A quote usually attributed to Henry Ford, states that the difference between disruption and optimization can be summarized as his answer to the reason for inventing the Model T: 'If I had asked clients what they wanted, they would have said a faster horse.' Similarly, any competitive advantage, Big Data included, can offer companies the opportunity to improve an existing process or create entirely new ones.
A recent study by Forbes shows how active innovators are more likely to invest in Big Data (4.5 times) and how they use the results. The best applications include identifying new themes, highlighting trends, understanding ecosystems, recognizing competition and taking educated decisions based on data. It comes as no surprise that the top innovative companies of 2016 have placed capital in Big Data and AI.
The KISS principle for disruption
The paradox of Big Data as an innovation tool is that it allows increasingly simpler business models, based on cloud computing and other software tools and decreases the entry barriers. It has already been said: Uber has no cars, Facebook doesn’t create content and Alibaba has no stock, yet they are the biggest players in their niches, and they will most likely continue to be so until a competitor breaks the pattern yet again.
The lesson to be learned here is to capitalize on the immaterial. Use data as currency and think outside the box, along the lines of the sharing economy. You don’t need the assets yourself, you just need to convince the people who have it that you offer the best platform for their needs and use the collected data to live up to this promise while continuously improving. The correct order here is to disrupt, then optimize your business model.
The infrastructure for innovation
For this new world to run smoothly, the Internet is the backbone, and at the very least minimal access technology is necessary. Of course, as more resources become available, companies will need to think about ways of embedding their offer into the infrastructure provided by smart cities. Those businesses which want to make the most out of these new trends need to satisfy the high expectations of consumers. Big Data can help in this matter by providing a 360 degree view of the client, innovating the channels to reach them or innovating the ways a company gets across the marketing message.
An excellent example regarding infrastructure comes from third world countries, which previously had no means of implementing traditional business models and started directly in the Internet age. They don’t have classic banks, but online banking and money transfers from mobiles work great.
To disrupt or to optimize? That is the question
Data analytics can do more than making suggestions when it is time to carve a new way. It can show a company their current state and perform survival analysis. This helps assess if the time is right for a quality leap, or if a small step forward is enough.
Such an approach is necessary when the enterprise has begun a price war with competitors. When the only thing you have left as an advantage is money, it’s time to move forward and take a new lead. The difference between optimization and disruption is that optimization is replicable is a short amount of time. On the other hand, optimization means stability, while disruption requires a certain tolerance for reliability problems, usually a convenient price to pay. But first, when is it really disruption?
First, let’s be blunt and say that a new color of a product, a logo on it or adding an accessory to the package is not innovation, it is just marketing. Of course, if data shows that the public is receptive to this kind of changes in the short-term, serve them, collect the cash, just don’t call it innovation.
Next, try to keep your new business propositions relevant to your client base, don’t just do it for the sake of it or to follow a new commerce trend. Keep a performant system that serves the purpose and that clients gladly use and take into consideration the steepness of the learning curve every time you think about introducing an upgrade or an entirely revolutionary system.
The real innovation comes from creating AI systems that the end user can perceive as being human. Big Data consulting company Itransition have listed machine learning, natural language processing, neural networks and more as the tools companies can use to create disruption and examples include applications ranging from autonomous assistants to self-driving cars.
The breakfast of champions-tech start-ups
New tech companies flourish like mushrooms, but few of them live beyond their first 3-5 years in their original setting. Those who succeed in getting off the ground are quickly made an offer they can’t refuse by tech giants like Facebook, Google or Amazon and engulfed, although this usually means preserving the tech and the management teams. Some even suggest this is an anti-competitive practice, but could very well be regarded as an incentive to get into this activity sector and create solutions that never existed before.
Big data can even show emerging companies what the hottest trends are and the underserved niches to maximize their chances of creating a breakthrough and getting the attention of angel investors or established giants. Even seeing such an opportunity and seizing it is innovation at its finest.
Not all innovations are disruptive, even if they are not just optimizations of current ways, some just don’t live up to their potential. Big data has the power to create a new order by changing old fluxes and replacing working ways. To adapt and embrace the opportunities brought by big data, companies should first evaluate their current understanding of the phenomenon, the present analyses, and management capabilities and the steps already taken by competitors in this direction. At the end of the day, optimization and disruption are not opposite, they are more like different flavors of the innovation. The real challenge for companies that want to stay on top will be to choose the right one.