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New trends driving greater adoption of analytics

The adoption of analytics is increasing significantly, driven by a wide range of factors, encompassing everything from AI and open source to the soon-to-launch local cloud data centres.

It's clear that over the past decade, business intelligence (BI) has undergone a number of significant changes that have been underpinned by access to cloud technology. Furthermore, the availability of self-service analytics has democratised access to insights. Obviously, the BI landscape will continue to evolve as we move forward, but there are definitely several key trends, being driven by increasing digital transformation, worth noting as we move into 2019.

According to Akesh Lalla, country manager for SAS South Africa, the first of these trends is the demand for data quality management. After all, he says, the quality and context of data gathered is just as, if not more, important than the quantity of data gathered for analysis.

"More crucially, the consequences of making poor decisions based on insights gained from poor data could have a significant impact on an enterprise, and could ultimately determine the overall future of the organisation," he explains.

"Another trend worth noting is the rise of artificial intelligence (AI), which is feared by many. The reality is that we shouldn't fear machines, but should rather use them to augment humanity. A convergence of algorithmic advances, data proliferation, and tremendous increases in computing power and storage has propelled AI from hype to reality. Organisations are now beginning to embrace the use of AI, chatbots, intelligent services, machine learning, mobile solutions and social platforms to make work more enjoyable, simple and engaging."

Lalla highlights the popularity of open source and the growing open ecosystem as another vital trend, pointing out that organisations need to have the freedom to choose languages, tools, data, techniques and environments, because this is key to driving innovation.

Open source, he adds, provides an open space to try things out, to tackle new challenges, to explore the data and to see what answers it contains. A wide range of employees need to be able to take advantage of all these choices, regardless of their analytics skills set.

"However, controls are necessary to provide trust in the data, which is critical because, if you lose that, it's difficult to get it back. Businesses have to trust that the results of models are accurate and that the models will continue to perform over time. As a result, transparency, governance and security are essential. And controls become even more critical as organisations scale their analytic efforts," states Lalla.

"Another thing worth noting is the fact that South Africa is expecting an influx of hyperscale public cloud providers to implement local data centres this year. This, naturally, opens new avenues for forward-thinking providers to begin delivering analytics as a service. In the past, customers were less happy with giving their data over to an analytics organisation, owing to data sovereignty and other legal aspects. However, when the local hyperscale data centres arrive, these concerns will no longer be an issue."

Lalla also suggests that predictive and prescriptive analytics will become increasingly important, even as they are starting to take centre stage in discussions among BI professionals. As big data becomes the main focus of the analytics processes being leveraged across industries of all sizes, so the ability to undertake not just historically focused analytics, but predictive analytics, will come to the fore.

"While all of these trends are being driven by digital transformation, we need to remember that digital transformation is about much more than mere automation. Today, in the market, we see innovation-based disruption coming from everywhere. We see new technologies driving the ability for non-technical individuals to write apps, thanks to technologies like big data, edge computing, the cloud and open source. This surge in technology-based innovation, coming from non-traditional sources, leads to the disruption of traditional business models, allowing new entrants to establish new business models that challenge the status quo in many industries."

"This innovation-based disruption is the basis for the analytics economy. This could be described as the unifying fabric that delivers the full value of innovation-based disruption, because no other technology-based economy can deliver this kind of value on its own. It is only through the iterative, accelerating pace of applied insights, driven by the compounding value of combining data, analytics and collaboration, that technologies can be harnessed to form the basis for innovation-based disruption," he concludes.

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