The problem with big data and business intelligence software is that it is reactionary and static. It is great for analysing things after the event -- but how do enterprises manage when they need real-time insight?
A recent survey from data analysis provider GlobalData showed that IoT professionals still have a heavy reliance on traditional business intelligence (BI) software. Around 40 percent of its 1,000 respondents ranked BI platforms well above all other means of analysing data.
Unfortunately, do-it-all BI software platforms have been usurped by smaller, more discrete ways of deriving value from enterprise data. It could be a direct SQL query, a predictive data modeller, an auto-generated data discovery visualisation, or an interactive dashboard that delivers insights in real-time.
The reasons for this are that users rely on basic reporting mechanisms that use complex queries and reports. BI software tends to be reactionary and static. This brings costs into the enterprise to build and maintain systems.
For the Internet of Things (IoT), enterprises need to focus their efforts on the basics of business optimization rather than innovate from insights. But businesses are reluctant.
This reluctance to follow the broader market away from BI platforms within IoT is concerning. The survey noted a subtle shift over time with IoT deployment fails.
In 2016, no failures were noted post-deployment. In 2017, however, that number had increased to 12 percent.
The top reason IoT deployments fail or are abandoned prior to deployment are deployment and maintenance costs.
Encouragingly, however, nearly 70 percent of enterprises who had already implemented an IoT solution indicate that the project had already met their return-on-investment (ROI) expectations, regardless of the initial goals.
AI could be the answer to the IoT problem. It could prove the value of IoT as a means of optimizing existing business processes.
Even with a simple AI Machine Learning (ML) framework and model, IoT practitioners would be able to detect anomalies and predict desired outcomes. This would enable them to solve two problems at once.
The survey shows that enterprise buyers are eager to improve operational efficiencies. Forty three percent of survey respondents indicated that the best role for AI is to centrally automate and optimise business processes.
Although centralization is part and parcel to traditional BI analysis, reporting, and predictive modeling, where AI tends to be most useful is at the edge of deployments. IoT deployments should use tools like ML, close to the device itself.
Any analytics endeavors should be brief and focused on solving specific challenges. IoT buyers want centralized, global visibility of the business but also local optimization through AI.
This approach will not solve all problems, but it is affordable and it will have a direct impact on businesses. It will help to prove the value of IoT by not building an expensive monolithic analytics system centrally.
Brad Shimmin, service director for global IT technology and software at GlobalData, said: "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."
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