While data analytics has gained massive importance as a vital business tool in recent years, it's not exactly a new concept. Some date the beginnings of the formal analysis of data for business use back to the 19th century, with Frederick Winslow Taylor's time management exercises.
Others argue that data analysis has been around for as long as businesses have existed, but it just went by a different descriptor, namely 'consulting'. And still others go even further back, claiming that the use of data, albeit not on the technological scale of today, can be traced to the early Egyptians, who used it to build the pyramids.
Irrespective of one's belief in the origins of data analysis, the rapid and massive evolution and broadening of the science, particularly over the past sixty years or so, is beyond question. The arrival of computers in the late 1960s was the primary catalyst for this rapid increase in evolutionary momentum. Then, as recently as 2005, the evolutionary process exploded with the advent of big data, enhanced data warehousing capabilities, and the Cloud.
As quickly as the software and hardware used in these analytics evolved, particularly in areas like artificial intelligence, robotics, and machine learning so too did their use, and possibly more significantly, the expectations of what they should be doing for businesses. For most businesses, those expectations also evolved from wanting to leverage data and analytics to improve efficiencies, to requiring the analysis and application of data to ensure a competitive advantage. Inevitably, this caused the evolution of data analysis to speed up exponentially, and analytical techniques developed to meet the expectations, giving rise to disciplines like predictive analytics, data mining and machine learning. However, the pace at which actionable insights have been unlocked by such analysis has been a lot slower, mainly due to a lingering shortage of qualified and capable analysts, the prohibitively high costs of tools, resources, and infrastructure, and a lack of insight by many organisations into the real value of investing in the science to answer business questions and catalyse business growth.
Adding to this gap between potential and actual outcomes is the fact that, recently we witnessed another step change in the rationale for data analytics in business. Unfortunately, this was a result of many organisations effectively bypassing the optimisation potential of the science, simply to keep up with the proverbial Jones's, by automating as many business functions as possible, and building bigger and better robots.
The major problem with this was that, in their rush to show that they are doing things better than their competitors, many businesses lost sight of the real value of data and the science of analysing it. Which is really to understand customers in order to add value to them. And in so doing, build more sustainable, robust, diversified and growth-oriented businesses.
Of course, data analytics can, and should, be a cornerstone of business success and growth going forward. But there are a number of checks and balances that every business needs to have in place to ensure that it doesn't get ahead of itself and focus more on being at the cusp of the ongoing evolution of data analytics, and less on unlocking its real value for them and their customers.
Be savvy about the roles you choose to automate. The initial rush to robotics was driven largely by a desire from businesses to build something so intelligent that it could automate even the most complex of processes. While automation can, and must, be harnessed to automate certain functions - like checking the accuracy of client data against existing information - trying to shoehorn total automation into areas that require creativity, subjectivity and empathy is an undertaking that's doomed to fail. These functions can most certainly be augmented by artificial intelligence, but we have a long way to go before robots are solely responsible for these roles.
Don't put the cart before the horse. Before you rush off to automate a process, build a robot or undertake machine learning, it's essential to make sure you have the data and analytics ability required to fuel that process and maximise its likelihood of success. The truth is that data science, in all its iterations, will always be in a process of evolution. In order to maximise the benefits of the science in business, we need to understand that evolutionary process and follow it, at least to some extent, ourselves. In other words, businesses must learn to walk before we try to run, irrespective of the urgency they perceive to exist in order to keep up with what their competitors are doing.
Don't disrupt for the sake of disruption (or because everyone else is doing it). Make no mistake, disruption in every industry is very real. The way businesses work is transforming right before our eyes. But the idea that you have to be disruptive in order to be successful, or to compete with other organisations, is fundamentally flawed. Disruption alone doesn't ensure success. Rather, that requires optimisation, transformation, and an ability to respond quickly to disruption when it happens.
Check your motivation. The socioeconomic challenges facing South Africa today mean that there has never been a greater imperative for businesses to be motivated by more than profit. The triple bottom line is more relevant and important than ever before in the history of business, which highlights the need to be purpose driven and motivated by societal needs. Investors know it.
As do shareholders. So, if businesses are approaching data analytics, disruption and automation from a purely financial angle, they are out of alignment with all their stakeholders. And that is not conducive to survival, let alone long-term success.
Ironically, the very nature of technological advancement has created something of a Catch 22 situation for businesses. These advances have served to democratise data, make its collection more effective, and make it accessible to analysts for the purpose of business development. However, these change catalysts are available to all businesses, which means that leveraging them for competitive advantage is actually becoming progressively more difficult.
The key to successfully leveraging data and enjoying the benefits of its ongoing analytics evolution therefore doesn't necessarily lie in striving to always be at the cutting edge, no matter the cost. Rather the business that will ultimately win through its analytical competitiveness is the one that stays clear of a 'first-at-all-costs' mindset and instead balances the science with non-technical success components, including a culture that embraces the human development potential of a data driven business.
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