Data driven innovation is one of those technical terms that’s thrown around a lot in innovation circles. In our experience here at Idea Drop, however, few people have a clear idea of what it actually means, often misrepresenting it to others who further confuse the issue.
That’s because it can be hard to truly understand this crucial innovation style without having clear examples to hand. In order to keep a competitive edge, companies often like to keep their innovation and product development strategies a secret, making it difficult for newcomers to learn how these processes function in a real working environment.
To remedy this, today’s article will feature three examples, two of which are examples of true data driven innovation and one of which is not. As we go through the examples, try to guess which ones reflect true innovation and which one falls short.
First, however, let’s take a look at a quick definition of data driven innovation.
What is data driven innovation?
Put simply, DDI is an innovative project born trends or correlations in data. That project could be a new system, process or product that hasn’t been invented yet, but will go on to fill a measurable need or problem.
Sounds simple, right? Well, it’s more complex than it seems. There are thousands of innovative products that use big data to function, with many more processes and systems optimised through big data. However, there’s a subtle but important distinction between platforms that use data and platforms born from observations within a data set.
One of the following three examples is a classic example of data-driven optimisation, but not of innovation. See if you can figure out which one it is.
1. House of Cards by Netflix
House of Cards was one of Netflix’s flagship programmes. In 2013, 86 per cent of their subscribers claimed they were less likely to cancel their subscription because of this one show alone. However, the truly fascinating fact is that Netflix knew the show would be a hit before it aired. How? Data.
By analysing their data sets carefully, Netflix noticed that there was a correlation between fans of the original British House of Cards TV show and fans of both Kevin Spacey and director David Fincher. Netflix brought together these three elements in one show and, voila, instant cult classic.
2. Nappies and beer, Walmart
Beer and baby nappies aren’t two things that you’d usually associate with each other. However, these two products have become infamous in data science circles because of their unique relationship.
In 1992, Karen Heath – an analyst at Teradata – discovered that men visiting Walmart were extremely likely to buy beer whenever they stopped in to buy babies nappies. By placing the two items near to each other in the outlet, she was able to increase sales of both items by a significant margin.
3. Quickbooks, Intuit
Intuit are the outfit behind Quicken – a leading personal finance management tool. Back in the late nineties, analysts at Quicken wanted to learn more about how customers were using their platform. During an analysis of geographical data, the team found, to their surprise, that over 50% of users were using the platform in their place of work.
Initially believing that customers were spending company time on their personal finances, they scheduled some interviews with users. It turned out that the Quicken platform was so useful that many users extended the functionality of the platform to run their business accounting systems. Sensing an opportunity, the team handed the data over to the product development team and Quickbooks was born.
So, which out of the three examples above tells a story of data driven innovation?
That’s right! It was examples one and three.
The reason is that these two are examples of new platforms, products or systems that were created because of observable data correlations. The nappies and beer story is a perfect example of data driven optimisation of an existing system, but there is no true innovation happening there.
These examples illustrate an interesting dichotomy between innovation and optimisation. Whilst the latter is certainly essential for your business, it doesn’t contain the same potential for dramatic organisational change that true innovation does. If innovation is what you’re shooting for, it’s prudent to plan for some fairly dramatic changes within the landscape and culture of your business.
The next time you’re trying to explain what data driven innovation truly means, you can turn to these two examples.
To keep learning about innovation and its various types, read ‘Different Types Of Innovation: Why One Size Doesn’t Fit All’.