M2M PREDICTION ECONOMICS IN 3 INDUSTRIES

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 ?"

1. How can we monetize on device predictions ?
2.  Is the effort taken disproportionately large compared to benefits unclocked in predicting outcome ?
3.  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.

8 M2M Use cases ... Analytics + Big Data + Building Management Systems

As the internet of things explodes, the building management industry is ripe for disruption from sensor data. Flutura has been obsessed with end user stories in the M2M big data space. Here are a 8 specific big data use cases which are at the intersect of M2M Analytics + Big Data + Building Management Systems.

Triangulating false alarms

Triangulating alerts from multiple big data event streams is key in M2M situation. For example the control room which is receiving fire alarm event needs to know if this is a smoker accidentally causing an alert or are their multiple alarms going of which could mean a serious event is happening in the building

Geofencing assets

In an Building management situation certain assets can have constraints on their possible location. If the asset crosses certain geo coordinates an alarm event can be triggered by examining the sensor and geospatial event streams.

Signal in technicians notes

Building management technician creates inspection reports which have a lot of unstructured text around keywords like "leakage", "noise", "vibration" which could serve as an early warning system to impending

Predictive modelling of crucial asset failures

Chillers and boilers are 2 crucial components in a building management systems. If enough historical data exists, one can build a predictive model which can proactively nudge maintenance staff to respond when it sees an increase in probability of breakdown

Real time condition monitoring

As a swarm of sensors engulf a building, it's very important to ensure that the alarm events are able to "swim" faster to the central command centre and not be impeded by the avalanche of state messages waiting to be processes by the central command centre in real time

Causal hypothesis testing

Building management technicians and maintenance staff have a lot of experience regarding potential causes of failure of say a lift. These hunches can be periodically harvested and a statistical hypothesis tests can be done to either confirm or reject the existence of an "experiential hunch"

Adverse event message forensics

When an adverse event happens say - crash of a lift or a fire gutting a floor of a building, investigative agencies may need to process the exchange of messages between the sensor and the command centre in order to understand the sequence of events leading to a failure

Alarm hot spot analysis

A central command center team can also do hot spot analysis of alarm frequencies to see if there are clusters of devices or geo locations which need intervention because of the abnormal amount of alerts experienced. It could also trigger a replacement of device on a proactive basis

Flutura would be posting more M2M big data use cases ... Stay tuned ...

What are real life end user stories which bring out the power of M2M and Big Data Analytics ?

The best way to understand the disruptive potential of Big data is thru actual real life end user stories of adoption on the ground.

Here are 3 we found interesting

End user story-1 : GE Trip Optimizer

It is a solution which leverages M2M and Big data analytics to optimize fuel consumption in locomotive engines. A 1 % saving in fuel = $ 1 Trillion dollars per year.

End user story-2: Samsung T9000

Its a futuristic refrigerator from Samsung which can be connected to the internet with Wifis and runs a lot of apps.

End user story-3 : Trxcare

Pill dispensers which contain a sensor. Each time a medicine is consumed from the dispenser it activates an sms to Trxcare command centre thru the vodafone network

We at Flutura will be sharing more such end user stories as the adoption of M2M goes mainstream and a sensor network engulfs the world :)

Which are the 3 Cs of M2M ?

Intelligent Building Big Data Use cases (M2M )

Intelligent buildings are the future. An intelligent building consists of devices like lifts, chillers, boilers, fire alarms, smoke detectors, carbon monoxide detectors all of which are emitting data. There are multiple times of data

1. State data .For example temperature of a room or pressure of water in boiler

2. Alarms data . For example when certain events of interests are observed, for example a fire alarm goes off.

Using these 2 inputs the context of a device in an intelligent building can be modeled.

Lets take the simple case of understanding the history of a fire alarm which has been installed on a crucial building in the city.

This alarm has been installed for almost 3 years. The technician wants to understand the history behind the device

1. What is the rate at which alarms are going off ? ( Velocity )
2.  When was the last time this went off ? ( Recency )
3.   How many times did it go off ? ( Frequency )

A big data analytical solution can stream events from this device and help the technician answer previously unanswerable questions regarding the health of a building or a device in the building. So what are the other possibilities M2M and Big Data Analytics unlock for the Building Management Systems

When 2 worlds collide - M2M + Big Data ...

When human beings got connected, it unlocked a whole new set of possibilities and companies like facebook, linkedin etc came up with solutions which had never been there before.

The ability of machines to interact with each other promises to unlock a whole new set of opportunities unprecedented in our history

Flutura has been working on some game changing M2M big data analytical solutions with multiple industry players for the last 12 months .

Some of the most interesting M2M use cases have emerged from Oil and gas, Energy/Utility industries, Intelligent building management systems, Security device use cases and telecom sector. 

But before we go the use cases lets step back and walk thru some of the most frequently asked questions in plain simple language

What is M2M ?

Its machines having the ability to communicate and "talk" ( hmmm machines can talk ? )

What are some real life examples of M2M ?

1.  Connected car ( Think Fords onstar program )
2. Clinical remote monitoring
3.  Security ( Think listening to firewalls and identity management devices )
4. Pay as u drive car insurance ( Insurance )
5. Smart meters ( Energy )
6. Traffic Management ( Smart City )
7.  Building Automation systems

What are the forces driving M2M to the tipping point ?

1.   Miniaturisation of sensors
2.  Plummeting cost of instrumenting an asset with sensors
3. Regulatory needs ( European regulation on sensors in vehicles and Energy smart meters )
4. Emergence of eco systems
5.  Innovations in bandwidth + cloud + taming big data

Who are the players and what roles do they play in the M2M ecosystem ?

The 3 major players in the M2M eco system are

1. Device manufacturers ( Honeywell, GE , Philips etc )
2. Network carriers ( Vodafone, AT&T, Deutsche telecom etc )
3. Central monitoring players ( Pacific data systems )

Trust you enjoyed the first in a series of blogs on the intersect between M2M and Big Data Analytics. Stay tuned as Flutura goes deeper and deeper in the M2M Big data ocean :)