Deloitte’s IoT “Information Value Loop”: critical attitudinal shift

Posted on 1st April 2015 in collaboration, communication, data, Essential Truths, Internet of Things, M2M, management, marketing, open data, paradigm shift, strategy

Ever so often it’s good to step back from the day-to-day minutia of current Internet of Things projects, and get some perspective on the long-term prospects and challenges.

That’s what Deloitte did last December, when it held an “Internet of Things Grand Challenge Workshop,” with a focus on the all-important “forging the path to revenue generation.”

The attendees included two of my idols: John Seely Brown and John Hagel, of Deloitte’s “Center for the Edge” (love the pun in that title!).

The results were recently released, and bear close examination, especially the concept of how to foster what they call the “Information Value Loop”:

Deloitte IoT Information Value Loop

Deloitte IoT Information Value Loop

“The underlying asset that the IoT creates and exploits is information, yet we lack a well- developed, practical guide to understand how information creates value and how companies can effectively capture value. The ‘Information Value Loop’ describes how information creates value, how to increase that value, and how understanding the relevant technology is central to positioning an organization to capture value. The Information Value Loop is one way to begin making sense of the changes we face. The Loop consists of three interconnected elements: stages, value drivers, and technologies. Where the stages and value drivers are general principles defining if and how information creates value under any circumstances, it is the specifics of today’s technology that connect the Loop to the challenges and opportunities created by the IoT.”

This fits nicely with one of my IoT Esssential Truths,” that we need to turn linear information flows into cyclical ones to fully capitalize on the IoT.  No pussy-footin’ about this for these guys: “For information to create any value at all, it must pass through all the stages of the Loop. This is a binary outcome: should the flow of information be blocked completely at any stage, no value is created by that information.”

IMHO, this is also going to be one of the biggest challenges of the IoT for management: in the days when it was sooo difficult to gather and disseminate information, it made sense for those in the C-suite to control it, and parcel out what they felt was relevant, to whom and when they felt it was relevant. More often than not, the flow was linear and hierarchical, with one information silo in the company handing on the results to the next after they’d processed it. That didn’t allow any of the critical advantages the IoT brings, of allowing everyone who needs it to share real-time data instantly.  But saying we need to change those information management practices is one thing: actually having senior management give up their gatekeeper functions is another, and shouldn’t be understated as a challenge.

So here are some of the other key points in the conference proceedings:

  • In line with the multi-step strategy I outlined in Managing the Internet of Things Revolution, they concluded that incremental improvements to existing processes and products are important, but will only take you so far, at which point radical innovation will be crucial: “At first blush, the early IoT emphasis on sustaining innovation seems reasonable. Performance and cost improvement are seldom absent from the priorities of stakeholders; they are relatively easy to measure and their impact is likely more immediate than any investment that is truly disruptive. Put simply, the business case for an IoT application that focuses on operational efficiencies is relatively easy to make. Many decision makers are hard-wired to prefer the path of less resistance and, for many, truly innovative IoT applications seem too far-flung and abstract to risk pursuing. Still, organizations cannot innovate from the cost side forever.”
  • Melding the public and private, “Cities have inherent societal challenges in place to serve as natural incubators of IoT solutions.” Yeah!
  • As in everything else, those contrarian Millennials (who aren’t so hung up on buying stuff and often prefer to just use it)  are likely to save us when it comes to the IoT:  “From an innovation perspective … some of the new technologies are first marketed at the consumers. Thus, many believe that near-term innovation in IoT applications will come out of the consumer sector – spurred by the emergence of the tech-savvy Millennial consumers as a driving economic force.”
  • As I’ve written before, while some customers will still prefer to buy products outright, the IoT will probably bring a shift from selling products to marketing services based on those products, creating new revenue streams and long-term relationships with customers: “As IoT makes successful forays into the world of consumer and industrial products, it may radically change the producer—buyer transactional model from one based on capital expenditure to one based on operating expenditure. Specifically, in a widely adopted IoT world, buyers may be more apt to purchase product service outcomes on some kind of “per unit” basis, rather than the product itself and in so doing, render the physical product as something more of an afterthought. The manufacturer would then gradually transform into a service provider, operating on a complete awareness of each product’s need for replenishment, repair, replacement, etc.”

    Or, a hybrid model may emerge: “What may ultimately happen in a relatively connected product world is that many may accept the notion of the smartly connected product, but in a limited way. Such people will want to own the smartly connected product outright, but will also accept the idea of sharing the usage data to the limited extent that the sellers use such data in relatively benign ways, such as providing advice on more efficient usage, etc. The outcome here will also rely upon a long term total cost of ownership (TCO) perspective. With any fundamental purchasing model changes (as is taking place in owned vs. cloud resources in the network / IT world), not all suppliers will be able to reap additional economic benefit under the service model. Buyers will eventually recognize the increase in TCO and revert back to the more economical business model if the economic rents are too high.”

  • It’s likely that those players in the IoT ecosystem who create value-added data interpretation will be the most valuable and profitable: “…are certain building blocks of the IoT network “more equal” than others?

    “Some have argued that the holy grail of the IoT value loop resides in the data and that those in the IoT ecosystem who aggregate and transform massive amounts of raw data into commercially useful intelligence capture the real value in the IoT environment. This notion holds that commercially useful data provide insights that drive action and ultimately represent the reason that the end user pursues a smart solution in the first place. Put another way, the end customer is more apt to pay for a more comprehensive treatment of raw data than for a better sensor. Indeed, some even believe that as time passes, the gap in relative value captured by those who curate and analyze the data and the rest of the IoT ecosystem will only widen and that, on a long-term basis, players within the “non-data” part of the IoT ecosystem will need to develop some data analytics capabilities simply to differentiate themselves as something more than commodity providers. Of course, some think that the emphasis on data is overblown and argue that where the real value in the IoT ecosystem is captured depends on application. Time will tell of course. But there can be little doubt that the collection and enhancement of data is highly coveted, and analytics and the ability to make use of the vast quantities of information that is captured will serve as critical elements to virtually any IoT solution.”

I urge you to download and closely analyze the entire report. It’s one of the most thoughtful and visionary pieces of IoT theory I’ve seen (no doubt because of its roundtable origins: in keeping with the above-mentioned need for cyclical information flow for the IoT [and, IMHO, creativity in general], the more insights you can bring together on a real-time basis, the richer the outcome. Bravo!

 

comments: Comments Off on Deloitte’s IoT “Information Value Loop”: critical attitudinal shift tags: , , , , , ,

The Internet of Things’ Essential Truths

Posted on 17th March 2015 in collaboration, communication, data, Essential Truths, Internet of Things, M2M, management, manufacturing, marketing, open data, paradigm shift, privacy, security, strategy, supply chain

I’ve been writing about what I call the Internet of Things’ “Essential Truths” for three years now, and decided the time was long overview to codify them and present them in a single post to make them easy to refer to.

As I’ve said, the IoT really will bring about a total paradigm shift, because, for the the first time, it will be possible for everyone who needs it to share real-time information instantly. That really does change everything, obliterating the “Collective Blindness” that has hampered both daily operations and long-term strategy in the past. As a result, we must rethink a wide range of management shibboleths (OK, OK, that was gratuitous, but I’ve always wanted to use the word, and it seemed relevant here, LOL):

  1. First, we must share data. Tesla leads the way with its patent sharing. In the past, proprietary knowledge led to wealth: your win was my loss. Now, we must automatically ask “who else can use this information?” and, even in the case of competitors, “can we mutually profit from sharing this information?” Closed systems and proprietary standards are the biggest obstacle to the IoT.
  2. Second, we must use the Internet of Things to empower workers. With the IoT, it is technically possible for everyone who could do their job better because of access to real-time information to share it instantly, so management must begin with a new premise: information should be shared with the entire workforce. Limiting access must be justified.
  3. Third, we must close the loop. We must redesign our data management processes to capitalize on new information, creating continuous feedback loops.
  4. Fourth, we must rethink products’ roles. Rolls-Royce jet engines feed back a constant stream of real-time data on their operations. Real-time field data lets companies have a sustained dialogue with products and their customers, increasingly allowing them to market products as services, with benefits including new revenue streams.
  5. Fifth, we must develop new skills to listen to products and understand their signals. IBM scientists and medical experts jointly analyzed data from sick preemies’ bassinettes & realized they could diagnose infections a day before there was any visible sign. It’s not enough to have vast data streams: we need to understand them.
  6. Sixth, we must democratize innovation. The wildly-popular IFTTT web site allows anyone to create new “recipes” to exploit unforeseen aspects of IoT products – and doesn’t require any tech skills to use. By sharing IoT data, we empower everyone who has access to develop new ways to capitalize on that data, speading the IoT’s development.
  7. Seventh, and perhaps most important, we must take privacy and security seriously. What responsible parent would put an IoT baby monitor in their baby’s room after the highly-publicized incident when a hacker exploited the manufacturer’s disregard for privacy and spewed a string of obscenities at the baby? Unless everyone in the field takes privacy and security seriously, the public may lose faith in the IoT.

There you have ’em: my best analysis of how the Internet of Things will require a revolution not just in technology, but also management strategy and practices. What do you think?

comments: 3 » tags: , , , , , , ,

Remember: The IoT Is Primarily About Small Data, Not Big

Posted on 16th March 2015 in data, Internet of Things, M2M, management, manufacturing, open data

In one of my fav examples of how the IoT can actually save lives, sensors on only eight preemies’ incubators at Toronto’s Hospital for Sick Children yield an eye-popping 90 million data points a day!  If all 90 million data points get relayed on to the “data pool,” the docs would be drowning in data, not saving sick preemies.

Enter “small data.”

Writing in Forbes, Mike Kavis has a worthwhile reminder that the essence of much of the Internet of Things isn’t big data, but small. By that, he means:

“a dataset that contains very specific attributes. Small data is used to determine current states and conditions  or may be generated by analyzing larger data sets.

“When we talk about smart devices being deployed on wind turbines, small packages, on valves and pipes, or attached to drones, we are talking about collecting small datasets. Small data tell us about location, temperature, wetness, pressure, vibration, or even whether an item has been opened or not. Sensors give us small datasets in real time that we ingest into big data sets which provide a historical view.”

Usually, instead of aggregating  ALL of the data from all of the sensors (think about what that would mean for GE’s Durathon battery plant, where 10,000 sensors dot the assembly line!), the data is originally analyzed at “the edge,” i.e., at or near the point where the data is collected. Then only the data that deviates from the norm (i.e., is significant)  is passed on to to the centralized data bases and processing.  That’s why I’m so excited about Egburt, and its “fog computing” sensors.

As with sooo many aspects of the IoT, it’s the real-time aspect of small data that makes it so valuable, and so different from past practices, where much of the potential was never collected at all, or, if it was, was only collected, analyzed and acted upon historically. Hence, the “Collective Blindness” that I’ve written about before, which limited our decision-making abilities in the past. Again, Kavis:

“Small data can trigger events based on what is happening now. Those events can be merged with behavioral or trending information derived from machine learning algorithms run against big data datasets.”

As examples of the interplay of small and large data, he cites:

  • real-time data from wind turbines that is used immediately to adjust the blades for maximum efficiency. The relevant data is then passed along to the data lake, “..where machine-learning algorithms begin to understand patterns. These patterns can reveal performance of certain mechanisms based on their historical maintenance record, like how wind and weather conditions effect wear and tear on various components, and what the life expectancy is of a particular part.”
  • medicine containers with smart labels. “Small data can be used to determine where the medicine is located, its remaining shelf life, if the seal of the bottle has been broken, and the current temperature conditions in an effort to prevent spoilage. Big data can be used to look at this information over time to examine root cause analysis of why drugs are expiring or spoiling. Is it due to a certain shipping company or a certain retailer? Are there re-occurring patterns that can point to problems in the supply chain that can help determine how to minimize these events?”

Big data is often irrelevant in IoT systems’ functioning: all that’s needed is the real-time small data to trigger an action:

“In many instances, knowing the current state of a handful of attributes is all that is required to trigger a desired event. Are the patient’s blood sugar levels too high? Are the containers in the refrigerated truck at the optimal temperature? Does the soil have the right mixture of nutrients? Is the valve leaking?”

In a future post, I’ll address the growing role of data scientists in the IoT — and the need to educate workers on all levels on how to deal effectively with data. For now, just remember that E.F. Schumacher was right: “small is beautiful.”

 

comments: Comments Off on Remember: The IoT Is Primarily About Small Data, Not Big tags: , , , , , , , , ,

IBM picks for IoT trends to watch this year emphasize privacy & security

Posted on 23rd February 2015 in collaboration, data, Internet of Things, M2M, management, privacy, security, strategy, Uncategorized

Last month Bill Chamberlin, the principal analyst for Emerging Tech Trends and Horizon Watch Community Leader for IBM Market Development (hmmm, must have an oversized biz card..) published a list of 20 IoT trends to watch this year that I think provide a pretty good checklist for evaluating what promises to be an important period in which the IoT becomes more mainstream.

It’s interesting to me, especially in light of my recent focus on the topics (and I’ll blog on the recent FTC report on the issue in several days), that he put privacy and security number one on the list, commenting that “Trust and authentication become critical across all elements of the IoT, including devices, the networks, the cloud and software apps.” Amen.

Most of the rest of the list was no surprise, with standards, hardware, software, and edge analytics rounding out the top five (even though it hasn’t gotten a lot of attention, I agree edge analytics are going to be crucial as the volume of sensor data increases dramatically: why pass along the vast majority of data, that is probably redundant, to the cloud, vs. just what’s a deviation from the norm and probably more important?).

Two dealing with sensors did strike my eye:

“9.  Sensor fusion: Combining data from different sources can improve accuracy. Data from two sensors is better than data from one. Data from lots of sensors is even better.

10.  Sensor hubs: Developers will increasingly experiment with sensor hubs for IoT devices, which will be used to offload tasks from the application processor, cutting down on power consumption and improving battery life in the devices”

Both make a lot of sense.

One was particularly noteworthy in light of my last post, about the Gartner survey showing most companies were ill-prepared to plan and launch IoT strategies: “14.  Chief IoT Officer: Expect more senior level execs to be put in place to build the enterprise-wide IoT strategy.” Couldn’t agree more that this is vital!

Check out the whole list: I think you’ll find it helpful in tracking this year’s major IoT developments.

comments: Comments Off on IBM picks for IoT trends to watch this year emphasize privacy & security tags: , , , , , , , , ,

Gartner study confirms senior managers don’t understand IoT

Posted on 21st February 2015 in Internet of Things, M2M, management, manufacturing, marketing, strategy

The “Managing the Internet of Things Revolution” e-guide I wrote for SAP was aimed at C-level executives. Even though it’s proven popular enough that the company is translating it into several languages, it appears we need to redouble our efforts to Managing_the_Internet_of_Things_Revolutionbuild IoT awareness among executives.

I say that because Gartner has just come out with a survey confirming my suspicions: even though a lot of companies now think the IoT will have a major effect on them, they’re clueless about how to manage it and most have yet to launch major IoT initiatives.

In fact, “many survey respondents felt that the senior levels of their organizations don’t yet have a good understanding of the potential impact of the IoT.” (my emphasis)

 

That’s despite the fact that a key conclusion of my guide was that (even though the IoT is a long way from full maturity) companies can and should begin their IoT strategies and implementation now, because they can already achieve significant savings in operating costs, improve marketing, and create new revenue streams with the current early stage sensors and analytical tools. Getting started will also build their confidence and familiarity with IoT tools and strategy before they begin more dramatic transformational strategies.

Consider these findings from the survey of 463 business and IT leaders:

  • 40% of companies think the IoT will at least bring new short-term revenue and cost reduction opportunities in the next three years — or perhaps even transform them. More than 60% think that will be true over 5 years or more.
  • Fewer than 25% said their company had “established clear business leadership for the IoT,” — even among the companies predicting a significant  – this includes those who said they expect the IoT to have a significant or transformational impact, says Gartner (however, 35% of them came from this group).
  • Yet, few have delegated specific responsibility for IoT strategy and management: “… less than one-quarter of survey respondents has established clear business leadership for the IoT, either in the form of a single organizational unit owning the issue or multiple business units taking ownership of separate IoT efforts.”
  • “attitudes toward the IoT vary widely by industry. For example, board of directors’ understanding of the IoT was rated as particularly weak in government, education, banking and insurance, whereas the communications and services industries scored above-average ratings for senior executive understanding of the IoT.”

Gartner concluded most companies have yet to really create IoT strategies:

“‘The survey confirmed that the IoT is very immature, and many organizations have only just started experimenting with it,’ said Nick Jones, vice president and distinguished analyst at Gartner. ‘Only a small minority have deployed solutions in a production environment. However, the falling costs of networking and processing mean that there are few economic inhibitors to adding sensing and communications to products costing as little as a few tens of dollars. The real challenge of the IoT is less in making products ‘smart’ and more in understanding the business opportunities enabled by smart products and new ecosystems.’ However, a lack of clear business or technical leadership is holding back investment in the technology.” (my emphasis)

In line with my current preoccupation, privacy and security, the survey did show companies are concerned with both issues, as well as with finding talented new staff who understand the IoT and how to benefit from it. According to Steve Kleyhans, Gartner’s research vp:

 “While a single leader for the IoT is not essential, leadership and vision are important, even in the form of several leaders from different business units. We expect that over the next three years, more organizations will establish clear leadership, and more will recognize the value of some form of an IoT center of excellence because of the need to master a wide range of new technologies and skills.”

If you haven’t launched any IoT projects or begun to create a strategy, the writing’s on the wall: get going!

Carpe diem: I take this survey as an omen that there’s a desperate need for When Things Can Talk: profiting from the Internet of Things revolution,” my proposed full-length book on IoT corporate strategy. Let me know if you can suggest a possible publisher!

comments: 1 » tags: , , , , , ,

Management Challenge: Lifeguards in the IoT Data Lake

Posted on 20th January 2015 in collaboration, data, Essential Truths, Internet of Things, M2M, management, manufacturing, open data, paradigm shift, strategy, supply chain

In their Harvard Business Review November cover story, How Smart, Connected Products Are Transforming Competition, PTC CEO Jim Heppelmann and Professor Michael Porter make a critical strategic point about the Internet of Things that’s obscured by just focusing on IoT technology: “…What makes smart, connected products fundamentally different is not the internet, but the changing nature of the “things.”

In the past, “things” were largely inscrutable. We couldn’t peer inside massive assembly line machinery or inside cars once they left the factory, forcing companies to base much of both strategy and daily operations on inferences about these things and their behavior from limited data (data which was also often gathered only after the fact).

Now that lack of information is being removed. The Internet of Things creates two unprecedented opportunities regarding data about things:

  • data will be available instantly, as it is generated by the things
  • it can also be shared instantly by everyone who needs it.

This real-time knowledge of things presents both real opportunities and significant management challenges.

Each opportunity carries with it the challenge of crafting new policies on how to manage access to the vast new amounts of data and the forms in which it can be accessed.

For example: with the Internet of Things we will be able to bring about optimal manufacturing efficiency as well as unprecedented integration of supply chains and distribution networks. Why? Because we will now be able to “see” inside assembly line machinery, and the various parts of the assembly line will be able to automatically regulate each other without human intervention (M2M) to optimize each other’s efficiency, and/or workers will be able to fine-tune their operation based on this data.

Equally important, because of the second new opportunity, the exact same assembly line data can also be shared in real time with supply chain and distribution network partners. Each of them can use the data to trigger their own processes to optimize their efficiency and integration with the factory and its production schedule.

But that possibility also creates a challenge for management.

When data was hard to get, limited in scope, and largely gathered historically rather than in the moment, what data was available flowed in a linear, top-down fashion. Senior management had first access, then they passed on to individual departments only what they decided was relevant. Departments had no chance to simultaneously examine the raw data and have round-table discussions of its significance and improve decision-making. Everything was sequential. Relevant real-time data that they could use to do their jobs better almost never reached workers on the factory floor.

That all potentially changes with the IoT – but will it, or will the old tight control of data remain?

Managers must learn to ask a new question that’s so contrary to old top-down control of information: who else can use this data?

To answer that question they will have to consider the concept of a “data lake” created by the IoT.

“In broad terms, data lakes are marketed as enterprise wide data management platforms for analyzing disparate sources of data in its native format,” Nick Heudecker, research director at Gartner, says. “The idea is simple: instead of placing data in a purpose-built data store, you move it into a data lake in its original format. This eliminates the upfront costs of data ingestion, like transformation. Once data is placed into the lake, it’s available for analysis by everyone in the organization.”

Essentially, data that has been collected and stored in a data lake repository remains in the state it was gathered and is available to anyone, versus being structured, tagged with metadata, and having limited access.

That is a critical distinction and can make the data far more valuable, because the volume and variety will allow more cross-fertilization and serendipitous discovery.

At the same time, it’s also possible to “drown” in so much data, so C-level management must create new, deft policies – to serve as lifeguards, as it were. They must govern data lake access if we are to, on one hand, avoid drowning due to the sheer volume of data, and, on the other, to capitalize on its full value:

  • Senior management must resist the temptation to analyze the data first and then pass on only what they deem of value. They too will have a crack at the analysis, but the value of real-time data is getting it when it can still be acted on in the moment, rather than just in historical analyses (BTW, that’s not to say historical perspective won’t have value going forward: it will still provide valuable perspective).
  • There will need to be limits to data access, but they must be commonsense ones. For example, production line workers won’t need access to marketing data, just real-time data from the factory floor.
  • Perhaps most important, access shouldn’t be limited based on pre-conceptions of what might be relevant to a given function or department. For example, a prototype vending machine uses Near Field Communication to learn customers’ preferences over time, then offers them special deals based on those choices. However, by thinking inclusively about data from the machine, rather than just limiting access to the marketing department, the company shared the real-time information with its distribution network, so trucks were automatically rerouted to resupply machines that were running low due to factors such as summer heat.
  • Similarly, they will have to relax arbitrary boundaries between departments to encourage mutually-beneficial collaboration. When multiple departments not only share but also get to discuss the same data set, undoubtedly synergies will emerge among them (such as the vending machine ones) that no one department could have discovered on its own.
  • They will need to challenge their analytics software suppliers to create new software and dashboards specifically designed to make such a wide range of data easily digested and actionable.

Make no mistake about it: the simple creation of vast data lakes won’t automatically cure companies’ varied problems. But C-level managers who realize that if they are willing to give up control over data flow, real-time sharing of real-time data can create possibilities that were impossible to visualize in the past, will make data lakes safe, navigable – and profitable.

comments: Comments Off on Management Challenge: Lifeguards in the IoT Data Lake tags: , , , , , , , ,

Good Checklist for Creating #IoT Strategy

Posted on 15th January 2015 in design, Essential Truths, Internet of Things, M2M, management, paradigm shift, strategy

Still not ready to tackle an analysis of the November Harvard Business Review cover story, by PTC CEO Jim Heppelmann and Professor Michael Porter, on How Smart, Connected Products Are Transforming Competition, but I did want to do a shout-out to a companion piece, Digital Ubiquity: How Connections, Sensors, and Data Are Revolutionizing Business, by two HBS profs, Marco Iansiti and Karim R. Lakhani.

In particular, I wanted to suggest that you use the last section of the paper, “Approaching Digital Ubiquity,” as a checklist of priorities to create your own IoT strategy (I’d be remiss if I didn’t also mention my “Managing the Internet of Things Revolution” i-guide and this blog’s “Essential Truths” as references as well..).

Here are their points, and my reflections on them:

  1. Apply the digital lens to existing products and services.
    This is a profound transformation, because we’ve become so accustomed to working around the gaps in our knowledge that were the reality in an analog world.As Iasanti and Lakhani say, you now need to ask:
    “What cumbersome processes in your business or industry are amenable to instrumentation and connectivity?
    Which ones are most challenging to you or your customers?”
  2. Connect your existing assets across companies.
    We “get” competition, but collaboration, especially with competitors, is a little less instinctive.

    “If you work in a traditional analog setting, examine your assets for new opportunities and look at other industries and the start-up world for new synergies. Your customer connections are especially valuable, as are your knowledge of customers’ needs and the capabilities you built to meet knowledge of customers’ needs and the capabilities you built to meet them. Nest is connecting with public utilities to share data and optimize overall energy usage. If you work in a start-up, don’t just focus on driving the obsolescence of established companies. Look at how you can connect with and enhance their value and extract some of it for yourself.knowledge of customers’ needs and the capabilities you built to meet them. Nest is connecting with public utilities to share data and optimize overall energy usage. [my note: this is a great example of thinking expansively: even though your product is installed in individual homes, if data can be aggregated from many homes, it can be of real value on a macro scale as well. The smart grid is a great example of bringing all components of energy production, distribution, and use together into an integrated system.]  If you work in a start-up, don’t just focus on driving the obsolescence of established companies. Look at how you can connect with and enhance their value and extract some of it for yourself.”

  3. Examine new modes of value creation.
    Just because you make tangible products doesn’t mean that you’re limited to just selling those products to make money in the future. You’ll be able to make money by selling customers actionable data that will allow them to improve productivity and reduce maintenance. Perhaps you’ll stop selling altogether, and make money instead by making your products the cornerstone of profitable services.

    Begin to ask:
    “What new data could you accumulate, and where could you derive value from new analytics?”
    “How could the data you generate enable old and new customers to add value?”

  4. Consider new value-capture modes.
    “Could you do a better job of tracking the actual value your business creates for others?”
    “Could you do a better job of monetizing that value, through either value-based pricing or outcomes-based models?”

  5. Use software to extend the boundaries of what you do.
    You will still make products, as in the past, and that gives you a tangible basis for the future. But you’ll need a digital component as well.

    “Digital transformation does not mean that your company will only sell software, but it will shift the capability base so that expertise in software development becomes increasingly important. And it won’t render all traditional skills obsolete. Your existing capabilities and customer relationships are the foundations for new opportunities. Invest in software-related skills that complement what you have, but make sure you retain those critical foundations. Don’t jettison your mechanical engineering wizards—couple them with some bright software developers so that you can do a better job of creating and extracting value.”

    What do you think?  Any more questions you’d add? Let me know!

comments: Comments Off on Good Checklist for Creating #IoT Strategy tags: , , , ,

IoT: What Can You Do That You Couldn’t? Heavy Construction

Posted on 8th January 2015 in collaboration, communication, Essential Truths, Internet of Things, M2M, maintenance, management

Not sure why, but I’m particularly fascinated by how the IoT can transform parts of the economy that have been around for more than 100 years, such as the way the Union Pacific uses it to reduce derailments — and worse.

One of those tradition-bound industries where the IoT Essential Truth “What Can You Do Now That You Couldn’t Do Before” is starting to revolutionize both daily practices and strategy is heavy construction, both for buildings and public works.

First of all, heavy construction is inherently dangerous, so anything that can be done to manage that danger is beneficial.

Lots of very heavy machinery; many people, frequently on foot; almost impossible to coordinate all of them in the past, especially as vehicles enter and leave the site.  According to OSHA, in the US alone, 796, or 20.3% of all workers killed on the job in 2003 were killed on construction sites, primarily through falls, struck by objects, electrocution or “caught-in-between.” Of those, lack of coordination probably resulted in most of the struck by objects and “caught-in-between” deaths.

One of the most exciting developments in that regard is SAP’s demonstration program with SK Solutions, which makes anti-collision software, on a construction site in Dubai. They are capitalizing on new construction cranes and construction vehicle  that have sensors built in so their real-time location can be determined instantly. SAP and SK Solutions combine sensor-based data – such as 3-D motion control, location, load weight, equipment usage and wind speed – to avoid collisions with trucks  to enhance worker safety, improve productivity and reduce costs. The site and project managers monitor the equipment via a dashboard.

Less dramatic than collision avoidance is the way that construction companies are using real-time data from the equipment to maximize operating efficiency and reduce maintenance costs through innovations such as “predictive maintenance.”  As my Boston IoT MeetUp co-director Chris Rezendes of INEX Advisors discussed at the recent Association of Equipment Management Professionals Asset Management Symposium, “instrumentation of assets” through digital plans and models, sensors, data and embedded communication devices in buildings and bridges is becoming a key differentiator in the industry. According to Rezendes:

““Everybody in tech wants to instrument your assets, inventories, operations, people and processes… They are looking at instrumenting all manner of industrial machines, equipment and more. And they’re doing it really well…. You should feel threatened, at least a little bit, by big technology companies trying to instrument your assets for you, maybe to you… I’m going to tell it to you straight: He or she who controls the intelligence–the data about those assets, inventories and areas of operation–will control that market, the customer, the regulatory environment and the supply chain. They will control you.”

What a seismic shift from the old days of heavy construction, which was largely a matter of brute force and difficult demands on operators to remain always vigilant in the midst of loud noises.  Add in the sensors that these construction crews are now embedding in bridges’ structure and in buildings to monitor a wide range of stresses and environmental conditions, and the conclusion is inescapable: every industry can and will be fundamentally altered in the coming decade as equipment and processes begin switch the requirements from brawn to brains.

comments: Comments Off on IoT: What Can You Do That You Couldn’t? Heavy Construction tags: , ,

IoT Security After “The Interview”

Posted on 22nd December 2014 in defense, Internet of Things, M2M, management, privacy, security, US government

Call me an alarmist, but in the wake of the “Interview” catastrophe (that’s how I see it in terms of both the First Amendment AND asymmetrical cyberwarfare), I see this as a clarion call to the #IoT industry to redouble efforts to make both security AND privacy Job #1.

Here’s the deal: if we want to enhance more and more parts of governmental, commercial, and private lives by clever IoT devices and apps to control them, then there’s an undeniable quid pro quo: we MUST make these devices and apps as secure as possible.

I remember some bright young entrepreneurs speaking at a recent wearables conference, where they apologized for not having put attention on privacy and security yet, saying they’d get to it early next year.

Nope.

Unacceptable.

Security must be built in from the beginning, and constantly upgraded as new threats emerge.  I used to be a corporate crisis manager, and one of the things that was so hard to convince left-brained, extremely rational engineers about was that just because fears were irrational didn’t mean they weren’t real — even the perception of insecure IoT devices and apps has the potential to kill the whole industry, or, as Vanity Fair‘s apocalyptic “Look Out, He’s Got a Phone” article documented, it could literally kill us. As in deader than a doornail.

This incident should have convinced us all that there are some truly evil people out there fixated on bringing us to our collective knees, and they have the tech savvy to do it, using tools such as Shodan. ‘Nuff said?

PS: Here’s what Mr. Cybersecurity, Bruce Schneier, has to say on the subject. Read carefully.

comments: Comments Off on IoT Security After “The Interview” tags: , , , ,

Lifting the Veil After the Sale: another IoT “Essential Truth”

Posted on 19th December 2014 in automotive, data, design, Essential Truths, Internet of Things, M2M, maintenance, management, manufacturing, services

Count me among those who believe the Internet of Things will affect every aspect of corporate operations, from manufacturing to customer relations.

Perhaps one of the most dramatic impacts will be on the range of activities that take place after the sale, including maintenance, product liability, product upgrades and customer relations.

In the past, this has been a prime example of the “Collective Blindness” that afflicted us before the IoT, because we basically had no idea what happened with our products once they left the factory floor.

In fact, what little data we did have probably served to distort our impressions of how products were actually used. Because there was no direct way to find out how the products were actually used, negative data was probably given exaggerated weight: we heard negative comments (warrantee claims, returns, liability lawsuits, etc.), loud and clear, but there was no way to find out how the majority of customers who were pleased with their products used them.

That has all changed with the IoT.

Now, we have to think about products  in totally new ways to capitalize on the IoT, and I think this merits another “Essential Truth” about the IoT:

Everything is cyclical.

Think about products — and industrial processes in general — in the old industrial system. Everything was linear: perhaps best exemplified by Henry Ford’s massive River Rouge Complex, the world’s largest integrated factory, and the epitome of integrated production.

Ford River Rouge Complex

“Ford was attempting to control and coordinate all of the necessary resources to produce complete automobiles.  Although Ford’s vision was never completely realized, no one else has come so close, especially on such a large scale.  His vision was certainly a success, one indication of this is the term Fordism, which refers to his style of mass-production, characterized by vertical integration, standardized products and assembly-line production”

At “The Rouge,” raw materials (literally: it had its own coke ovens and foundry!)  flowed in one side, and completed cars flowed out the other, bound for who knows where. Once the cars were in customers’ hands, the company’s contact was limited to whatever knowledge could be gleaned from owners’ visits to dealers’ service departments, irate calls from customers who had problems, and (in later days) safety recalls and/or multi-million dollar class-action lawsuits.

That linear thinking led to a terrible example of the “Collective Blindness” phenomenon that I’ve written about in the past: who knew how customers actually thought about their Model T’s? How did they actually drive them? Were there consistent patterns of performance issues that might not have resulted in major problems, but did irritate customers?

Sure, you could guess, or try to make inferences based on limited data, but no one really knew.

Fast forward to the newest auto manufacturer, Tesla, and its factory in Fremont, California (aside: this massive building — Tesla only uses a portion, used to be the NUMMI factory, where Chevy built Novas and Toyota built Corollas. Loved the perceptual irony: exactly the same American workers built mechanically identical cars [only the sheet metal varied] but the Toyotas commanded much higher prices, because of the perception of “Japanese quality.” LOL. But I digress….).

Tesla doesn’t lose track of its customers once the cars leave the plant.

Tesla assembly line

In fact, as I’ve written before, these “iPhones on wheels” are part of a massive cyclical process, where the cars’ on-board communications constantly send back data to the company about how the cars are actually doing on the road. And, when need be, as I mentioned in that prior post, the company was able to solve a potentially dangerous problem by simply sending out a software patch that was implemented while owners slept, without requiring customer trips to a repair shop!

I imagine that the company’s design engineers also pour over this data to discern patterns that might indicate elements of the physical design to tweak as well.

Of course, what would a blog post by me about IoT paradigm shifts be without a gratuitous reference to General Electric and its Durathon battery plant (aside to GE accounting: where should I send my W-9 and invoice so you can send me massive check for all the free PR I’ve given you? LOL)?

I can’t think of a better example of this switch to cyclical thinking:

  • including sensors into the batteries at the beginning of the production process rather than slapping them on at the end means that the company is actually able to monitor, and fine tune, the manufacturing process to optimize the critical chemical reaction. The same data allows the workers to remove defective batteries from the assembly line, so that every battery that ships works.
  • once in the field (and, remember: these batteries are deployed in incredibly remote areas where it might take days for a repair crew to reach and either service or repair them) the same sensors send back data on how the batteries are functioning. I don’t know about the specifics in the case of these batteries, but GE has actually created new revenue streams with other continuously-monitored devices by selling this data to customers who can use it (because the data is shared on a real-time basis, not just historically) to optimize performance.

Elsewhere, as I’ve mentioned before, General Electric’s William Ruh has said that being able to lift the veil of “Collective Blindness” through feedback from how customers actually use their products has even revolutionized their product design process:

“… G.E. is adopting practices like releasing stripped-down products quickly, monitoring usage and rapidly changing designs depending on how things are used by customers. These approaches follow the ‘lean start-up’ style at many software-intensive Internet companies. “’We’re getting these offerings done in three, six, nine months,’ he (Ruh) said. ‘It used to take three years.’”

Back in the ’90’s, I used to lecture and consult on what I called “Natural Wealth,” a paradigm shift in which we’d find all the inspiration we needed for an information-based economy in a table-top terrarium that embodies billion-year-old  principles of nature:

  • embrace chaos, don’t try to control it. (i.e., use open systems rather than proprietary ones)
  • create symbiosis: balance competition with cooperation (IFTTT.com, where you release your APIs to create synergistic mashups with others).
  • close the loop.

With the IoT, we can finally put that last principle into practice, substituting cyclical processes for linear ones.  At long last, the “systems dynamics” thinking pioneered by Jay Forrester and his disciple, Peter Senge, can become a reality. Here’s a closing tip to make that possible: in addition to SAP’s HANA or other analytics packages, look to systems dynamics software such as isee systems’  iThink to model your processes and transform linear into cyclical ones. Now get going: close the loop!

comments: 1 » tags: , , , ,
« Previous Next »
http://www.stephensonstrategies.com/">Stephenson blogs on Internet of Things Internet of Things strategy, breakthroughs and management