Every 60 seconds, the world creates an average of 98 000 tweets, 695 000 Facebook updates, 11 million instant messages, 168 million emails and over 1,820 terabytes of data. The modern-day business has to search through all of this to find trends to build products and services to respond to. So more and more, business analysis is moving away from the “disruptive innovation” fad and more into a balanced approach where business uses data and information to decide on whether to optimise the systems and models they already have, or to totally disrupt markets.
We often view innovation from a funnelled perspective, where we perceive disrupting markets with new propositions as the only form of innovation. This is not true. Innovation should not simply always entail disruption, sometimes optimisation is the ultimate form of innovation simply because it looks to solve the core problem by fixing what is broken rather than replacing it all together.
Businesses need to first have a deeper understand of how optimisation differs from disruption.
Disruptive innovations are defined as technologically straightforward innovations that are often not appealing to the mainstream market at that time. The first automobile was not mainstream as people back then were used to horse-drawn carriages. The telephone replaced the telegraph, the PC replaced the typewriter and Wikipedia replaced encyclopaedias, all these fit the definition.
Businesses that survive and thrive need to understand what actually drives their business – demand and supply factors; and also be able to create customisable sales conversations to enhance customer experience. These all talk to optimisation. Disruption would then come in the form of extracting insights from data, combined with research to build new business models and enable experimentation of new ideas.
Successful implementation of disruptive ideas often comes with viewing data as a flow of information as opposed to at a point in time – do not view each customer event as an isolated incident, but rather consider the entire lifecycle of that customer to enhance your next interaction with them.
Essentially, there needs to be a paradigm shift in the approach to the use of data in informing decisions. Disruption may not be the most ideal or cost-effective approach, even though it is the current talk of the town. What businesses may have to consider going forward is that the successful use of data and analytics relies on data not being considered an IT function, but rather a business function that requires IT input and support.
Once data is viewed as a function that can inform all decision making in a business rather than reserved to a single business function, a business will achieve a more practical approach to making business decisions.
At the core of it all though is that business thinking needs to start moving away from using data simply to innovate through disruption. In most cases, the most effective way to innovate is to use data to understand what is currently not working and simply optimise that.