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Is traditional BI software hampering your IoT success?

By Heather Wright
Thu 3 Aug 2017
FYI, this story is more than a year old

Reliance on traditional business intelligence software could be hampering IoT deployments, with a new report showing artificial intelligence is more important to IoT than big data insights.

The GlobalData report of 1000 internet of things professionals showed a heavy reliance on traditional business intelligence software, which 40% ranked above all other means of analysing data.

However, GlobalData says the report also shows a big increase in the number of IoT post deployment failures, with post deployment failures jumping from zero in 2016 to 12% this year.

The data and analysis company says business intelligence software is ‘reactionary and static’. Users rely heavily on basic reporting mechanisms which in turn rely heavily on laborious queries an dreports – a costly venture to both build and maintain.

“[The] 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,” GlobalData says.

Brad Shimmin, GlobalData service director for global IT technology and software says deployment and maintenance costs also topped the survey as the number one reason IoT deployments fail or are abandoned prior to deployment.

“It becomes clear, therefore, that IoT practitioners should emphasise tactical benefits over strategic analytical insights at least at the outset of a project as a means of proving return on investment and securing future investment from the business,” Shimmin says.

GlobalData says AI can do far more than inform, immediately proving the value of IoT as a means of optimising existing business processes.

“With even the simplest AI machine learning framework and model at the ready, for example, IoT practitioners can solve two pressing problems: detecting anomalies and predicting desired outcomes.”

GlobalData says AI is most valuable at the edge. “IoT deployments need to employ tools like machine learning, not centrally, but at the edge, close ot 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.”

The company says enterprises should avoid building an expensive monolithic analytics system centrally – with centralisation part and parcel of traditional BI analysis and reporting and traditional ideals ike predictive modelling.

Instead, GlobalData says IoT buyers should seek not only centralised, global visibility of the business but also local optimisation via discrete AI-driven outcomes.

“This approach will not only solve the full set of potential problems, but it is affordable and will have a direct and immediate impact on busineses, helping to provide the value of IoT one problem at a time.”

The report shows that enterprises are looking to gain improved operational efficiencies from deploying an IoT solution, with 43% of respondents noting that as their number one goal.

Cost reduction was second, favoured by 26% of respondents, with increased revenue from existing products and services (16%); increased revenue from new products and services (8%) and enhanced insight and decision making (7%) rounding out the pack.

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