The impact of big data on the value chain
On 12 May 2014 IMD organized a one-day open event in Lausanne that analyzed big data and its impact on value chains. Several experts with different backgrounds shared their views on the latest trends at the intersection of big data and value chains. 26 year‐old Pau Garcia-Milà, winner of the MIT “35 innovators under 35” award and founder of several innovative companies since he was 17 provided the general context. When did big data start being part of our life?
According to Garcia-Milà, innovation made a switch approximately five years ago. Up until then, the new technology developments were meant above all to be useful, offering more and more features that made computer programs increasingly complicated. But the birth of the iPad and other intuitive devices changed the pattern, shifting towards gadgets that were easy to use and that made the internet world accessible to the public. This, together with the implementation of the social media widely, resulted in a massive creation of data that needed to be processed.
Some leading‐edge websites started to analyze the information collected from their users, in order to create metrics and patterns that allowed companies to better know them and their behaviour. This learning process would be the first step in a cycle that has made it possible today for some websites to build personalized homepages for each different customer based on the information that they have, and in real‐time. It is based on the lean start-up methodology, applied to big data.
From an initial idea, a minimum viable product needs to be built very quickly so that the learning cycle can start. Measures and metrics validate the learning that is helping tune the product with the information obtained. This way the cycle is closed, and it starts again by adapting the product initially built to the learning previously obtained.
A good example of this practice would be Amazon.com. Users tend to think that the time required to load the page is due to the low speed of our network, but this is incorrect. It is actually due to the server’s response speed. The moment we decide to visit amazon.com and write the URL on our computers, their servers start comparing data to build a customized page with what they consider are our preferences. That process take some time, the time that takes on our side for the website to load.
Eric Koch, VP of Global Supply Chain and e-commerce at Ahold, offered practical examples of how companies are reacting to big data. Now that the organizations are able to store and manage increasing data points from customers, they are reshaping their business strategy. Business‐to‐consumer companies start by adding an e‐commerce shop to their traditional one. But the transparency that big data brings with it has an impact on the organizations’ actions. The creation of a new e‐commerce channel to interact with customers also implies a new information source for consumers, who can easily compare on the internet and choose their best-buy based on price instead of loyalty. Therefore companies need to make an extra effort to show consistency along their value chain, actions and communications if they want to keep their customers engaged.
Big data turns “consistency” into a must for every organization who wants to build a strong relationship with its consumers. They are ahead of the companies, and therefore the value chain will have to be redesigned around the customer journey.
True to life
IMD Professor Michael Wade reflected on the challenges that the use of big data will raise, from a different angle. Big data has been there for a while. It is only now that we are starting to learn what to do with it. And as we learn, we realize that a cultural change in attitude is needed at all levels. Using the experiment by Daniel Kahneman (author of “Thinking, Fast and Slow”) in which participants are prompted to choose if Steve is a farmer or a librarian based on a short description given by the author, Professor Wade illustrated to what extent human decisions are irrational, and more likely to be wrong than data‐driven decision.
Data‐driven decision‐making is considered to be artificial, unreal, incomplete, blind… whereas intuition is meaningful, deep, global and true to life. And yet, if we look at it from a probability perspective, automated decisions have a much higher chance to be right than human decisions.