Data analysis helps organisations make sense of complex data sets, providing insights that are not necessarily visible to the human eye.
The availability of higher computational, storage and networking capabilities means that the amount of data being transferred, are increased proportionately.
According to the Senior Research and Consulting Manager at International Data Corporation (IDC) South Africa, Sabelo Dlamini, that has resulted in the increased use of Artificial Intelligence (AI) to derive insights that drive business decision-making: “AI is one of the tools that can be used to analyse data and give the organisation deep insights. It is several algorithms that mimic human behaviour in analysing the data. Along with Machine Learning (ML) and Deep Learning (DL), it is one of the techniques used to analyse the data, apply intelligence and ultimately provide insights,” he says.
With the adoption of these analysis tools, comes the responsibility of identifying the right tools to meet the needs of the business and to hire employees with the right skills.
“It comes with embracing the use of innovation accelerators such as AI, robot process automation (RPA) and using data scientists – a role which is becoming vital in organisations,” Dlamini adds that the types of insights the business wants will determine the tools they deploy. “The organisation needs to decide what type of data they want to analyse and the type of insights they are after. AI is a collective word for a number of algorithms that can be applied, so it’s more about zooming into the AI bag and seeing which tool you can be able to pick based on your needs and cross-checking your solutions to ensure they meet your needs.”
With most organisations already overwhelmed by the amount of data that is available, it is now more about prioritising what you really want out of it. “When you get all the data that is available, you can easily be side-tracked by interesting insights that might not even be relevant to the business. The key here is to know what the business priorities and KPIs are and then focusing on those when you analyse the data,” says Dlamini. “It is about knowing exactly what you want to extract from the data or why you are collecting that data in the first place.”
While AI does reduce the need for human intervention, it does not eliminate it entirely. “There will still be a need for skilled individuals or people who are able to relate and narrate insight into human experiences and communicate them. This is not something machines or robotics would necessarily be able to do. You will still need someone to interpret the data because while robots are much more advanced and able to solve complex problems, they might struggle with some of the more basic problems that require a human touch. They are also not capable of relating some of the results to human experiences that have a practical implication and must be related meaningfully to real live cases,” he says.
Dlamini says AI, the Internet of Things (IoT) and the fourth industrial revolution (4IR) are significantly changing the way data is collected, managed and analysed. “With IoT, we will eventually get to a point where pretty much everything will have computing ability or a sensor and send data on a regular basis, resulting in big data. To draw insights from that data you will require AI tools to draw the required insights. So, these are all interconnected and in the bigger picture of things, like the fourth industrial revolution (4IR), it is more about defining which use cases you will be focusing on,” he says. “It will drive change in industries as they start requiring less human intervention. In manufacturing, for example, we are envisioning factories being run by robotics or AI, or even running autonomously. It will be about having the sensors in place to collect the data, storing that data and having the AI mimicking human intelligence to make the decisions and feedback to the factory, enabling it to run on its own.”
Dlamini concludes that organisations are already being prepared for this eventuality. “While data analytics capabilities seem very new and almost futuristic, it will eventually become the same as generic computer skills. Where 30 years’ ago, computer skills were only for the select few, they have now become the norm. We foresee that soon, as with computer skills, everyone will have the skills to analyse the data and the capability to apply the right tools to draw the required insights.”