As the possibilities at the intersect of M2M+Big Data gets unlocked its very important to examine the economics underlying the use cases
So the core questions becomes
"What is the economic value of correct prediction in the M2M Big Data world ?"
How can we monetize on device predictions ?
Is the effort taken disproportionately large compared to benefits unclocked in predicting outcome ?
Is the cost of incrementing additional sensors points to collect data worth the returns ?
In this blog Flutura would share 3 real world examples of the economic value power of prediction in the M2M Big data world
M2M Example-1 : Predicting Oil blow outs in a Digital Oil Field
One month back Flutura's Oil n Gas M2M data scientists were engaged with a leading Houston based provider of valves for the oil industry . The problem they were solving was that of Oil blowouts. A blowout is the uncontrolled release of crude oil and/or natural gas from an oil well or gas well after pressure control systems have failed. While executing the engagement we understood the economic equation when for example the cost of an pipeline blowout on an average cost 5 billion USD. Modeling blowout can help save billions of dollars in cost.
M2M Example-2 : Predicting energy leakages in a Power Grid
Similarly last week Flutura's Energy M2M data scientists kicked off an engagement with a utility firm. The problem they were trying to solve was the millions of dollars lost when energy transmission in a power grid gets leaked when equipments are inefficient in transmitting (step down transformers ) and/or electricity pilferage happens. A business case from a utility in Canada, BC Hydro, reveals electricity theft cost them at least 850 GWh or approximately $100 million U.S. dollars per year. We understood that millions of dollars can be saved by predicting energy pilferage on specific high risk corridors.
M2M Example-3 : Predicting network attacks from "low and slow attacks" in Telecom
Fluturas Telecom M2M data scientists were engaged with a leading telecom provider who was experiencing a surge in attacks on their network infrastructure. These attacks were getting increasingly sophisticated going beyond the traditional denial of service attacks. These were experienced hackers lying low and "poking" vulnerable points in the network to solicit a response before launching a full blown attack. Identifying signatures of these low and slow attacks can save millions of dollars in lost down time on the telecom infrastructure.
The above real life end user use cases give a glimpse of the economic impact of M2M prediction models To conclude,while M2M and Big Data is very interesting, Flutura is sharply focussed on the economic value which gets unleashed at the intersect. We shall share more of Fluturas experiences in the coming weeks, specifically on the economic value on executing M2M Big data solutions.
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