A Data-Driven Approach Can Boost Your Innovation—Here’s How

Data-driven approaches can help identify new opportunities and open new markets through relevant insights that are great for innovation.
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Not so long ago, a lot of people thought that innovation came from some sort of epiphany. Companies developing clever yet simple ideas seemed like geniuses and inspired widespread admiration. Funnily enough, there’s plenty of people who still believe that’s how innovative processes happen. 

While there might have been a time when people relied on serendipity to come up with new products, services, or ideas, the reality is that we don’t need to wait for lightning to strike when searching for innovations. In today’s day and age, we have a more reliable ally for our innovative processes. I’m talking about data.

The Value of Data-Driven Approaches for Innovation

Becoming data-driven has turned into an obsession for all kinds of companies. In fact, a recent executive survey from NewVantage Partners has shown that most mainstream businesses are making solid investments in big data and AI initiatives as a way to become more data-driven. In fact, 91.9% of those companies are accelerating the pace of their data-driven investments. 

Such a massive interest in data-driven initiatives has a lot to do with the multiple benefits they bring, especially the agility they can offer when making business decisions. Executives also know that data-driven approaches can help them identify new opportunities and open new markets through relevant insights that are great for innovation. In fact, many refer to big data focused on innovation as “data-driven innovation” (DDI).

To put it simply, DDI refers to the practice of using trends, patterns, correlations, or insights found in data to develop innovative projects. Those projects could be anything from new products, processes, services, or systems that radically depart from their existing alternatives and that end up meeting a particular demand or solving an issue. 

The way in which DDI works is fairly simple to understand but quite hard to master. Using large datasets composed of information from multiple sources, AI algorithms detect and pinpoint potential opportunities for businesses to innovate. That innovation can be external (such as a new product or service) or internal (such as a new workflow). After getting those insights, the company using DDI has to define how to better act on them. 

Here it’s very important to make a distinction between data-driven innovation and data-driven optimization. While the first is a process to create something truly new and unique that redefines processes, markets, and industries, the second is about improving existing assets and resources. You can better understand the difference through a couple of examples. 

When Netflix first launched “House of Cards,” it was a huge success. Audiences loved the political thriller and subscribed in droves to the platform. Little did those audiences know that the show was born out of a carefully executed DDI plan

The streaming giant gathered data about its users’ consumption habits and found out that many of them loved the British version of the show, Kevin Spacey, and David Fincher. The opportunity was obvious. Right now, streaming platforms regularly put out movies and shows based on data, but back then, doing so was a huge game changer.

The “House of Cards” example wildly differs from how many web developers use data to inform their web development projects. In this case, web engineers use data-driven initiatives to better understand how people interact with websites. Armed with those insights, they can build websites and apps that offer a refined UX. However, that’s hardly innovative—adjusting websites is a common practice. And while that practice can benefit from data-driven approaches, the reality is that it’s closer to data-driven optimization than data-driven innovation.

That differentiation makes it evident that data-driven innovation offers something far more unique than data-driven optimization. While you can benefit from both of those, data-driven innovation approaches have the added benefit of creating value that can potentially redefine your company, your users, and even your market. 

How To Adopt Data-Driven Innovation

When you adopt data-driven innovation, you can cast aside gut feelings and intuition and empower your whole company with powerful new tools to boost their innovation initiatives. With that, I’m certainly not saying that data-driven innovation will replace your team’s creativity with algorithms.

What I’m trying to say is that data-driven initiatives can greatly improve your innovation processes, fueling brainstorming sessions and your own instinct with insights you might otherwise have never considered. Naturally, you’ll need to understand how to combine both

That begs the question—how do you adopt data-driven innovation? Well, it certainly isn’t enough to integrate a big data platform into your workflow and call it quits. If you truly want the benefits big data can offer for your innovation strategy, you need to make a planned adoption that boosts the right teams at the right moments and provides relevant information. You can do so by following these suggestions:

  1. Use data-driven tools where they can make the most impact. While virtually every operation in your company can benefit from big data, you need to be pickier when it comes to innovation initiatives. Start by analyzing your needs and pain points as well as those of your customers so you can define the better starting point. If you embrace data-driven tools but pursue a broad objective (such as “create a disruptive product”), you’ll fail. You need a precise objective that answers a specific problem. 
  2. Infuse your data-driven innovation process with as much data as possible. When you gather information from multiple sources, you can get a clearer picture of the issue you’re trying to solve. In fact, having more data can help your AI algorithms make deeper connections that can lead to richer insights. A word of caution, though: Don’t gather more data just because. Each source you consider for gathering information has to be relevant for your specific goal. Otherwise, you’re just bringing noise into your innovation process. 
  3. Give your data-driven approach time to show results. You can’t expect your data-driven tools to provide an innovative product or service on their first try. These tools get better with time, as AI algorithms refine themselves to provide more valuable results. What’s more important, giving time to your DDI initiatives will allow you to invest in other necessary aspects of any data-driven approach, including data literacy, data governance, and data training programs. With DDI, you’ll have to learn to be patient to reap the rewards.

As the survey above shows, data-driven approaches are now part of the mainstream business culture. It’s now time to take that to the next level, which means understanding how a data-driven approach can better serve your company. When it comes to innovation, data-driven initiatives can provide you a path forward that can completely redefine you, your team, your customers, or your industry. 

The key in doing so is being patient. While I understand that today’s world requires everything to be quick, there’s no rushing data-driven innovation. There are plenty of challenges to build a successful DDI pipeline, so you can’t expect to get it overnight. If that’s your main worry, then you might as well stick to epiphanies. But if you do so, remember something—once your competitors develop their DDI processes successfully, you and your instinct will surely be left behind.

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