A recent survey of 1,000 IoT professionals by GlobalData has shown 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.
Using AI at the edge is a more effective way to addressing large data problems and ensuring business continuity, says the report. This is a key trend that has been followed by the Embedded blog.
The trend toward distributed data means these all-in-one 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.
This reluctance to follow the broader market away from BI platforms within IoT is concerning says GlobalData, given the problems 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%.
“With deployment and maintenance costs also topping our survey as the number one reason IoT deployments fail or are abandoned prior to deployment,” said Brad Shimmin, Service Director for Global IT Technology and Software at GlobalData. “It becomes clear, therefore, that IoT practitioners should emphasize tactical benefits over strategic analytical insights at least at the outset of a project as a means of proving ROI and securing future investment from the business.”
Artificial Intelligence (AI), however, can do far more than inform. It can immediately prove the value of IoT as a means of optimising existing business processes. With even the simplest machine learning (ML) framework and model at the ready, for example, IoT practitioners can solve two pressing problems: detecting anomalies and predicting desired outcomes.
GlobalData’s survey shows that enterprise buyers are eager to do just that with 43% indicating that the best role for AI is to centrally automate and optimise business processes.
The problem lies within the idea of centralisation. Centralization is part and parcel to traditional BI analysis and reporting and traditional ideas like predictive modeling. Where AI is most valuable, however is at the edge. IoT deployments need to employ tools like ML, not centrally, but at the edge, close to the device itself. And like today’s enterprise software, those analytics endeavours should be brief and to the point, and focused on solving specific challenges.
This approach will not solve the full set of potential problems, but it is affordable and will have a direct and immediate impact on businesses, helping to prove the value of IoT one problem at a time.
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