Globaldata: Artificial Intelligence (AI) more crucial for IoT than Big Data insights

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Artificial intelligence won’t solve every problem, but its current applications are already fundamentally reshaping the way we do business and collect data.

While the development of the internet of things has revolutionized heavy industry, online shopping, localized data collection and virtually every other aspect of modern life and business, innovators are still struggling over the future of the IoT, and how they’ll get there. While many see big data as the driving engine behind the IoT, savvy investors and entrepreneurs have shown that the real power behind the interconnectivity phenomenon is artificial intelligence.

Tapping into the potential of AI won’t be easy for innovators, but doing so will be far more profitable for the IoT’s future than relying on big data alone. As programmed intelligence grows to new and greater heights, its ability to optimize the IoT will only be enhanced.

A recent GlobalData survey of 1,000 IoT professionals revealed a heavy reliance on traditional business intelligence (BI) software. 40% of those surveyed ranked business intelligence platforms well above all other means of analysing data. Unfortunately, with the broad market trend toward the democratization of data now well-established, such do-it-all BI software platforms have already given way to numerous smaller, more discrete ways of deriving value from enterprise data, be that a direct SQL query, a predictive data modeller, an auto-generated data discovery visualisation, or a live, interactive executive dashboard.

The reasons for this are simple: business intelligence software is reactionary and static. Its users rely heavily upon basic reporting mechanisms that, in turn, rely heavily on laborious queries and reports – a very costly venture to both build and maintain.

This reluctance to follow the broader market away from BI platforms within IoT is concerning, given a subtle shift noted in the same survey concerning when, during its lifecycle, an IoT deployment fails.

In 2016, no failures were noted post-deployment. In 2017, however, that number shot up to 12%. Artificial Intelligence (AI), however, can do far more than inform. It can immediately prove the value of IoT as a means of optimizing existing business processes. . With even the simplest AI machine learning (ML) framework and model at the ready, for example, IoT practitioners can solve two pressing problems: detecting anomalies and predicting desired outcomes.

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