Big Data and analytics: mining information for value

Big Data and analytics have become omnipresent buzzwords recently, but what do they mean for how businesses should operate? Business Chief explores the subject with Abel Smith at Tech Data

It’s often said that, in our modern economy, data is becoming the new oil. Whether this metaphor is totally accurate is almost beside the point; in an increasingly digital world, everything is data, a fact that becomes ever more pertinent when the tools available for collecting and analysing information evolve. The scale of data’s explosion was estimated by Domo to reach 1.7MB of new information every second for every person on Earth by 2020, with an approximate total of 40 zettabytes (40 trillion gigabytes) globally. Contributing to this enormous volume is ‘Big Data’ - large quantities of information pertaining to corporate assets, which require highly innovative forms of processing to decipher and render useful for decision-making within business.

Abel Smith, Director of IoT Solutions at Tech Data, believes that how a company chooses to analyse its data can have a significant impact on enabling efficiencies. After all, when it comes to Internet of Things (IoT) devices, the value a customer derives will not necessarily be from the device itself, but rather the wealth of insights and options for action that the analysis of data can make possible. “Businesses, small and large, need to aggregate, unlock and organise their data so it is accessible and can be maintained whilst being secure and ethical. When that is in place, analytics can be used to visualise, gain insights and drive even more value with artificial intelligence (AI) and machine learning,” he says. 

AI-powered analytics

The premise of AI-powered analysis is rooted in the goal of designing technology that can perform tasks normally reserved for people. According to SAS, machine learning forms an independent subset of AI and focuses on training a machine to identify patterns in data and then ‘draw conclusions’ from it in a similar way to the human brain. First, machines are given ‘inputs’ and their associated ‘outputs’ in order to generate a prediction algorithm. Next, they are presented with a new input and use the set algorithm to predict an output - the ultimate goal being to refine the algorithm until the error margin between the machines’ prediction (called the ‘cost function’) and the actual output is as close to zero as possible. Therefore, machine-learning-based analytics represents a cycle: data is collected, an algorithm is formed and used to make a prediction, the result is collected and analysed, repeat ad nauseam. 

By investing in these next-gen forms of analytics, vast amounts of data, which would otherwise be wasted, can be transformed into a highly valuable asset. “By analysing the usage, the channel can begin to take a number of actions. For example, the data can give resellers and systems integrators an understanding of what challenges their customers are encountering and what additional services they might need in order to solve them,” says Smith. The seemingly infinite streams of data generated on a daily basis take on a whole new dimension, as each piece can be used to better inform executives on how to steer corporate strategy. “Information and dialogue can result in continual improvements, adding value for the end customer and helping to create lasting relationships built on meeting real-world business objectives. It also helps with securing and onboarding new clients, as the process of continual development highlights and helps you open up new markets.”

“For those companies that can bridge the gap between IT and business objectives, there are major opportunities for success,” Smith adds. But what does this mean for Big Data and analytics? 

Extracting the value of data

For many companies, this will mean finding ways to improve the end-user experience, with data analysis providing the engine to solve larger volumes of problems than ever before. In an article by McKinsey & Co, Victor Nilson, SVP at AT&T, explained that the company uses data analytics to optimise customer care. “We’ve used Big Data techniques to analyse all the different permutations to augment that experience to more quickly resolve or enhance a particular situation. We take the complexity out and turn it into something simple and actionable.” Other companies might leverage data analytics to improve the operation of a product itself, although some, like Vince Campisi, Chief Digital Officer at United Technologies, consider both forms of optimisation to be intrinsically linked. Campisi told McKinsey, “We’re starting to enable digital industries, like a digital wind farm, where you can leverage analytics to help the machines optimise themselves. It’s an example of using analytics to help a customer generate more yield and more productivity out of their existing capital investment.”

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The opportunities afforded by Big Data are practical and abundant for companies dedicated to developing innovative ways of analysing the available information. Smith remarks that, although the modern era is one of “digital supremacy” and technology is undoubtedly indispensable to nearly every industry, there is some hesitance - even fatigue - among executives for digital transformation schemes that under-deliver. However, the eminently practical and widespread advantages of streamlining via data analytics is an opportunity that should be fully embraced. “If there is one thing that businesses are interested in, it is how they can be more efficient, open up new growth, or be more compliant,” he says. “For those in the channel that want to continue to succeed, the focus has to switch from technology to business outcomes.”