*Updated September 2022
Data and innovation aren't words that you often see referencing each other. When you do, it's usually where "innovative" is the modifier for some ground-breaking data science. Rarely do we think of "data" as a means towards innovation. However, what I will propose to you in this article will hopefully change your mind about that. We will look at data vs. creativity and structuring innovation around data, but we need to clarify our terminology before we get to all of that.
What do we mean by data-driven innovation?
We define data-driven innovation as the initiative to make decisions for innovation initiatives based on objective data and preferably with a significant foundation. More specifically, it is data that provides the user with a method of making certain decisions. The outcome is an overall improvement in accuracy and ability to act in the best interest of your innovation initiatives.
Data-driven innovation is not about individual precision but overall upward growth. In other words, it might not be right every time, but just as with statistics, we will see a trend with enough decisions that underline a positive impact on acting based on data.
The term and approach have their origin in the modern scientific method, which has provided a framework for scientific discoveries going back to Francis Bacon in the 1600s—although early methodologies of the scientific method go back to Ancient Babylonia. But I am digressing.
You can also think of data-driven innovation as the opposite of acting on gut feeling, vision, anecdotal evidence, or human experience. You know, the typical attributes associated with "Innovation."
So you can see where the confusion might come in when describing data-driven innovation. One might think of it as a form of innovation with characteristics opposite from what we commonly attribute to innovation.
Act on instinct, but act on data first
The idea of data-driven decision-making should be both a familiar subject and something that most professionals use in their daily work. From marketing to focus groups and product development, data is the root of making many decisions to improve the outcome of our activities.
However, when it comes to innovation, that is often an area of a business relegated to more "creative types." Design teams, think tanks, and innovation hubs, there are a host of clever titles given to these groups of people who tap into a—sometimes individual, sometimes collective—ideation process that is often based on intuition, brainstorming, and creativity.
While those groups are undoubtedly essential for innovation, the process of driven innovation through data can provide those teams with some more concrete structure and results to base their creativity upon.
The approach I would like to propose is one where creativity is encouraged by choices based on underlying data. It is one where decisions are ultimately enabled by objectively analyzing data through a data-driven innovation methodology.
Data vs Creativity
One might argue that structured creativity is creativity that is smothered by frameworks. However, while this might be true somewhere, like in the art industry, it is far less true in corporate and commercial industries. Unfettered and unshackled creativity within a corporate structure rarely leads to long-term sustainable success because structure is a core foundation for good business.
Anyone that has been involved in a blue-sky brainstorming session that moved directly into implementation is well aware of unavoidable pitfalls that occur with a lack of structure.
Despite what you're think-tank or marketing department might tell you, there need to be structured boundaries around creativity to be innovative—and in most traditional situations, those structures are implemented afterward. Inspiration, Ideation, Implementation. Those are the core steps of Ideo’s iconic design thinking process.
However, the methodology for data-driven innovation differs by implementing a structure "before" the creative phase—a structure based on data and science.
So this begs a few questions: Why is data science so foreign in brainstorming sessions and the later stages of prototyping, finding MVP, or setting a new direction for M & M&A and collaboration?
The way I see it, there is a two-fold reason for this. One, there is often a lack of data for those early-stage initiatives—or at least the structured and helpful kind of data that you can use as input and catalysis for the process.
The second reason innovation initiatives are not often founded on data-driven decision-making could be a cocktail mix of gut feeling, reliance on the wisdom of an expert, following trends, fear of rocking the boat, or good old assumption.
Now, of the reasons mentioned above for using the traditional route of driving innovation, there is perceived merit. Gut feeling is often attributed to the industry-changing icons of our day. Reliance on professional expertise? It would be reasonable to trust people who have worked within a particular field to be more knowledgeable and capable of making better decisions than others with less or no experience.
Following trends or traditions? That is a classic Moneyball situation, where the entire industry or field has an outdated approach to something where the data can be identified and implemented to produce vastly more impressive results and fewer resources.
Long story (slightly) shorter, creativity is an inherent driver of innovation. Still, with the massive amount of information that is out there about any number of subjects, it is time that we acknowledge that the human mind is limited in its ability to make leaps of intuition regarding "how to innovate."
However, when creativity can stand firm on a foundation of solid data and structured methodologies, then that is where simple creative ideas can be scaled into large outputs which constitute real innovation.
What can you do to start driving innovation with data?
The most obvious implication of working from data is to make sure you have data—in large enough volumes and at the quality you need.
Once you have the data to work from, the more complex element is to ensure that it's available and tangible for the organization to implement and use confidently. If you share raw data in a large table, it will be impossible for most people to process and draw conclusions from it. For example, think of how long it would take your team to scan through 500,000 startups from all around the world?
So, when you have a table of data that you want to start testing some theory on, you will want a way of parsing through all of this data. Some companies have teams for this; others hire outside agencies. We have built an AI-driven platform that can parse through those 500,000 startups in mere seconds, based on what you are searching for.
What processes can you implement?
Implementing frameworks like LEAN and Agile can help businesses move away from the traditional waterfall model. They improve your chances of cutting waste and speeding up the process by having touchpoints and indicators for if and when you are moving the wrong way and how to try and correct it.
Both the LEAN and Agile methodologies streamline the process of ideation to implementation and production. Both have fundamental similarities that draw on the scientific method and data-driven decision-making.
Another very tangible but more data science-founded model is IBM's Data science methodology, which provides a framework for feeding business intelligence and understanding into a workflow and going through data collection to preparation and modeling, all the way to deployment and testing. This model is mainly used for actual data science projects. Still, it provides a thought process that ensures the underlying elements for making decisions are there and then tests and iterates on it to improve or scrap the hypothesis or something in the making.
How to jump-start innovation initiatives with Valuer
When it comes to empowering an organization to quickly and effectively involve and introduce data-driven decision-making into innovation workflows, we have worked to solve many of the pains that would otherwise restrict an organization from working based on data.
Our Platform is fueled by a vast database with even more data points. However, it's not something our users will even need to be aware of.
The Platform is designed for exploration, discovery, and navigating through that vast amount of data. It can be used as a preliminary process for inspiration points for a creative process, or it can be used in the creative process to help make informed and critical decisions based on the insights that Valuer services can provide.
And what are those services, specifically?
Bespoke reports
One way of diving into the vast universe of data around technologies, startups, industries, and trends is to have our in-house analysts help you compile a bespoke report on the specific data you need. These reports can be requested within any technology or cluster of companies, technologies, or industries and are rooted in parsed data from our database.
A bespoke report can provide data and perspective that can kickstart an investigation into an area where a company could find value in trying to innovate.
Due to the custom nature of these reports that we do for our customers, no two are alike. They are as individual as companies are themselves.
Valuer Platform
Our Platform is effectively your innovation hub. It's the more self-service or on-demand approach to Valuer's data-driven innovation methodology. Our customers have seen a definite ROI in using the Platform to further their innovation initiatives.
As a user, you can freely search through our vast database of technologies, industries, startups, and scale-ups to help you find results to drive your decision-making further.
Additionally, the app allows for targeted in-depth research on both companies and technologies, which can speed up gathering data and further improve your workflows.
