In the 1940s, IMB President Thomas Watson allegedly predicted the world would need only five computers. Over the past decade, this statement has been recirculated and mocked as the consumerisation of technology has led to the creation of an estimated 2.35 billion devices (PCs, tablets and mobile phones) as of 2013. Indeed, there are toddlers who own the five devices Watson believed would satisfy the world’s computing needs!
Regardless of whether Watson uttered these now infamous words or not, few predicted the dramatic impact IT has had in the world, or indeed our thirst for technology as consumers. While the developed world has had its fill of computers and smartphones, the revolution has only just begun in the developing world, further expanding the ‘Internet of Things’ – a rapidly multiplying number of connected devices generating data.
The Data Deluge
As is usually the case with unexpected growth, unforeseen problems are thrown up in its wake. How do we cope with all the data this network of devices is generating? While data analytics techniques have been used for decades, what we are dealing with now is on a new, vast scale. According to Qlik CEO, Lars Bjork, the first doubling of global data levels took about a century. Currently it is doubling every 14 months. At this rate, global data levels will exceed 40 zettabytes by 2020 – the equivalent of 5,200 GB of data for every man, woman and child on Earth.
Businesses and consumers alike stand to benefit greatly should they find ways of processing this data. Although the sheer quantities of data are intimidating, we believe ‘the humanisation of IT’ will provide the next stage of technological developing as well as the answer to our analytical challenges.
Natural Analytics
The humanisation of IT will essentially see the influx of technology that needs little or no training to use and suits our natural analytical abilities. It is often overlooked that all humans are naturally data analysts if we’re presented with information in the right way. Outside of technology, humans are naturally capable of automatically processing thousands of pieces of information every second.
For example, a hungry hunter foraging for food in a forest can make a multitude of observations and decisions to find what they need. They will instantly distinguish between edible and inedible fruit, the best conditions where mushrooms will grow, which are poisonous and where the much sought-after deer is likely to be found. Although a primitive analogy, this acknowledges three effective natural processes our brain uses to digest information and which to a certain extent are not accounted for in modern data analysis and analytics; the processes of association, comparison and anticipation.
The human brain strives to make associations. The hunter knows to look under oak trees for mushrooms based on previous experience that they have been found there before. Our mind constantly categorises and connects, searching out the important features together, and the warning outliers. Furthermore, we don’t simply stop once we’ve made these associations. The hunter will not settle with the first batch of mushrooms, but will find others and compare them against what they have found already; deciding if they are bigger or smaller for example. Also, they may draw on past experience to know where the ripest fruit is to be found or indeed where the deer herd will be grazing during that part of the day.
Finally, just as we make sense of the present based on our past experiences, we constantly anticipate the future. The hunter knows to eat certain types of food because they know from past and present experience that eating will aid survival.
Counter-Productive Tech
Each and every one of us uses these three natural tools on a daily basis in our everyday lives without even noticing them. We use these sensibilities in the business world too, to help us understand complex problems. However, most of the technology we have access to doesn’t complement or extend these natural skills.
Despite the consumerisation process, tech in the business world is still needlessly complicated – designed by experts for experts. How many tech initiatives at work require training before full use can be made of the product or service? Excel, for example, has functions that the vast majority of people never make use of. Complicated technology ultimately hinders productivity at user level, to the detriment of the business.
Democratisation of Technology and Data
By humanising IT, business leaders, will be able to empower the whole workforce, not just a select technical few, via the ability to analyse large data sets.
Google’s search engine is a great example of what is possible – a kind of technology which appeals to our natural sense. It has an intuitive interface which can be used by practically anyone, but has complex algorithms behind it to make it insightful and process huge quantities of global data.
Smartphones and tablets are also great enablers for making IT more accessible for everyone, providing another step to analysing data in the natural world. Touch and swish gestures on a screen are far more instinctive and therefore usable than the old fashioned mouse and monitor which dominated consumer technology’s opening decades.
With the tools and natural behaviour already out there, businesses need to provide humanised systems that foster our natural analytics ability. The rewards of making greater sense of company, customer or user data are great and can be extended far beyond the corporate world – to help solve bigger problems or even social issues (health or poverty for instance). If they don’t, we will remain submerged in the ever-growing data deluge.
Sean Farrington has been MD UK & Ireland and Regional Vice President for Northern Europe for Qlik since July 2009. Prior to Qlik, he was Regional Vice President and General Manager, UK, Ireland and South Africa for SAP Business Objects. During his tenure, he doubled the company’s revenue to approximately €30M per annum. Sean has over 19 years’ experience in the business software industry, 15 of those are within Business intelligence.
