Coding Tips

How To Approach Corporate Innovation The Data-Driven Way

Companies both big and small all want the exact same thing. From the mighty tech giants to the startup working out of a garage, they all want to maintain longevity, growth, and profitability.

To achieve this goal, organizations often strategize and mobilize employees with a shared vision in the hope that this philosophy would drive the business forward. But most of the time, giant enterprises and tiny startups find that growth just doesn’t happen.

What’s lacking here is innovation. In fact, it’s the most vital aspect of business agility. When companies innovate, it adds value to the market and increases revenue. But for it to be effective, there has to be a good measure of disruption.

So why is it a challenge for most companies? Are innovation managers not involved enough in driving business growth? Or is it something else?

For innovation to meet or exceed business goals, it can sometimes seem like it requires some unknown magical component. To some extent, there is some truth to this notion.

The solution to this problem is hidden away in the data. So businesses will have to make a conscious effort to analyze the information at their disposal and derive some valuable insights from it.

What is Corporate Innovation?

Corporate innovation is basically any type of corporate activity or product that combines 2 important elements: creativity and profitability. A useful framework that helps better understand corporate innovation distinguishes what “innovation assets” are controlled by the corporation from what belongs to others. While these assets can include anything from new technology, business model or patents, it's important to distinguish whether they're inside or outside of corporate control.

According to TechCrunch, the following methodologies are distinguished as follows in terms of inside - outside control:

  • Research & Development (R&D): starts inside and stays inside
  • Incubator: inside - outside
  • Accelerator: outside - inside - outside
  • Venture capital: outside - outside
  • M&A: outside - inside

Traditionally, R&D has been a trusted methodology for internal corporate innovation, while M&A has been regarded as an established way to bring external innovation under corporate control.

Why Data-Driven Approach Can Be a Big Pain In the Neck

Regardless of the field, any organization can take advantage of big data analytics to adjust business processes and make better decisions. But unfortunately, it’s not as simple as it sounds.

Making data-driven decision to drive corporate innovation comes with its own set of unique problems. Ultimately, no matter how many big data tools are utilized or how many “pretty” reports are generated, humans will have to come in and take the time to read them, understand them, and make appropriate changes.

Companies have tried to avoid this as there are ways to get things moving in this space while limiting human intervention. Some enterprises are trying to do this by automating business processes. But while companies look for ways to establish fully automated feedback loops, all the best widgets out there also have the potential to fail (and they will at some point in the future).

A possible solution to this might be gathering loads of data to figure out the mean time between failures (MTBF). But regardless of when statistics predict that something will fail, in reality, we just don’t know it. As replacing something too early will mean that money was wasted, this process will require significant time and thought.

The good news is that you can also automate this feedback loop to accelerate the ability of companies to respond to the situation. Through feedback analysis, results can improve automatically over time making the organization more responsive to change in the business environment.

This type of business-centric automated feedback loops probably provide the biggest benefit to enterprises, but it’s just a part of what will help the organization become innovative. It’s probably safe to say that levels of adoption are far from what they should be. While hardware can be maintained and growth can be engineered, it also requires the right people with the right mindset within the company to be effective.

Freeing up time for employees to focus on innovation is a step in the right direction. But businesses also need to shift their focus to delivering tangible outcomes (but this is not the norm). For example, when we look at marketing functions like brand building, this is actually measured by intangible values.

Corporations can benefit from having innovation managers to think beyond the innovation creation cycle to ensure that the actual intended value was delivered. Furthermore, growth engineering with a holistic view of product development, marketing, and sales can close the gaps between departments and help increase revenue.

Growth Engineering Defined

Growth engineering is omnipresent in the startup world where companies are forced to do a lot more with fewer resources. As time and money are in short supply, startups are usually desperate to secure executive support and funding. But to do this, they had to find a way to show a clear and measurable value.

Growth engineering looks at the business model in realistic terms which include the following primary goals:

  • Develop products to enhance customer retention (read what to consider before launching a new product to market)
  • Get the target market to hear positive news about the product
  • Get customers to identify themselves as potential curators of the product
  • Generate revenue

So when you follow this data-driven approach, product development, marketing, and sales all become interconnected around an engine. This, in turn, will create a unified alliance to develop a clear route to generating more revenue.

The support for a growth engineering will have an underlying foundation made up of a motivated team that’s willing to do whatever it takes to achieve qualifiable outcomes. This is key for companies to work on their growth internally.

The growth engine is made up of five parts:

  • Acquisition
  • Activation
  • Retention
  • Referral
  • Revenue

Each different part flows between each other in a logical manner. Now if you compare this to marketing functions, it’s dramatically different. In fact, it’s the opposite as marketing has multiple activities happening at once, but none are connected.

For engineered growth to be achieved, you need to have a cross-functional team with the required expertise to work together. This data-driven approach can also provide the team with freedom to work in a radically different manner while accepting failures as part of the process.

Read how our client successfully uses Software Team as a remote R&D unit to build a robust Big Data solution from scratch and eliminate a technical debt.

Growth engineering team members can help organizations change the mindset by using the insights derived from the data.  They can help turn the company into one that works smarter, faster, and in a more innovative manner. As more employees participate in growth engineering activities, a cultural change within the organization can also take place rapidly.

No matter how much you automate, the human element can never be replaced. But this type of data-driven approach will change the way experts function within an organization. While they used to tell everyone what to do, this form of open innovation philosophy will have them start with a question.

Without questions, you won’t know what answers to look for (in the data). Further, activities such as finding answers and engaging in experimentation sprints can also encourage radical thinking while decreasing risk, cost, and time.

But getting this right will also require some luck. Putting teams of humans together will throw in some personalities into the mix that have to work together. But by fostering diversity in personality types you can improve your chances of getting it right.

So a data-driven approach to corporate innovation will require proper data analytics processes and the right people to put it into action and achieve sustainable results. This will require a lot of trial and error to not only get the most valuable insights from the data, but also to put them into action.

IT Storyteller and Copywriter
Andrew's current undertaking is big data analytics and AI as well as digital design and branding. He is a contributor to various publications with the focus on emerging technology and digital marketing.