We’ve all heard about the trillions of dollars of value at stake in the business of the Internet of Things (IoT), have heard of immense value to be captured from selling and using IoT-enabled solutions.

How do we make sense of the numerous innovations and use-cases? What lies ahead? What lessons from industrial IoT are applicable to healthcare, the toughest vertical industry innovation problem of the 21st century?

IoT in Three Waves

Let’s start by looking at IoT evolution from three perspectives — Operational Innovation, Product Innovation, and Consumption Model Innovation. I’ll tag them IoT 1.0, IoT 2.0, and IoT 3.0, respectively.

Each innovation area is best seen through the lens of the 4-stages of a Thing Lifecycle — the very same thing that we are connecting to the Internet. The four phases of a thing’s lifecycle where the maker of a thing designs and manufactures it (thing is called a product) and its buyer procures and uses it (thing is called an asset) — design-phase, manufacturing-phase, procurement-phase, and use-phase — are shown below.

Over the last two decades we’ve seen makers and buyers innovate in all the quadrants of the thing lifecycle. With this lifecycle in mind, let’s look at each type of IoT innovation below.

IoT 1.0 — Operational Innovation

The predominant IoT use case we were awash in during the last decade, has been product or asset maintenance — maintenance services that varied in nomenclature from proactive, to predictive, to preemptive. Each adjective tried to parse asset maintenance transitioning from time-based to condition-based in nuanced ways. They all addressed the use-phase of the thing lifecycle just do use things, not the design, manufacturing, or procurement phases. See quadrant-4 of the thing lifecycle below for the core operational processes affected by IoT in the use-phase.

IoT 1.0 is the era of aftermarket IoT, since in most cases, the thing — the capital asset we connected — wasn’t natively designed to be connected to the Internet. It required intermediate devices to translate data and send to the Internet.

The extent of value extracted from this step towards condition-based maintenance varied across industrial markets. In some cases, it was simply about visibility into the asset and data collection. Depending on the organization, we automated certain level of maintenance over the last 20 years to augment a labor pool using automation, while reducing downtime and driving capital efficiencies.

IoT 2.0 — Product Innovation

IoT 2.0 affected quadrants 1 and 2 of the thing lifecycle as shown below. The connectivity and smarts that are needed to network the capital asset were designed into the product itself. Manufacturing-phase of the thing lifecycle, adapted to this design change by integrating electronic and industrial supply chains and processes.

I call this, Native IoT.

If after-market IoT was like installing GPS in a car after we bought it, native IoT is like a car with built-in GPS.

Data from the use-phase, both functional and experiential, fed into design-phase of products. It is easy to see how IoT influenced a designer’s life. (Hint: Design Thinking!)

IoT 3.0 — Consumption-centric Innovation

Quadrant-3 of the thing lifecycle deals with packaging, pricing, and delivery models to enable new ways to consume the thing. IoT-driven changes to this procurement-phase changes the prevalent asset-consumption model from owning, to subscribing, to on-demand use, as shown in quadrant 3.

Some examples of IoT 3.0:

• Air-as-a-service from Kaeser Kompressoren
• Train-as-a-service from Hitachi
• Care-by-Volvo from Volvo
• Book-by-Cadillac from GM

Arguably, stage-3 changes to thing lifecycle could be done even without IoT. IoT makes this more easily and sustainably doable, while mitigating financial risks.

For a detailed dissection of various consumption-models is here: How IoT Transforms Business Models.

Mapping IoT Waves to the Thing Lifecycle

Below is a summary of generic IoT use-cases mapped to the thing lifecycle and categorized into the three waves of IoT evolution. You can see detailed industrial IoT use-cases and firms adopting them here.

Whither IoT from Here?

We are still in early days of IoT 2.0 since we are dependent on capital refresh cycles for an installed-base-level change towards native-IoT assets.

IoT 3.0 is disruptive not just to makers and buyers, but to the entire ecosystem that sells, finances, insures, and services capital assets in any industry.

A prime example is auto dealers suing Volvo when it started its car subscription service that combined lease, insurance, maintenance, and upgrades, a la AppleCare.

Elsewhere, in the healthcare industry, IoT evolution is still in its early days when compared to other industries. The lessons from industrial IoT asset monitoring and consumption models apply in healthcare industry too. E.g. Use of medical devices such as surgical robots (the ones shown in the graphics above), durable medical devices (DME), and even pharmaceuticals, could be monitored and sold as subscriptions.

While in industrial IoT we monitor things, in healthcare we are monitoring our selves. It may be more aptly termed IoS — Internet of Souls.

IoS would be an unprecedented amalgamation of digital and medical technologies, care delivery ecosystem, and public policy, building on IoT 3.0.

IoS = IoT 3.0 + Healthcare Ecosystem + Public Policy

A well-worn cliche says hindsight is 20/20. If so, IoT hindsight from the last 20 years should give us a head start on what lies ahead in the 2020s.

— —

First published on LinkedIn.

The post IoT 3.0 — Whither IoT in its Third Decade? appeared first on Hari Harikrishnan.

New ways of consuming products alters the entire Thing Lifecycle — from product design to asset operations, not just the making of an Internet of Things product.

We saw in previous articles in this series on digitization of a physical thing how the four stages of a thing’s lifecycle is impacted (Part 1), and how the production side of a thing’s life is impacted (Part 2).

In Part 3, we will examine the consumption side of the thing lifecycle — the procurement and use of an asset by the buyer.

Consuming a Digitized Thing

Consumption preferences of customers are changing in B2B and B2C markets. Consumption goes beyond user experience (UX) or industrial design (ID). It is about the overall asset lifecycle experience for the buyers.

For example, assets may be consumed on-demand, operated by third parties or vendors, and paid for via transactions or subscriptions. As the buyer, operator, and user interacts with the product and its maker — from procurement to use to decommissioning — asset operations change.

The graphic below illustrates four primary dimensions of consumption — mode, ownership, operations, and payment — and their variants, on how an asset can be procured and used.

For example, a traditional car purchase follows this consumption pattern: Mode 1 (we package it), Ownership 1 (we own it), Operations 1 (we drive it), Payment 1 (we pay for it as a one-time transaction). This example is a M1-O1-O1-P1 pattern. This is similar to buying license to packaged software.

On the other hand, using a ride-sharing service follows a M2-O2-O2-P1 pattern since we use it on-demand, without owning or driving it, and we pay per usage. Similarly, Software-as-a-Service (SaaS) normally follows a M2-O2-O2-P2 pattern.

More discussion on these consumption models can be found here.

Consumption Influences Design

Cars are beginning to get consumed differently via fleet operators and 3rd party drivers in a ride-sharing world. This is a prime example of consumption change.

CATERING TO THESE CONSUMPTION PATTERNS DRIVES NEW PRODUCT REQUIREMENTS AS TRADITIONAL ASSUMPTIONS OF OWNER, OPERATOR, AND USER PERSONAS CHANGE.

Functional requirements of smart and connected devices require obvious design changes. However the subtle product changes needed to accommodate fleet operators and split-roles between drivers and passengers is forcing a design rethink.

Whose tastes, convenience, and economic considerations should the car maker cater to? It is not as simple as typical demographics and psychographics of the consumer.

Consumption Ecosystem Impact

Digitization of the thing lifecycle has an impact on all the ecosystem players involved in the thing’s life. Here is a brief look at a few of them.

Buyers: Thing Consumers

As ownership, operations, and payment for the asset changes, buyers have more core vs. context decisions to make on operational decisions and personnel. They also have capital expense vs. operating expense (capex vs. opex) decisions to make. Opportunities to align costs to revenues is significant in the asset-as-a-service world.

Financial Services Firms

When assets are no longer purchased as capital goods, how should financing change? How does accounting change? How do leases change? How does insurance and re-insurance change?

Product liability in an autonomous world gets shared differently between manufacturer and operator. Warranty accounting in a connected-asset economy that teems with big data gets more precise. Can it bridge the gap between accruals of warranty liability and actuals? What does warranty mean in an ‘as-a-service’ world anyway?

The implications are far-reaching.

Service and Solution Providers

Professional services providers of all shapes and forms, from consulting to systems integration to managed and support service providers are already adapting to a connected-asset world as part of the IoT wave.

Beyond monitoring and managing assets, will they shift their business models too? How will they blend the skillsets across IT and OT to effect business outcomes?

Digitizing the thing lifecycle calls for collaboration across technology and business services providers. A new world of channel ecosystem collaboration is emerging.

Summary

Production and consumption of a physical thing is influenced by new ways of designing, manufacturing, owning, paying, operating, and using smart and connected products. Innovating requires a lifecycle approach to digitizing physical things — things as products and things as assets.

New modes of consumption changes how assets are owned, operated, and paid for. Consumption models fundamentally alter product design in ways we are just starting to adapt to.

The thing consumption ecosystem of financial intermediaries, software makers, and services and solution providers is adapting. Collaboration between IT and OT players across the thing lifecycle is countered by competition to mark new territory as boundaries shift.

Welcome to the new thing’s life.

🗓 This article was originally posted on iotforall.com.

The post Digitizing a Thing’s Life: Part 3 — Consumption appeared first on Hari Harikrishnan.

Digitization of a physical thing is more than the Internet of Things (IoT). More than connecting the thing and performing predictive maintenance. We saw this in part 1 of this series.

In part 2, we will take a closer look at the impact of digitization on the production of the thing and the implications on the ecosystem involved. In part 3, we will look at the consumption of assets.

Designing a Cyber-Physical Thing

The convergence of materials with information technology makes products not just physical as they used to be, but cyber-physical or cyber-organic.

The science and technology stack (“STEM stack”) on the left shows physical (“analog”) parts of the product and the cyber parts of the product. Designing a cyber-physical thing requires juxtaposing the following:

1. Materials: Blending of materials new and old. New plastics, polymers, and fiber herald a revival of material sciences and new products.
2. Processes: Taking advantage of additive manufacturing (“3D Printing”) and robotics in fabrication to build new products and geometry.
3. Electronic and Industrial Blend: Integrating computing and communications technologies from the semiconductor world with industrial world in both design and manufacturing.
4. Ease of consumption: This goes beyond simple user experience (UX) and aesthetics. Ease of consumption spans the lifecycle of the asset, as the buyer, operator, and user interacts with the product and its maker. It spans procurement to use to decommissioning. Subscription models allow the product to be consumed as a service rather than via classic transactional product purchases and owner operations.

PRODUCT DESIGN GOES FROM COMPUTER-AIDED TO COMPUTE-INTEGRATED WHILE PRODUCT ITSELF BECOMES SERVICE-ORIENTED.

All this requires not just cyber-physical product thinking, but a systems-thinking about cyber-physical systems.

Manufacturing a Cyber-physical Thing

New manufacturing processes (e.g. Robotics and 3D printing) with even tighter process control (using AI) bring more flexibility to fabrication so the product can be made and delivered to the buyer not only at lower cost but much faster.

They (and their AI-enhanced Digital Twins of the factory and supply chain) enable mass customization, avoid waste and defects, alter forward and reverse logistics, increase durability, increase utilization of expensive supply chain assets, reduce parts, and provide an outward focus (through visibility, traceability, and faster and better decisions) to the builder and buyer if anything were to miss a beat.

Production Ecosystem Impact

A few of the implications for firms in the production ecosystem are as follows.

OEMs: Thing Makers

The convergence of materials and smarts (“mechatronic” devices) requires a design-rethinking. Simulations and design automation for cyber-physical products move beyond traditional thermal and structural integrity to signal integrity and heating/cooling considerations for mechanical, electrical, electronic, and communications sub-systems.

New geometries are possible from stage 2 innovations like Robotic Additive Manufacturing. Contract Manufacturers (CM) from stage 2 partner with OEMs as Original Design Manufacturers (ODM).

Software Makers: Design & Lifecycle Management

Firms like Autodesk, ANSYS, SolidWorks, and Cadence add IoT capabilities to their products. Computer-aided Design (CAD) and Electronic Design Automation (EDA) firms extend into each other’s turf. e.g. ANSYS product line has EDA products.

This line-extension dynamic continues on to the manufacturing stage and beyond in PLM and ALM (Product / Asset Lifecycle Management) software.

Classic CAD firms and electronic CAD firms (ECAD or EDA) are tussling. Autodesk bought an EDA firm in 2016. Industrial IoT firms are buying or investing in small software or systems companies.

Manufacturers: Contract and Original Design

Contract manufacturing worlds of industrial and electronic equipment are nudging closer due to the digitization of the product. Contract manufacturing in electronic manufacturing services industry have been moving closer to OEMs by turning into ODMs (Original Design Manufacturing). e.g. Qanta, Foxconn, Flex, Jabil.

Collaborations across contract manufacturer types and OEMs, whether industrial or electronic, is poised to strengthen in the digital age.

Summary

Digitization of a Thing requires us to rethink about its entire lifecycle, from design to manufacturing to procurement and operations. Design and manufacturing are impacted by the entire “STEM stack” coming together to form mechatronic systems.

Product strategies including line extensions, M&A’s, and sourcing strategies change for OEMs as the thing lifecycle is digitized. Greater collaboration and consolidation within the OEM, contract/design manufacturing, and design and lifecycle software is in its early stages.

In part 3 of this series, we will examine how consumption changes for a digitized thing.

🗓 This article was originally posted on iotforall.com on December 6, 2017.

The post Digitizing a Thing’s Life: Part 2 — Production appeared first on Hari Harikrishnan.

Software may be eating the world. Yet, blindly wanting to be a software company could eat you up and chew you out if 3 fit questions are not addressed.

I listened while the speaker pounded their fists on the lectern and declared. “Walmart is a software company. Ford is a software company. GE is a software company.” On and on the list went on. After a few iterations, the audience got the message. Every company mentioned was greeted with a chorus of, “X is a software company”.

I was having visions by then of feeding the kids on software bought from Walmart — 5 licenses of low-fat milk please!

Let’s get real. Using software to improve your products or services does not make you a software company. So what will?

The Situation

How can systems or services businesses productize home-brewed software — software created to improve internal business operations or deliver services?

The situation looks like this for hardware equipment (OEMs / systems makers) or services firms:

They have an existing portfolio of businesses in various domains. Business operations support those businesses. Often software is built or customized for their needs.

For such firms, answering the software productization question is not easy. Not just because of “skills-gap” or execution issues ranging from build-market-sell-adopt cycle of building and delivering software continuously.

Successful productization is down to 3 fit question — before we ramp up execution.

3 Fit Questions

In the startup world, product-market fit is the hard work that goes on in early startups to prove out a value proposition in the market.

In established firms, product-market fit alone is insufficient to determine successful scaling of a new business.

Introducing a new software product lines in these firms has consequences beyond simple product-market fit. The 3 fit questions to test your software productization aspirations are:

1. Product-Portfolio Fit: How will the new product be positioned within the existing portfolio? Does it fit the overall company strategy?
2. Product-Channel Fit: What is the channel to take this product to market? Existing or new?
3. Product-Financials Fit: Are the financials similar to existing portfolio or is it different?

Let’s look at each fit question.

Product-Portfolio Fit

So you want to add a sizzling-hot swan to your portfolio of ducks. Perhaps it aligns with your mission to breed poultry that swim? Or of adding variety to your aquatic park?

Are you that industrial equipment company that muddled its way into selling some enterprise software? Or a services company that wants to sell software subscriptions, hoping to drive up its valuation?

Is software a means to improve your existing offering or is it a new product by itself? How does it benefit your customers and your investors if you became a software company?

In any case, be sure to place the new fowl side-by-side with the existing poultry to ensure its fit with your mission and product portfolio strategy. Or else, find yourself in deep waters after the initial excitement and multi-year investments.

Product-Channel Fit

Assuming you have built a swan, does it have a channel to swim to your customers? Are they existing or new? Can it reach the new buyers?

Do you have GTM (go-to-market) partners who can sell, deliver, and service your new product?

The example above shows the channel to reach the customer — the full complement of direct and indirect, tier-1 and tier-2 channel needed to scale your new offering.

Not mapping out a channel to get to new buyers for your swan will stunt your growth.

[More on this topic here: Rethink your GTM strategy for the Internet of Things]

Product-Financials Fit

Your current duck business is a cash-cow. But your new software business could see many dog years before it turns cashflow positive and profitable.

As seen from the illustrative financial metrics above, your duck and swan businesses could stand in sharp relief against each other when you look at your P&L and financial statements — after you account fully for the R&D and sales opex required to make the swan swim.

Note: You don’t need financial compatibility between the new and the old. In fact, you may never have compatibility. e.g. Amazon consumer vs. AWS. You do need to factor in the differences in the businesses and set expectations on growth and investment needed.

Not doing so will make our heads swim and your swan sink.

In Short

While exaggerating to make a point about the importance of software and declaring your company to be a software company, do not forget the distinction between using software to improve products vs. productizing software itself.

Test your software product for fit with portfolio, GTM, and financials, beyond product-market fit.

Aspects such as SaaS delivery, subscriptions sales and renewals, adoption, and customer success are important once we establish fit and move thoughtfully into execution.

A wise colleague once said, “just because you put up a website doesn’t make you an e-commerce company”. The same statement holds true for software, data, AI, blockchain, and any new hype. Just because you used those technologies in your products or services, doesn’t make you that company.

Before you jump into the software lake, align it with your overall portfolio strategy or your swan could turn into an ugly duckling really fast!

The post 3 Reasons Why NOT All Companies Become Software Companies appeared first on Hari Harikrishnan.

Don’t we love stories of IoT (Internet of Things) and how predictive maintenance delivers economic value? We are fascinated by the phase of a Thing’s life that deals with operations and maintenance. Many consider a digital twin as a means to proactively monitor the beloved Thing while it is being used, a shadow.

What happens when the Thing is born? What happens before it is born? Does digitization impact the poor Thing’s Lifecycle?

This article is the first in a series discussing the digitization of a Thing’s lifecycle from design to demise. We’ll look at the lifecycle of a Thing from a builder’s and buyer’s perspective, across design, manufacturing, procurement, and use.

Let’s explore if we can give it more care through its life, not just when it is about to break and we have to send in the cavalry to prevent it from breaking.

Digitizing a Thing’s Lifecycle = Product and Asset Lifecycle

To do justice to a Thing’s life, we need to look at the creation side of its life and the consumption side of its life.

CREATORS (MAKERS) CALL THE THING A PRODUCT, BUYERS OF THE THING CONSIDER IT AN ASSET.

A discussion on the Thing Lifecycle must then include both builder and buyer perspectives.

The graphic below shows the Thing Lifecycle in 4 lifecycle stages across creation and consumption, shown left to right from the builder and the buyer perspectives.

1. Design: How the product is conceived and prototyped by the builder
2. Manufacture: How the product is built in volume and the logistics of inventory, shipping, and returns are managed by the builder
3. Procure: How the asset and associated services are procured by the buyer (e.g. owned vs. leased)
4. Use: How the asset is put to productive use by the buyer over its economic life until it is decommissioned. This includes its operations and maintenance.

In these still early days of IoT, most discussions are focused on extracting efficiencies in stage 4 where the asset is put to daily use, e.g. maximizing overall equipment efficiencies (OEE) for equipment on a manufacturing floor.

However, digitization of a thing impacts all four lifecycle stages. There are efficiencies to be had from each stage: efficiencies in materials and processes, optimizing durability and strength etc. We are still in the beginning stages of re-imagining the Thing across its entire lifecycle to maximize value extraction.

A Thing’s Life Zoomed In

Here is a closer look at the Thing’s Life across the four lifecycle stages. The following illustrates key capabilities and activities we perform in each stage.

Everything goes aflutter with digitization. All aspects of the four stages are changing.

EACH STAGE IMPACTS THE OTHER. TECHNOLOGY INNOVATIONS AND CONSUMPTION PREFERENCES RIPPLE THROUGH ALL STAGES.

Here are the key changes in each phase due to digitization:

1. Design: Assets are connected, intelligent, and use modern materials. Designers leverage modern robotic and additive manufacturing processes for fabrication. Contract Manufacturers (CM) participate in the design stage and become Original Design Manufacturers (ODM).
2. Manufacture: New manufacturing processes (e.g. Robotics and 3D printing) bring more flexibility to fabrication. This allows for not just built-to-order improvements and mass customization, but it reduces waste, alters forward and reverse logistics, increases durability, and reduces the number of parts.
3. Procure: Beyond owning or leasing, an asset can be purchased including its operations. Asset-as-a-service business models are becoming more prevalent with IoT. How the asset is accounted for (capital lease vs. operating lease vs. subscription) is changing. How the asset is insured is changing due to changes in expected economic life, residual value, and liability for operations (e.g. onus to insure autonomous cars split between driver and automotive OEM).
4. Use: This is the most commonly discussed phase in IoT. All aspects of the asset’s operation can be monitored, recorded and optimized. Assets can be remotely controlled. Asset’s life can be extended via selective parts upgrades vs. full replacement.

Twins for Life: Bridging Design and Use

Digital prototypes have existed ever since computer-aided design (CAD) came into prominence. However, digital prototypes were never used by buyers or builders across the lifecycle of the product or asset.

Always-on connectivity is bridging that divide between physical and digital twins, from phase 1 through phase 4.

IOT ENABLES THE THING AND ITS TWIN TO STAY IN TOUCH THROUGHOUT ITS LIFE, NOT JUST DURING CONCEPTION.

As the physical thing “ages” in the field, the digital twin can virtually inherit its properties, making the Thing’s twin live.

A few examples of how this lifecycle view is driving vendor behaviour in design and use stages:

• GE and ANSYS collaborating to use digital twin from design stage to deployment.
• Autodesk partnering with Electric Imp and buying SeeControl to bridge the design and use stages.
• PTC supplementing its design automation software lines with the acquisition of ThingWorx IoT software platform.
• Mathworks enhancing simulation products for IoT

Summary

Digitization has a profound effect on the lifecycle of a Thing from its design to its decommissioning. That effect goes beyond new degrees of freedom in design and manufacturing stages to procurement and use. Design is influenced by new ways of producing, owning, and operating smart and connected products.

Product strategies, M&A’s, alliances and sourcing strategies change for product firms as the thing lifecycle is digitized. Digital twin is no longer a static digital prototype, but stays connected to the physical thing and ages with it.

In the 21st century, a thing and its digital twin are very much intertwined.

Stay tuned for future discussions on how digitization impacts each stage of a Thing’s Life.

🗓 This article was originally posted on iotforall.com on November 22, 2017.

The post Digitizing a Thing’s Life: Part 1 — Beyond Maintenance appeared first on Hari Harikrishnan.

It has become commonplace to hear news from the Big 5 in technology – Facebook-Microsoft-Apple-Google-Amazon. FMAGA for short, if you are like me and can’t recall who is in the Big 5 club. 

One day we hear from them about the cloud or the Internet of Things. The next day we hear about new phones, laptops, their ultra-sharpness, or about gadgets that talk back at their owners. Then off we go hearing of brain mimicry with AI (artificial intelligence), world mimicry with VR (virtual reality), or balloon-powered Internet and drone-based witchcraft.

What game of thrones are they playing? What is their playing field? What are they trying to control?

This is a look at the FMAGA and their continuing thrusts and parries through the lens of the following three C’s:

1. Communications: Encompassing all forms of communications including telecom, collaboration tools, social networking, chat etc.
2. Computing: From personal to business computing, smart devices to cloud computing and software stacks (middleware or PaaS, applications or SaaS, and emerging analytics and AI services).
3. Content: Physical goods, virtual goods (digital content), gaming, and technology-enabled services beyond streaming, such as health care and transportation.

We will see how each of the FMAGA started out their play in the 3C’s battlefield, how they have evolved, and briefly how they compare with technology veterans AT&T and IBM.

The Opening Game

Below is a depiction of our main protagonists FMAGA placed in in the 3C’s battlefield as they started their game in their home turfs (their heritage corners, so to speak).

Summary of the opening gambit of FMAGA in the three realms:

• Communications: Facebook birthed social communications of the 21st century via the virtual social network and is fast trying to capitalize on their 2B+ active users.
• Computing: The longest running battle in its fourth decade is continuing between Apple and Microsoft in this realm.
• Content: Amazon rules in delivering us physical content from books to groceries. and virtual content through devices and streaming. Google’s heritage is also in this realm, started by giving us access to sellers and content via sophisticated search.

Now that we’ve pinned their starting points on the battle field, let’s look at a decade of evolution in sixty seconds.

The Middle Game

Each player is assiduously protecting their core positions while ambitiously expanding to other realms. From each firm’s vantage point, moves towards other realms are either left or right as shown by arrows below. Yellow sticky notes show the product, the capability, or the initiative they introduced to make that move.

Acts of aggression by FMAGA, moving left or right from their original corners:

• Facebook: Moved left to content corner via Facebook Watch, the recently announced streaming service. Moved right to get cost-effective cloud computing via Open Compute Project (OCP). Trying to defend their hold on communications via Telecom Infra Project (TIP).
• Microsoft: Moved left with Skype acquisition for communications and collaboration and moved left again with LinkedIn, the business networking analogue of Facebook. With Facebook itself expanding to B2B services, the left side of this 3C’s battlefield should be interesting to watch as B2B and B2C approaches collide.
• Apple: Moved left to communications a decade ago and has since sold 1B+ converged mobile communications and computing devices. It strengthened communications with Apple Messaging and FaceTime; moved to the right and delivered content and apps via iTunes and AppStore.
• Google: Moved to communications (2B+ Android devices) and computing via Android, Chrome, and Google Cloud Platform (GCP); moved right to social and communications via gmail, Hangout (video/conferencing), and Google+.
• Amazon: Moved left to computing via the awe-inspiring Amazon Web Services(AWS) with Microsoft defending its home turf of computing solidly with AzureCloud; moving right with unified communications service, Chime. All this, while massively expanding the home turf of physical and virtual content and services.

Here is a summary of the FMAGA offerings and initiatives in each realm of the 3C’s to show the middle game stage. [Note: A not comprehensive and ever-changing list].

Well, it’s a lot to process for sure, despite simplified categorization in three buckets! There is a lot more we can inventory here based on “moonshot” efforts underway in laboratories.

The battle for the 3C’s is raging hard. It feels like we are still at the cusp of bigger battles to come, built on these lego blocks.

Ma Bell, Big Blue, and FMAGA

Let us not forget last century’s behemoths yet. AT&T and IBM are shown below in their home turfs along with FMAGA.

The new AT&T is clearly attempting relevance in the new world of media and entertainment by acquiring old-world players in content creation and delivery via Time Warner and DirecTV acquisitions. [See comparison of AT&T Ma Bell and Facebook here].

IBM is trying to cope in the new computing world of cloud and AI. Can Big Blue modernize and deliver its mind-boggling array of enterprise software and collaboration products from its cloud as fast as FMAGA? Can it repeat its services-led strategy in a self-service, prefabricated technology world of cloud? Can it use Watson as the rallying cry and umbrella portfolio cover (like say, WebSphere for middleware)?

I hope the old guards give FMAGA a run for their money. Keeping the doors shut from the barbarians won’t be easy. (Note for the ardent game-of-thrones fans: As hopeful as I am about this, I hear a distant echo of the desperate “hold-the-door” cry from Hodor).

FMAGA Next: Greater or Lesser?

Unlike in Game of Thrones, we can’t time-travel to see the next decade. We can either pass off as futurists and make unprovable assertions or attempt some mild observations based on the battle for the 3Cs. One such observation is this:

The battle will intensify in the content corner where technology-enabled services in health, finance, and transportation will drive upstream innovations in computing and communications.

Big pieces of the game are in place for another decade of exciting play between the old guard in tech and industries, FMAGA, Asian giants, and as-yet-unknown new entrants. We won’t be watching from the sidelines, but will be active, if unwitting, participants in it.

—

Originally published on LinkedIn.

Related article: Is Facebook the AT&T of the 21st Century?

The post How the Big 5’s Mega Battle to Control the 3C’s is Unfolding appeared first on Hari Harikrishnan.

Sensors have existed for longer than the internet has, and much longer than the Internet of Things (IoT). Once, canaries sensed danger and martyred themselves in coal-mines. Now drones do sensing in hurricanes and droids do it in nuclear reactors.

While at the sensor conference in San Francisco this year, I was struck by the realization that most of the companies participating have been building sensor technology for decades, pre-dating IoT. Connectivity to the cloud seems to have given sensors a new lease of life.

Since sensors are an essential component of IoT applications, here is a quick look at the evolution of sensors of all shapes and sizes and across industries; from the days of canaries to the age of drones.

Calling All Sensors

Classification of sensors is not easy due to the various ways you can classify them, especially since we are at a time when the very concept of what makes a sensor is evolving.

Sensors are windows from the digital world to our analog world — analog-to-digital (A-to-D) converters really.

Here are a few non-mutually exclusive categories of sensors to show why classification is difficult.

• MEMS Sensors: Almost the grand daddy of sensors, these sensors are based on Microelectromechanical systems (MEMS). This category is not application-specific, but based on the size of of the sensor (micrometer-sized) and how it is constructed (silicon, metal etc.). The sensors we normally hear about such as accelerometers, gyroscopes, microphones, and most biosensors are MEMS-type.
• NEMS Sensors: Miniaturization to nanometer size with new materials is giving rise to Nano-electromechanical systems (NEMS) which will advance the sensing functions and applications even further.
• Bio Sensors: These sense biological responses and operate at molecular level. Many of them are based on MEMS (Bio-MEMS), but they don’t need to be. Examples include implanted glucose monitors to cancer-cell detectors. They could rely on microbes, be light-sensing, or be density-sensing. They could be passive sensors, be wearable like a watch, or be embedded stents in our body.
• Environmental Stimuli based: Examples are sensors that work by sensing light, sound, and physical contact. Optical sensors, voice-activated sensors, ultrasound sensors, motion sensors, pressure sensors, vibration sensors, flow sensors all work based on these analog stimuli.
• Phones: Your smartphone is a sensor. It tracks your movements, identifies your face or thumbprint for security. New applications including passive sensing supplemented by augmented reality (AR) for 3D-sensing of our environment are just around the corner.
• Remote Sensors: Satellites have been serving this function for decades. There is a whole category of remote-sensing satellites that perform tasks from geo-mapping to weather analysis.
• Drones and Robots: We would normally not think of drones as sensors, but most drone applications are for aerial surveying, like the ones used after hurricane Harvey to survey damage by companies like AT&T and the government.
• Humans: When we tell Waze navigation application that there is a pothole on the road or when we snap the picture of a damaged public property and send it to a government agency (as they do routinely in India and Brazil), we are acting as macrosensors.

As you can see, mutually-exclusive categorizations of sensors aren’t easy, simply based on how sensors are constructed.

Sensors require integration of science and technology. They have come to be a metaphor for digitization.

Here is a view of sensors that combine analog and digital technologies, varying in sizes from nanometers to micrometers, and based on how various science and technology disciplines are combined to make them. The menagerie extends from MEMS sensors to satellites to people.

Sensors require integration of science and technology, not just computing and communications technologies. They are literally a metaphor for digitization.

Application-specific Sensors

Unless you are a component maker, the above classifications are not very helpful in choosing sensors. You need to choose them by application type, by functional requirements or by non-functional attributes like longevity of the equipment to which the sensor is attached to.

Sometimes you need composite sensors that do multiple functions in one like this micro-navigation sensor that combines accelerometer, gyroscope, and GPS from Inertial Sense. Mostly your device has many sensors embedded in it.

Examples from select verticals:

Examples from select verticals:

• Oil & Gas: In process manufacturing or resource industries like oil and gas, flow sensors reign supreme. High and low-precision sensors measure the flows of liquid and gas as well as environmental conditions.
• Automotive: Next to an aircraft, perhaps a car has the most sensors and the number is increasing to support autonomous driving. Even before autonomous vehicles arose, every aspect of a car was being measured, from speed to brake pads to fluid levels. Autonomy requires cars to increase sensing to do more external V2V (vehicle-to-vehicle) and V2I (vehicle-to-infrastructure) sensing using technologies like LiDAR.
• Consumer Devices: The smartphone has about 15 sensors, not counting the cameras that can function as sensors. They measure everything from location, proximity, orientation and environmental conditions.
• Healthcare: Sensors here range from non-invasive trackers and wearables to invasive in vivo sensors that can detect cardiac damage or mutated cells.

Finding the right sensor for an application requires expert domain knowledge of the industry. Accuracy requirements often drive up costs exponentially.

Summary

While sensors have existed for longer than the internet itself, sensors have a newfound power with cloud connectivity.

We have come a long way from using canaries as sensors to using drones and droids; from biochemical sensors to mechatronic sensors.

From nano to micro to macro sizes, sensors are artificial eyes feeding our artificial intelligence. Scientific advances in nano sensors and biosensors will unleash a new set of applications that we can only see in science fiction today.

It will take more technology convergence to reach full potential than simply big data and algorithms coming together.

Originally published at www.iotforall.com

The post The Evolution of Sensors: Canaries to Drones appeared first on Hari Harikrishnan.

Your business needs should dictate what your IoT platform is. Not vendor definitions.

They come in all shapes and sizes. Hoards of them. They are called IoT Platforms. They are hard to differentiate. They combine two words that we wax eloquent trying to describe. IoT and Platform.

IoT sits at the intersection of IT and OT. Any IT-centric definition fails to capture OT significance and vice-versa. Hence, rather than provide an academic definition, let me share the practical view of how customers in different industries build and use technology platforms that leverage pervasive connectivity and pervasive computing.

Types of IoT Platforms

I have come across IoT platforms that span this gamut:

• Platform for Industries: These platforms deliver functionality that enables vertical-industry specific capabilities. For example, asset performance and utilization management of industrial equipment, healthcare services platform that enables patient monitoring, etc.
• Platform for Business Operations: These are business function specific. Most common is a platform that enables support and maintenance or managed services.
• Platform for Cities: Cities in general look to optimize city operations or citizen services. For them, the services include parking, lighting, garbage collection and so on. So, city services platform (or city digital platform) IT-enable city’s OT assets.
• Software-only Platforms or Data Platforms: These platforms are most typically referred to as “IoT Platforms”. These help with data flow from “things” to business systems and people. Performs analytics and workflow integration.
• Software and Hardware Platform: If you are using ICT hardware and software (e.g. networking and telecommunications equipment in addition to the above software), your platform is a combo. You could add OT hardware (industrial equipment, medical systems) to this and deem it part of your master IoT platform.

The Universal Platform

If I were to draw a picture that encompassed all those variations, it would look like this:

It is an IT+OT Platform, not a platform based on ICT components alone.

The variations of platforms is due to excluding the OT tier of the cyber-physical systemfrom the above layers, or by limiting the definition to software alone.

Common Functions in the Platform

If we exclude hardware and connectivity challenges and OT systems, we end up with the garden-variety IoT software platform (or IoT Data platform).

It looks like this:

It enables higher-level business function or operational tasks to be performed. Each of the tasks described above could be viewed as its own “platform” such as asset management platform or service assurance platform.

Those variations are task-specific abstractions built on the following five core functions in the IoT software layer:

1. Connect: Ensure bi-directional data flow happens between things and other systems. The “Thing” gets provisioned and managed.
2. Collect: Collect and manage the data flowing from the “thing”.
3. Analyze: Perform simple (e.g. regressions) to complex analysis and AI techniques on the data using streaming data and at-rest data from systems-of-record.
4. Integrate: Take actionable information from the data and feed to enterprise workflows (fully-automated or human-augmented). Expose data or services to 3rd party systems via APIs.
5. Engage: Deliver data to people. (e.g. notifications, dashboards, approval chains). Let them take action based on the data.

Yes, easier said than done. Lots of middleware-sausage-making goes on to make this possible.

How to Build It?

I can visualize companies hiring armies of full-stack developers to build a digital platform. The project gets on the engineering or IT leaderboards both in terms of resources consumed and enterprise-wide visibility, not to mention prestige in being associated with one.

Luckily we don’t have to re-invent and build these capabilities from scratch. Various PaaS offerings have come of age to let us use them to build our business specific platform. The trick is making the right core-vs-context decisions and optimizing the cost of building and operating.

Summary

Salvation comes in many forms. So do IoT platforms.

Your business strategy and how you serve your customers by combining IT and OT dictates how you define what is in your IoT platform. You can then make informed choices and select components for that IoT platform from the plethora of options out there.

In 2017, may you create your own definition of an IoT platform. May this be the last IoT platform post you read about!

Originally published at www.iotforall.com

The post There is No Universal IoT Platform appeared first on Hari Harikrishnan.

AT&T as Ma Bell ruled the roost as the biggest communications company for over a 100 years. It had a plethora of technology inventions, 100 million subscribers, and over a million employees before its break-up in 1984. Today, Facebook has well over 2 billion monthly active users.

Clearly, AT&T and Facebook differ in how they enable communications. Despite many differences, there are similarities between the former with roots in the 19th century and the 21st century infant.

What are their similarities and differences? What are the lessons from AT&T for the 21st century behemoths in Information and Communication Technology (ICT) and Media industry?

Ma Bell: More Than the Mother of Talk

It is easy to be awed by AT&T’s telecommunications prowess alone and miss the true strength of AT&T. Over the course of the 20th century, AT&T’s influence went well beyond communications.

Here is a glimpse of its power over the last century in technology assets beyond their business which was legally restricted to telecom before 1984. These are categorized into 4 areas:

1. Communications: Classic telecom/phone services to modern day communications
2. Computing: Personal, business, and cloud-based
3. Content: Creation and delivery of content and content services
4. Common R&D: Everything from semiconductor to information theory

[Note: This is not a comprehensive list. A great graphic from New York Times is here]

Some takeaways across technology and business:

• STEM Breadth and Depth: AT&T’s technology expertise ranged from scientific research to applied technology, thanks to Bell Labs. Their assets spanned communications (core business) to computing, content, semiconductor, and basic sciences. Inventions like the transistor paved the way for ICs and modern computers; Unix and C paved the way for distributed systems and Internet.
• Barred from Computing: AT&T held a treasure trove of patents across communications and computing. The 1956 consent decree with the government prevented AT&T from entering the computer business while preserving its monopoly in telecom. Western Electric division built and sold communications equipment.
• Enters Computing and Leaves: Divestiture in 1984 – a seemingly clever idea then – split AT&T up, but gave it the rights to enter other markets including computers. Heralded as an unshackling of the giant by some, the market expansions for the information age failed to materialize. Their hostile takeover and renaming of NCR to AT&T Global Information Services lasted 4 years, only to be spun out.

Against that backdrop, let’s see how AT&T and Facebook compares across the three dimensions of communications, computing, and content.

Communications: AT&T vs. Facebook

AT&T had over 100 million subscribers in a world without cellular wireless communications and low tele-density. In a world with high wireless penetration, Facebook community has over 2 billion monthly active users (MAU). Its assets WhatsApp and Facebook Messenger has over 1 billion MAUs each (Note: These users overlap across the services and hence can’t be summed up).

It was simple to count AT&T’s subscribers based on lines in use. Now we count usage, not simply registered users. Underlying connectivity technologies and tele-density in their eras make direct comparison of the numbers alone meaningless. Hence, these numbers simply illustrate the magnitude and impact of each in their epoch.

Facebook’s Telecom Infrastructure Project (TIP) aims to drive new approaches to telecom network implementation in a world of wireless connectivity, 5G, and IoT. Aquila is a solar-powered drone initiative that beams connectivity. These are just two of the initiatives aimed at global connectivity.

Computing: AT&T vs. Facebook

AT&T desired to monetize its computing and information services business, but prevented by regulation from 1956 to 1984. It had the technologies like transistors, Unix, C etc. that eventually propelled the drive to the Internet.

For Facebook, computing and telecom network are simply enablers, not direct money-makers.

Unlike its contemporary mega peers (Microsoft, Amazon, Google), Facebook is not offering a computing service. It is focused on lowering the costs to build and operate compute and network infrastructure. Facebook exerts considerable influence in cloud computing architectures via the Open Compute project (OCP).

TIP is for communications what OCP is for computing. OCP and internal efforts like FBAR (Facebook Automated Remediation) to automate data center operations drive profits by keeping opex low and service availability high.

Content: AT&T vs. Facebook

In AT&T’s prime, analog broadcast TV was the norm. Media and telecom weren’t converged as we know it today. Most Bell Labs’ innovations were enablers for content creation and distribution. Facebook has just started to offer digital content through its Watch platform, moving it from offering user-generated content alone to professional, branded media.

A comparison of Facebook with the AT&T reincarnation, “at&t”, and its pending acquisition of Time Warner is below, juxtaposed on top of the communications assets used to deliver them.

AT&T buys Time Warner just to keep up with the Joneses (e.g. Comcast and NBC, Verizon and Yahoo)? Unlikely.

The new AT&T may be preparing for a larger battle looming in the Telecom, Media, and Entertainment world.

AT&T and Facebook: Apples and Oranges?

What makes AT&T dissimilar from Facebook?

• Monetization: AT&T and Facebook are both communications service providers. The main difference is that AT&T monetized the communication services directly while Facebook monetizes them indirectly via advertisements.
• Innovation: AT&T brought inventions to market. Phone being the prime example. Facebook on the other hand is (so far) applying technology for the outcomes of social connectivity. This is also a reflection of their times.
• Vertical Integration: From Bell Labs to Western Electric, AT&T was vertically integrated from pure research to businesses. Facebook’s vertical integration from communications to networking and computing is via TIP and OCP. Their content to communications integration is just starting.
• Competition: Formidable competitors stand between Facebook and the next 100 years. AT&T dealt with a different competitive and regulatory environment.

Facebook = AT&T Redux?

The historical and current picture of their vast swathes of business is below. Given the similarities at the communication solution level, is Facebook really a second-coming of AT&T? Is it a progression from Grand Dame to Lady Belle?

The post Is Facebook the AT&T of the 21st Century? appeared first on Hari Harikrishnan.

I am often asked what the standards for IoT are. Is it MQTT or AMQP? What about ZigBee or Z-Wave? Is it 6LoWPAN or LoRa and the LPWAN variations? Do we start with an OSI 7-layer framework? What about the long-standing OT (operational technologies) protocols of the industrial world, like OPC, CIP, or FieldBus?

In this article, we’ll take a look at the evolving world of IoT standards using a cyber-physical systems approach with three steps:

1. See sample interactions in a complex cyber-physical system (CPS)
2. Look at how to choreograph the interactions for outcomes
3. See a sampling of protocols needed for the interactions across IT and OT

IoT makes cyber-physical systems (CPS) possible. CPS includes autonomous vehicles, medical systems, process control, robotics and so on — wherever physical systems (“things”) and cyber systems (IT-based systems) are combined. If we have to choreograph the interactions for outcomes, we need to take an IT + OT approach to protocols.

The intent here is not to list every protocol, but show the broad categories and their evolution. For details on specific IT protocols, this page has a good list.

IoT Is All About Interactions

At the core of IoT are interactions where information is exchanged between physical or virtual end points. Below is an example of a cyber-physical system and the interactions between the various actors in the system:

In this case of connected cars, we have to orchestrate interactions across different entities; interactions between the people and business entities (B2B/B2C/P2P) and interactions between the physical and virtual entities (M2X/V2X/App2App). It is a veritable X2X world out there!

Control and Automation — The Ultimate Goal

The goal of fostering interactions and integrating physical things with cyber infrastructure is to monitor and control things. Once we have control, we can automate workflows, big and small, across humans and things.

How we do that using various technologies at our disposal looks like this:

From left to right, this is a progression of connecting our assets (physical or people), to processing data, to automating simple tasks, to coordinating complex workflows.

Two aspects of such a system stand out in this assets-to-automation progression.

Control and Communication

To build complex cyber-physical systems that interact, we need to control the systems and enable workflows. We do this by communicating securely with the things we want to control. [I’ll skip security discussion except to note that some level of security is often embedded in communication and control protocols themselves.]

The Protocols and Evolution

Now that we’ve covered the why of protocols, here is a bird’s-eye view of IT / ICT (Information and Communication Technologies) and OT protocols categorized broadly into control vs. communication and roughly mapped to our favourite OSI 7-layer model:

As I said, this is not a comprehensive list, but a simplified categorization to show the world of IoT protocols and its evolution.

Notes and key takeaways from this:

• OT protocol suites often cover physical, data link and app layers of the OSI stack. So I’ve categorized OT protocols into control (app tier) and communication.
• OT standards need to deliver synchronization and determinism compared to IT. Reason is simply this: Actuator actions can’t be non-determinstic. e.g. The autonomous car needs to stop in milliseconds.
• ICT WAN standards are bifurcating into high-power/high-bandwidth and low-power/low-bandwith to cater to people and things.
• Most ICT WAN protocols aid in “sensing” not in “actuating.” For example, if you are doing telesurgery using a robotic arm, make sure the quality-of-service on that connection is OT-standard! Alternatively, plan for edge computing if you are pairing LPWAN with an actuator.
• Satellite communications: Don’t underestimate what Iridium or Else can do for IoT.
• Vertical-specific protocols and interoperability standards: Expect these to evolve like HL7 or Digital Imaging and Communications in Medicine (DICOM) at the application layers.
• Both IT and OT worlds have a lot to learn from each other on evolving the standards for a cyber-physical world. Industrial Ethernet is just one example of this cross-pollination.

Summary

IoT protocols aren’t limited to internet protocols or the ones that IT domain created. A plethora of standards exist in OT and are tailored for specialized OT applications from production floor control to subsystem connectivity in cars. OT and IT protocols evolved separately with some borrowing from each other.

As IoT evolves, both IT and OT experts need to architect cyber-physical systems by cross-pollinating knowledge across both domains.

Originally posted at iotforall.com

The post A Cyber-Physical Systems Approach to IoT Standards appeared first on Hari Harikrishnan.