TECHNOLOGY
“ Hyperscalers have made the technology available for affordable prices and a lot of solutions and products are readily available on the market . All of this development has a knock-on effect on industrial sensors , IIoT gateways , networks and so forth , where the prices also came down while services , availability and capabilities became available . Take the development of 3G , 4G and 5G , in combination with cloud computing , which only enables topics like smart farming or smart cities . On the downside , the number of providers and solutions is growing so fast that it is very difficult for a customer to get an overview and find the right solution without being an expert .”
When it comes to the future for this technology , Beck adds : “ I currently predict two evolvements for the future of analytics technology . First there are more and more micro-services , solutions and products to allow normal users to tap into the field of predictive analytics without special skills or pre-knowledge . Even though the risk and weakness of this is that they are not considering the totality of the overall picture , they help educate people , build trust and are good starters for a data-first strategy . It ' s important for organisations to find the right balance when engaging with somebody to develop the solution path for their specific scenario .
“ Secondly , I see strong evolution bringing predictions and big data analytics back to the edge at reasonable cost . This will be very interesting to watch as it will open up even more business cases where high network traffic costs often hinder profitable business cases . The next major evolution will be affordable quantum technology which allows even larger prediction models and will truly be the enabler for complex prescriptive analytics .”
Echoing Beck ' s comments , Zimmermann also adds , “ the most significant change affecting predictive analytics and its future is its democratisation . Today data science is often deeply technical , expensive , hard to scale and siloed from the rest of the business . Low-code and no-code platforms are bringing these capabilities closer to line of business users that understand their workflow and have domain expertise but may not have data science training . This is
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