Finally the hype of big data has come to a stop and has transformed itself into real business. A lot of people are still talking about it, but more and more are really applying it, sometimes in a big way, sometimes in small pilots.
In the Netherlands, Peugeot even uses the concept in a humorous way to promote their cars. They start by saying that they didn’t do research anymore to build their cars, but used big data and social media to gather all the requirements and then turned it into new cars. The car manufacturer even invites a lady to no longer look at the Peugeot 106; they checked who was looking at her LinkedIn profile; based on which she can expect an offer with a better salary soon, & should therefore check the larger Peugeot 306.
This example leverages on the real time data insights, which our recent thought leadership study also supports; 77% of decision makers increasingly require data in real time (detailed information at )
A lot of the debate is on how to work on the Big Data play. How to set-up a team of data scientists, where to start (focus on marketing or use it to improve operations) and of course leveraging which tool sets (on premise or cloud).
These are all valid questions and need to be addressed in order to have real value from Big Data. But another interesting question is where the data is coming from, or even better yet, which data you would like to have.
Most of the big data projects I encounter focuses on using the Big Data tool sets to query on the data which is available for the company (not necessarily in the company) and what kind of analysis can be made from it. Again not a wrong approach, but it is working from the data, not from the processes.
We already have seen some companies who were working hard on their data analytics capabilities, but have got so much data in their data lake and so many opportunities for data crunching, that it basically killed the analytics engine. As Lewis Carrol stated, “If you don’t know where you are going, any road will take you there”.
If you take the process or a business design question as the starting point, then first you must look at which data you want to work on. This could be even data which you have not looked at before. Think about the latest press event of Apple. It was not the Apple Watch that interested me; it was the announcement of the Health Analytics apps that were announced.
Apple worked with large medical institutions on questions like “how can we easily detect and analyze the stadia of Parkinson”. By working on the question, it gave the possibility to think about how and where to gather the data from. The wearable technology of course, builds a foundation for this.
So rather than work on your current data and start a sequence of Big Data projects, I suggest taking the following approach:
1.     Define the “Big” questions you want to answer that can make a paradigm shift for your company or your organization
2.     From that question, analyze which data would give inputs to arrive at the answer
3.     Find the sources or media that can provide you the data, and take a broad perspective on this (from inside company data, to social and wearable data)
4.     Then select the data tool set that can best help you crunch the data and provide the answer
5.     Learn from the insights of this single journey and start over again defining new questions
Big Data is not about providing answers to questions we could not answer before; it is about our ability to think of the new questions.