"Show me the Money" - 5 Industrial IOT monetization models

Flutura was recently invited to a global IOT event and during the coffee break, we chanced upon a group of folks ignited by a speakers point of view and having an animated discussion on the practicality of Industrial IOT... One gentleman in particular was extremely vociferous - "SHOW ME THE MONEY!"(Apologies to Jerry McGuire). We empathised with the gentleman’s sentiment and felt there was a massive need to have a clear path to monetization and decided to share our experiential perspective based on the global Industrial IOT engagements we have executed in the last 24 months.

So what does it all boil down to ? 3 questions

1. How do we convert "Industrial Data Streams" to "Real Revenue streams”?
2. Which of the IOT offering has practical value resonance with the industrial customers? (Industrial folks have a “no nonsense” mental model :). Our experience has been fluffy stuff won’t cut it )
3. Is it a “Pain” or “Vitamin”? Does the customer have budgets already allocated or is there a need creation process to be gone thru

So we started on a journey to catalogue IOT models based on specific real life use cases where we impacted concrete outcomes and monetized machine data for our customers. That exercise resulted in 5 monetization models which we present below

a) IOT-Monetization-Model-1: RMAAS model (Remote Monitoring as a Service)

For example: Flutura was working on a connected buildings initiative where 11,000 strategic government buildings need to be risk optimised. Flutura created an industrial data product which processed billions of events per day from 96,000+ crucial industrial assets like boilers, chillers, pumps in real time with an intra-event interval of 3 seconds to the central command centre. The pricing model was price per asset per month for business models involving remote asset maintenance

b) IOT-Monetization-Model-2: PAAAS model (Predictive Action as a Service)

For example: Flutura worked on a predictive IOT data product for a global industrial giant which would proactively recommend which of its electrical assets needs to be recalibrated. (Most of these assets are installed with default settings and field conditions necessitate recalibration interventions). The pricing model here was a value added annuity service offering on a 3 year contractual basis which is a new revenue stream for asset manufacturers

c) IOT-Monetization-Model-3: Cross Sell VAS ( Value Added Services )

For example : Utility industry in the last mile is getting deregulated and REP( Retail Energy Providers) are coming with customer centric value added services like recommending energy audits based on guzzling habits of similar peer customers and offering asset refinancing is a new revenue stream

d) IOT-Monetization-Model-4 : EPS model ( Extreme Pricing Personalisation )

For example : Progressive insurance created an offering around bartering machine data (mileage, braking, turns, acceleration) from car which are a proxy for driving habits in return for discounted insurance prices.They would install a device in the car which would tap into the machine data generated by the car and infer driving habits from which a risk profile was created and a pricing model was decided which is unique to the individual as opposed to being a part of a generic segment

e) IOT-Monetization-Model-5 : MDAAS model ( Machine Data as a Service )

For example: In the consumer generated data world Nielsen, Experian and IRI created data exchanges where blind spots in consumer information where syndicated. What if we created Machine Data Exchanges and data blind spots in industrial organisations can be monetized based on benchmarking and other syndication constructs. The Pricing model could be pay per row or pay per analytical output. This is still at an infant stage and we expect this trend to rapidly accelerate in the coming years

Flutura does not claim to have an exhaustive catalogue of monetization model for the industrial world. We are sharing our humble experiences for the larger eco system to share and evolve. We are sure during the next time folks have a water cooler discussion on Industrial Internet monetization, Fluturas IOT monetization model taxonomy could come handy and they would depart as friends and have a glass of beer :). As the father of data monetization – Arthur C Nielsen said “The price of light is less than the cost of darkness”. We at Flutura passionately believe the price of machine data intelligence is less than the cost of “non digitization” in the industrial world.Happy to hear your views

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Industrial IOT : 6 Key Transitions

Engineering firms are going undergoing a fundamental transition. In fact Flutura from its global industrial IOT engagements in last 2 years has distilled out 6 core transitions at play . Depending upon the vertical (utility,heavy engineering, oil n gas) and market ( advanced / emerging) each of these transitions have different velocities and more importantly these transitions are irreversible. So what are the 6 key irreversible transitions which are accelerating and gaining momentum in the fast changing market place?

Transition-1 : From Engineering to Intelligence

Looking at an an engineering product, thru the eyes of a customer, one can differentiate a product on the engineering dimension or the intelligence dimension. Which provides more headroom to impact the consumers perception of value in the product - the intelligence or the engineering ?

For example, the car is a classic example where the headroom available to impact customer using intelligence far exceeds the headroom available to impact using engineering. Its relatively easy in the auto industry to create customer "wow" by providing mileage intelligence, personalised car settings or "Onstar" like remote intelligence than by a disruptive change to engine design

Transition-2 : From Data Streams to Revenue Streams

Is the increase in data streams matched by a corresponding increase in revenue streams ?

For example, in building management systems, with the decreased cost of instrumentation of assets like boilers, chillers , lifts etc there is going to be an explosion in data streams. The key question is whether these explosion in data streams also results in an increase in revenue stream from newer business models around remote monitoring, predictive maintenance etc.

Transition-3 : From Loosely coupled IT-OT systems to Tighter IT-OT coupling

There are 2 worlds in Engineering industries - the Operational Technology world of SCADA/PLC/Historians and out of condition monitoring software where ownership is with engineering and the world of business applications where CIO organisations own it

For example, in the oil and gas industry condition monitoring systems and process historians record a lot of data regarding the state of the process. But data from operational systems like SCADA/Historians are not "fused" and triangulated with the rest of the IT data say from shift management systems and employee databases or identity management systems which tell who operated the asset.

Transition-4 : From Fragmentation to Standardisation

One of the reasons the explosion of consumer internet was interoperability and standards. One of the main stumbling blocks of industrial IOT is the variety of different ways in which assets "talk" to each other. But this is changing

For example, in the Utility industry there is OSGP ( Open Smart Grid Protocol ) whose intention is to harmonise and speak a common energy exchange language

Similarly in the Oil n Gas industry standards like WITSML (Well site Information Transfer Standard Mark-up Language) provides non proprietary interoperability standards to exchange digitised oil well information

Transition-5 : From Edge Intelligence to Remote Intelligence

Flutura has also observed the increasing dependence on remote intelligence. For example typically the landing gear is controlled by a pilot instruction ( edge intelligence ). Supposing an adverse event happens and the pilot is incapacitated, in order to land the aircraft safely ground control can take over the flight using remote intelligence .Pilot programs under way to implement this use case to augment safety of passengers.

Transition-6 : From Pipe to Platform

Typically when an asset was being monitored, only single event streams where being monitored using condition monitoring systems ( How many times did the process exceed its bounds ?). But engineering firms are realising that in order to deeply understand the context of the asset the historian data has to be triangulated using ambient condition data and operator/human context. Doing so requires transitioning to a multi dimensional "platform" mindset as opposed to a "pipe" mindset which is very "uni" dimensional

Closing Thoughts ...

Flutura has observed that these transitions do have friction for adoption . But this friction is being "lubricated" or removed by the emergence of disruptive companies, regulatory pressure and customer expectations. Even if one of these Industrial IOT transitionsreach a tipping point it can disrupt the engineering market place dynamics considerably and as Jay Asher said

“You can't stop the future
You can't rewind the past
The only way to learn the secret
...is to press play.”

Flutura is excited as the Industrial world is increasingly moving towards pressing "play" :)