Big data, analytics and AI redefine the role of the data analyst

It is all too apparent that every industry is being radically impacted upon and even deeply disrupted by digitalisation, while existing and familiar roles are being transformed, making way for new opportunities to emerge.

One such role that is being challenged to prove its worth, is the data analyst, in no small part due to Big Data, analytics, and the most exciting, cutting edge development at the moment, artificial intelligence (AI). This convergence of forces begs the question: Are data analysts no longer needed?

Arguing in the affirmative is the fact that machines can process data at speeds that far surpass human capability, and do so 24 hours a day, seven days a week. Even the most dedicated data analyst, working from 7am to 9pm Monday to Friday (70 hours/week) is still vastly outpaced by the 168 hours a single machine can devote to that task in the same period, without requiring weekly and yearly breaks in its productivity.

Data Analyst - By the numbers

Additionally, machines can sift through data at a considerably lower price than it costs to employ a data analyst on an ongoing basis, earning the median average of R185 000 per annum. At the same time, the availability of data, and the need for it to be analysed, is only expanding. At present, 2.5 quintillion bytes of data are being created every day, while 90% of the data in the world today has been created in the last two years alone, according to IBM. Add to this the rise of machine learning, or cognitive systems, whereby computers become more efficient and smarter the more data they are given, and clearly, data analysts, using traditional tools, are not on a level playing field.

Furthermore, the financial benefits of the widespread adoption of cognitive systems and artificial intelligence (AI) across a broad range of industries are expected to grow exponentially, along with the tangible value they deliver. According to International Data Corporation (IDC), worldwide revenues from the use of these technologies will increase from nearly $8 billion in 2016 to more than $47 billion in 2020, while the market for them will grow by a substantial 55.1% between 2016-2020.

Keys to growth

From a business perspective, it has become essential that organisations leverage technological tools that are now available to glean valuable insight, and translate that into real business leads that increase the bottom line.

For example, using Leadify, one of our clients in the financial services sector has garnered 100,000 loan leads per month, which in turn generated R30 million worth of consumer loans. Furthermore, good targeting and clever exclusion handling have been responsible for more than 90% profitability across the direct marketing campaigns running through the platform.

Quite simply, the increasing role machines and cognitive systems are playing in every industry, from finance and technology through to medical, retail and marketing, cannot be ignored and should not be denied. However, a new possibility is emerging, with the tasks data analysts concentrate on shifting from the tasks to the more meaningful. Furthermore, data analysts will need to be able to work with both cognitive systems and software that can deliver a measurable return on investment, so as to increase their own productivity as well as the profitability of the business.

The path ahead for the data analyst

More specifically, there will likely be a convergence across the data analyst and campaign manager roles. For the latter, rather than spending 50% of their time extracting lists of data from different content sources, and weeding out duplicates, up to 60% of their workload will instead focus on data science and how to leverage data in more innovative ways. Certainly, sifting through data will remain a requirement moving forward, but it only makes sense to use tools that are designed to facilitate the process.

For all those dealing with data though, whether it is data analyst, campaign manager or data scientist, the prospects are considerably more encouraging, with data fluency becoming a sought-after skill. Indeed, it is widely recognised that there is a global shortage of data scientists, and barring that, individuals with analytical skills, that makes the field a lucrative and promising one.

Ultimately, to the question of whether there is a place for data analysts in this brave new digitally transformed world, it pivots on how quickly they become comfortable with turning data into profit, and fully exploiting technological tools on offer, to their advantage.