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Using “Perishable” Information in Big Data Sets

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How can your enterprise secure value from the increasing amounts of data that is being collected through an ever increasing numbers of sensors, stored, analysed and interrogated by ever more sophisticated systems? My strong sense is to stick to foundation principals and start by asking how you can improve your understanding of what your customers are doing and are about to do. For example, if you have data that a customer has bought a train (railroad) ticket for a journey this afternoon from city x to city z you might offer him / her complementary products that would help address his / her needs on their journey. For example a voucher for a coffee from an outlet at the station that they do not usually use and to give them something new to read on the journey -perhaps a link to a application such as Zinio with an invitation to read a topical publication (I have just started reading the McKinsey Quarterly on my iPad through Zinio). The customer may then see on Zinio another magazine they like and decide to buy it!

Behind the above scenario is an essential requirement to get through the process of collecting, analysing and turning the data into actionable information quickly. In this context it is very perishable! In the last week, amongst the mass of writing about “Big Data” two items have particularly resonated with me. Firstly, John Rymer of Forester who, in addition to commenting on the awful “Big Data” name highlighted the trend towards a focus on dynamic data (needed for the above) and some of the technical implications. Secondly, Chris Taylor focused on the key question on how does Big Data add value in his article “Big Data must not be an Elephant Riding a Bicycle”. In addition to providing a number of steps to avoiding being “crushed by the elephant riding the bicycle” he flags “Refining business requirements as the top business challenge, not skills or tools”, “User friendly analytics” and “adequate infrastructure”.

We would be delighted to help your Enterprise develop a perspective on how you could improve your use of data to better understand your customers and their requirements.

 

One Comment

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