Innovation is Dead, Long Live Data. Part 3 — Innovation’s Unsung Hero
Read Part 1: What do we mean when we say ‘Innovation’
Read Part 2: Why do Innovation Programs fail?
Long live data!
In Part 1 we discussed some of the core innovation methodologies being used today. A common theme running through them is a reliance on data in order to measure impact, progress, adoption and efficiencies. In Part 2 we looked at why large scale “innovation enablement” programs struggle to achieve their aims.
This section is about taking a different approach to delivering innovation transformation and focussing less on the methodologies, enablement, accelerators etc. that are the features of most innovation programs and instead focus on improving data and analytics skills across an organisation.
Everything we do as individuals creates data, if you go out for a run and track it on your phone or other device you are creating data on pace, location, elevation and even heart rate. There is very little that we do from waking in the morning (usually to an alarm on a smart phone) through to turning out the lights in the evening that is not recorded, tracked and stored. This data presents an amazing opportunity if organisations have the ability (and the permission) to make use of it.
This access to data is changing the way we can and should drive innovation.
Data at the Heart of Innovation
Eric Ries’ second principle at the heart of his Lean Startup methodology is that “Entrepreneurship is Management”. Innovation requires entrepreneurship and entrepreneurs and so therefore it follows that innovation is management.
Too often innovation is thought of as “new ideas” or “creative thinking”. Whilst both of those ingredients form part of successful innovation they are insufficient without the rest of the process. As we often tell (and show) our clients, the idea is the easy bit. The difficult part is taking that idea, validating that there is a genuine problem to be solved through experiments and data collection and then identifying the best solution to that problem using further testing and analytics. In short data is the key to understanding customers.
Even when sourcing ideas for innovation, one of the best places to start is with our data. For incremental improvements data can show us where our customers are falling out of our checkout process or which demographics are currently underrepresented in our market share. It might even show us that placing beer near the diapers in a supermarket would increase sales [N.B. This might be apocryphal].
Data Driven Decision Making
Some of the most innovative companies who may be thought of as being creative risk takers are actually the ones that are most obsessive about using data to test ideas and make decisions thereby reducing business risk.
One of the best examples of a company that is obsessive about the use of data to make decisions is Netflix. Their approach to this transcends and expands throughout their entire business from strategic to creative to people’s decisions. It is fundamentally a data science project.
Whilst the program may be no more, the story of how Netflix commissioned the once-hit series House of Cards demonstrates this well. No pilot was ever made of the program. Instead viewer data was used to build the project from the ground up. The original BBC series of House of Cards had remained popular making the format a good subject for a remake. In simplistic terms Netflix looked at the users who liked the original series and found that they also liked films that starred Kevin Spacey and films directed by David Fincher and so that is exactly the combination the production team put together. The process is not foolproof as demonstrated by flops such as Marco Polo but the company knows that the system does not have to be foolproof, it just needs to be predictably better than pure guesswork.
Alibaba is another great example. They used the vast amount of data they held on the merchants that used their platforms in order to launch Ant Financial and MyBank. They now use Machine Learning algorithms to assess a person’s creditworthiness based on his or her spending history and other data including friends’ credit scores. This allowed them to begin offering unsecured micro-loans to those merchants. Not only has this created a completely new business model using new technology (radical innovation), it’s increased access to capital and the amount of sales merchants drive through Alibaba (incremental innovation).
Data Skills Programs
Whilst launching innovation programs is mired in difficulty, that is not the case for all skill uplift programs. There is a global shortage of data and analytics skills, organisations are shifting towards being increasingly data driven but cannot find the talent to support them in that shift.
This results in demand for increases in data and analytics skills even without the added lens of “innovation” as a driver. In this instance, organisations escape many of the political difficulties that accompany launching an innovation skills program in a mature corporate environment.
Furthermore data skills have the opportunity of very quickly showing return on investment and real value. The delay in the impact being seen of an innovation program does not apply to data skills as they can be immediately used to drive improvements in performance of business as usual. By empowering employees to use advanced analytics techniques, they will very quickly become more productive in the roles and be better able to make data driven decisions.
Analytics skills tend to come hand in hand with coding skills and an appreciation of the power of automation. By automating repetitive tasks (e.g. replacing the manual receiving of an email attachment and then using a spreadsheet tool to wrangle it into the correct format) using more advanced tools (e.g. creating a Python script to download the data automatically and deliver it in the correct format) employees will be able to save time to spend on other, more productive tasks.
Crucially by democratising these skills outside of the specialist teams in which they tend to be siloed organisations can create the “killer combination” of deep domain specific knowledge and analytics skills. The people who are best placed to identify areas where data can be used as a tool to drive innovation are the people who have been working in those areas.
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We will be hosting two events in November to discuss the topic of Data-driven Innovation. Come by, listen, challenge us and have fun! They will be held in Auckland and Sydney on 11th and 14th November. Please RSVP on the respective event pages.
