (about $240 million).
Recognising the value of analytics as an important driver of its success, Tesco has also developed in-house competencies, creating an internal team responsible for extracting insights from customer and performance data. Tim Mason (at right), Tesco’s chief marketing officer and deputy group CEO, explains: “These people are geographers, statisticians who had spent a lot of time applying those skills to understanding how customers would behave. They could crunch through the stuff that came from the Clubcard, see the patterns in it and then start to help the management understand what was going on, but also point towards what should be done about it. They had to find the data, and present it in a way that makes the decisions stark, and clear.”
But if you don’t happen to have the IT budget of Tesco to leverage big data, what is new and different about the technology available today is that any business, even those with very small budgets, can start to reap the benefits of big-data analytics. Take the example of a small start-up retailer with two shops and a website. This company realised that the CCTV footage that it recorded in its shops for crime prevention purposes could be used for much more exciting purposes.
The company bought inexpensive pattern recognition software that allowed them to turn their CCTV footage into useful customer analytics. The software takes the data from the cameras and provides an analysis of customer volume, conversion rates, and even an analysis of where in the shop people go and what they look at. The same company also uses free big-data analytics tools, such as Google Trends, to carry out market research and analyse vast amounts of trend data and “social mention”, a tool that allows it to track social media mentions of their company and of their brands – it even provides an analysis of the sentiment of the comments, such as whether they were positive or negative.
So what’s next for companies that don’t want to miss the big-data train? Here are some key steps to take:
Think big – think beyond the data you have in your databases and of the vast amounts of data that is generated every second – how could you use it to your benefit?
Create big-data competencies – big-data skills are fast becoming a rare and expensive commodity. The sooner you can start creating some of the big-data analytics in-house the better. This can be achieved through training your business analysts or simply by experimenting with some of the tools discussed above. Alternatively, you could partner with an external company to provide the big-data analytics.
Look for free and inexpensive technology solutions for big-data analytics – two key ones are software as a service (SaaS) – where analytics software applications can be accessed over the internet and “rented” for the time it is needed, and open-source software frameworks (such as Hadoop) that enable large volume data processing by distributing raw data across different systems.
The final step is really the first step: be sure you know what you are looking for and what strategic questions you need answers to before you start on any big-data analytics journey. Without a clear idea of your strategic aims and information needs, your big-data adventure will surely turn into a big-data disappointment.
Bernard Marr is a global business performance expert and author of the book The Intelligent Company – Five Steps to Success with Evidence-Based Management.