I’ll Speak Twice at Internet of Things Global Summit Next Week

I always love the Internet of Things Global Summit in DC because it’s the only IoT conference I know of that places equal emphasis on both IoT technology and public policy, especially on issues such as security and privacy.

At this year’s conference, on the  26th and 27th, I’ll speak twice, on “Smart Aging” and on the IoT in retailing.

2015_IoT_SummitIn the past, the event was used to launch major IoT regulatory initiatives by the FTC, the only branch of the federal government that seems to really take the IoT seriously, and understand the need to protect personal privacy and security. My other fav component of last year’s summit was Camgian’s introduction of its Egburt, which combines “fog computing,” to analyze IoT data at “the edge,” and low power consumption. Camgian’s Gary Butler will be on the retail panel with me and with Rob van Kranenburg, one of the IoT’s real thought leaders.

This year’s program again combines a heady mix of IoT innovations and regulatory concerns. Some of the topics are:

  • The Internet of Things in Financial Services and the Insurance sector (panel includes my buddy Chris Rezendes of INEX).
  • Monetizing the Internet of Things and a look at what the new business models will be
  • The Connected Car
  • Connected living – at home and in the city
  • IoT as an enabler for industrial growth and competition
  • Privacy in a Connected World – a continuing balancing act

The speakers are a great cross-section of technology and policy leaders.

There’s still time to register.  Hope to see you there!

 

 

The IoT Will Reinvent Replacement Parts Industry

Of all the Internet of Things’ revolutionary impacts on industry, perhaps none will be as dramatic as on replacement parts, where it will team with 3-D printing to reduce service time, inventory and costs.

I came to that realization circuitously, upon noticing Warren Buffett’s blockbuster purchase of Precision Castparts, the major precision parts supplier to the aeronautics industry.  Having read last year about yet another breakthrough innovation by Elon Musk, i.e., the first totally 3-D printed rocket engines, I was curious to see what Precision was doing in that area.  Unless my search of their website was flawed, the answer is zip, and that suggests to me that Buffett, who famously once said he doesn’t invest in technology because he doesn’t understand it, may have just bought …. a rather large dinosaur.

I noticed that one of Precision’s biggest customers is GE, which not only is using 3-D jet fuel nozzles on its engines but also ran a high-profile contest to design a 3-D printed engine mount that was open to you, me and the kids trying out the new 3-D printer at our little town’s library (note to Mr. Buffett: might be good to schedule a sit-down with Jeff Immelt before one of your biggest customers takes things in-house). As I’ve written before, not only is GE a world leader in the IoT and 3-D printing, but also in my third magic bullet, nanotech: put all three together, and you’re really talking revolution!

OK, I know 3-D printing is sloow (in its current state), so it’s unlikely to replace traditional assembly lines at places such as Precision Castparts for large volumes of parts, but that doesn’t mean it won’t rapidly replace them in the replacement parts area.  I talked to a friend several years ago whose biz consists of being a broker between power plants that need replacement parts yesterday and others with an excess on hand, and couldn’t help thinking his days were numbered, because it was predicated on obsolete technology — and thinking.

Think of how the combined strengths of the IoT and 3-D printing can help a wide range of industries get replacement parts when and where they need them, and at potentially lower cost:

  • sensors in IoT-enabled devices will give advance notice of issues such as metal fatigue, so that repairs can be done sooner (“predictive maintenance“), with less disruption to normal routine, cheaper and reducing the chance of catastrophic failure.
  • because data can be shared on a real-time by not only your entire workforce, but also your supply chain, you can automate ordering of replacement parts.
  • perhaps most important, instead of a supplier having to maintain a huge inventory of replacement parts on the possibility they may be needed, they can instead be produced only when needed, or at least with a limited inventory (such as replacing a part in inventory as one is ordered). This may lead to “re-shoring” of jobs, because you will no longer have to deal with a supplier on the other side of the globe: it might be in the next town, and the part could be delivered as soon as printed, saving both delay and money.
  • your company may have your own printer, and you will simply pay the OEM for the digital file to print a part in-house, rather than having to deal with shipping, etc.

And, as I mentioned in the  earlier post about GE’s leadership in this area, there are other benefits as well:

  • “We’ll no longer do subtractive processes, where a rough item is progressively refined until it is usable.  Instead, products will be built atom-by-atom, in additive processes where they will emerge exactly in the form they’re sold.
  • “Products will increasingly be customized to the customer’s exact specifications. The products will be further fine-tuned based on a constant flow of data from the field about how customers actually use them.”

Sooo, Mr. Buffett, it’s time that you come to terms with 21-st century technology or Berkshire Hathaway’s financial slide may continue.

 

McKinsey IoT Report Nails It: Interoperability is Key!

I’ll be posting on various aspects of McKinsey’s new “The Internet of Things: Mapping the Value Beyond the Hype” report for quite some time.

First of all, it’s big: 148 pages in the online edition, making it the longest IoT analysis I’ve seen! Second, it’s exhaustive and insightful. Third, as with several other IoT landmarks, such as Google’s purchase of Nest and GE’s divestiture of its non-industrial internet division, the fact that a leading consulting firm would put such an emphasis on the IoT has tremendous symbolic importance.

McKinsey report — The IoT: Mapping the Value Beyond the Hype

My favorite finding:

“Interoperability is critical to maximizing the value of the Internet of Things. On average, 40 percent of the total value that can be unlocked requires different IoT systems to work together. Without these benefits, the maximum value of the applications we size would be only about $7 trillion per year in 2025, rather than $11.1 trillion.” (my emphasis)

This goes along with my most basic IoT Essential Truth, “share data.”  I’ve been preaching this mantra since my 2011 book, Data Dynamite (which, if I may toot my own horn, I believe remains the only book to focus on the sweeping benefits of a paradigm shift from hoarding data to sharing it).

I was excited to see that the specific example they zeroed in on was offshore oil rigs, which I focused on in my op-ed on “real-time regulations,” because sharing the data from the rig’s sensors could both boost operating efficiency and reduce the chance of catastrophic failure. The paper points out that there can be 30,000 sensors on an rig, but most of them function in isolation, to monitor a single machine or system:

“Interoperability would significantly improve performance by combining sensor data from different machines and systems to provide decision makers with an integrated view of performance across an entire factory or oil rig. Our research shows that more than half of the potential issues that can be identified by predictive analysis in such environments require data from multiple IoT systems. Oil and gas experts interviewed for this research estimate that interoperability could improve the effectiveness of equipment maintenance in their industry by 100 to 200 percent.”

Yet, the researchers found that only about 1% of the rig data was being used, because it rarely was shared off the rig with other in the company and its ecosystem!

The section on interoperability goes on to talk about the benefits — and challenges — of linking sensor systems in examples such as urban traffic regulation, that could link not only data from stationary sensors and cameras, but also thousands of real-time feeds from individual cars and trucks, parking meters — and even non-traffic data that could have a huge impact on performance, such as weather forecasts.  

While more work needs to be done on the technical side to increase the ease of interoperability, either through the growing number of interface standards or middleware, it seems to me that a shift in management mindset is as critical as sensor and analysis technology to take advantage of this huge increase in data:

“A critical challenge is to use the flood of big data generated by IoT devices for prediction and optimization. Where IoT data are being used, they are often used only for anomaly detection or real-time control, rather than for optimization or prediction, which we know from our study of big data is where much additional value can be derived. For example, in manufacturing, an increasing number of machines are ‘wired,’ but this instrumentation is used primarily to control the tools or to send alarms when it detects something out of tolerance. The data from these tools are often not analyzed (or even collected in a place where they could be analyzed), even though the data could be used to optimize processes and head off disruptions.”

I urge you to download the whole report. I’ll blog more about it in coming weeks.

Intel’s IoT tech improves its own manufacturing efficiency

This demonstration IoT manufacturing project hits my buttons!

I love IoT-enabled manufacturing (what I call “precision manufacturing“) and I REALLY love companies (such as GE, at its Durathon battery plant) that eat their own dogfood by applying their IoT technology internally.  Gotta walk the talk!

 

That’s why I was happy to learn how Intel is  applied its own IoT technology to its own factories. In the accompanying video, Intel VP for IoT operations and group marketing Frank James says:

“The real opportunity is how to combine … data differently, which will ultimately give you insights not only into how your factory is running but, what’s more important, will let you predict how your factory will run the next minute, the next hour, the next shift, the next day.”

The pilot factory automation project is a collaboration with Mitsubishi Electric (more points for a key IoT “Essential Truth” — collaboration!).  The project, at Intel’s Malaysia manufacturing facility, combines two critical components, end-to-end IoT connectivity and big data analytics. The benefits were impressive: $9 million in cost avoidance and improved decision making, plus:

  • improved equipment uptime
  • increased yield and productivity
  • predictive maintenance
  • reduced component failures.

That hard-to-quantify improved decision making, BTW, is one of the things that doesn’t get enough discussion when we talk about IoT benefits: decision-making improves when there is more data to consider, more people to analyze and discuss it simultaneously (not sequentially, as in the past), and when you’ve got tools such as data dashboards to allow visualizing the data and its patterns.

The companies plan to roll out the services commercially this year.

Here are the specs:

“Using an Intel® Atom™ processor-based IoT gateway called the C Controller from Mitsubishi Electric’s iQ-Platform, Intel was able to securely gather and aggregate data for the analytics server. Data was then processed using Revolution R Enterprise* software from Revolution Analytics*, an analytics software solution that uses the open source R statistics language, which was hosted on Cloudera Enterprise*, the foundation of an enterprise data hub.”

 

Sensors remain critical to spread of Internet of Things

What happens with sensor design, cost, and security remains front-and-center with the Internet of Things, no matter how much we focus on advanced analytical tools and the growing power of mobile devices.

That’s because, on one hand, truly realizing the IoT’s full potential will require that at least some sensors get to the low-power, tiny size and cheap costs needed to realize Kris Pister’s dream of “smart dust” sensors that can be strewn widely.

On the other hand, there’s the chance that low-end sensors that don’t include adequate security firmware can’t keep up with the changing nature of security risks and may give hackers access to the entire network, with potentially disastrous effects.

That’s why several reports on sensors caught my eye.

PWC released a report, Sensing the Future of the Internet of Things, zeroing in on sensor sales as a proxy for increased corporate investment in the IoT, and concluding that by that measure, “the IoT movement is underway.” Based on its 2014 survey of 1,500 business and technology leaders worldwide, there was one eye-popping finding: the US lags behind the entire rest of the world in planned spending on sensors this year: 26% of Asian and almost as many from South America (percentage not given)  followed closely by Africa, with 18%.  The surprising laggards? Europe with 8% and North America, dead last at only 7%.  Hello?????

Equally interesting was the company’s listing of the industry segments leading the deployment of sensors and examples of the sensors they’re using:

  • Energy & Mining: 33%. “Sensors continuously monitor and detect dangerous carbon monoxide levels in mines to improve workplace safety.”
  • Power and Utilities: 32%.  Instead of the old one-way metering, “Internet-connected smart meters measure power usage every 15 minutes and provide feedback to the power consumer, sometimes automatically adjusting the system’s parameters.”
  • Automotive: 31%.  “Sensors and beacons embedded in the road working together with car-based sensors are used for hands-free driving, traffic pattern optimization and accident avoidance.”
  • Industrial: 25%. “A manufacturing plant distributes plant monitoring and optimization tasks across several remote, interconnected control points. Specialists once needed to maintain, service and optimize distributed plant operations are no longer required to be physically present at the plant location, providing economies of scale.”
  • Hospitality: 22%. “Electronic doorbells silently scan hotel rooms with infrared sensors to detect body heat, so the staff can clean when guests have left the room.”
  • Health Care: 20%. “EKG sensors work together with patients’ smartphones to monitor and transmit patient physical environment and vital signs to a central cloud-based system.”
  • Retail: 20%. “Product and shelf sensors collect data throughout the entire supply chain—from dock to shelf. Predictive analytics applications process this data and optimize the supply chain.”
  • Entertainment: 18%. “In the gaming world, companies use tracking sensors to transfer the movements of users onto the screen and into the action.”
  • Technology: 17%. “Hardware manufacturers continue to innovate by embedding sensors to measure performance and predict maintenance needs before they happen.”
  • Financial Services: 13%. “Telematics allows devices installed in the car to transmit data to drivers and insurers. Applications like stolen vehicle recovery, automatic crash notification, and vehicle data recording can minimize both direct and indirect costs while providing effective risk management.”

The surprises there were that health care penetration was so low, especially because m-health can be so helpful in diagnosis and treatment, while the examples of telematics seemed off the mark in the financial services category. Why not examples such as ApplePay?

More compelling were the relatively high rates of sensor deployment in high-stakes fields such as energy, utilities, and automotive: those are such huge industries, and the benefits of real-time data are so compelling that they show the IoT is really maturing.

Finally, the percentage of companies investing in sensors grew slightly, from 17% to 20%, with 25%of what PWC labels “Top Performers” are investing in them compared to 18% the previous year. Surprisingly, most companies don’t get it about sensors’ importance: only “14% of respondents said sensors would be of the highest strategic importance to their organizations in the next 3–5 years, as compared to other emerging technologies.”

Most important, 54% of those “Top Performers” said they’d invest in sensors this year.


 

Sensors’ promise as the size decreases — radically — and functionality increases was highlighted by The Guardian.  It focused on PragmaticIC Printing, a British firm that prints tiny, hairlike sensors on plastics. CEO Scott White’s hope is that:

” the ultra-thin microcircuits will soon feature on wine bottles to tell when a Chablis is at the perfect temperature and on medication blister packs to alert a doctor if an elderly patient has not taken their pills.

“With something which is slimmer than a human hair and very flexible, you can embed that in objects in a way that is not apparent to the user until it is called upon to do something. But also the cost is dramatically lower than with conventional silicon so it allows it to be put in products and packaging that would never justify the cost of a piece of normal electronics,” said White.

 

These uses certainly meet my test of real innovation: what can you do that you couldn’t do before. Or, as White puts it, “It is the combination of those factors [price and size] which allows us to start thinking about doing things with this which wouldn’t even be conceivable with conventional silicon based electronics.”

Another article that really caught my eye regarded a new category of “hearable” — and perhaps even, more radically, “disappearables” –sensors which the headline boldly predicted “As Sensors Shrink, Wearables Will Dis-appear.” But they were barely here in the first place, LOL!  The article mentioned significant breakthroughs in reducing sensors’ size and energy requirements, as well as harvesting ambient energy produced by sources such as bodily movement:

“Andrew Sheehy of Generator Research calculates that, for example, the heat in a human eyeball could power a 5 milliwatt transmitter – more than enough, he says, to power a connection from a smart contact lens to a smartphone or other controlling device.”

 The same article mentioned some cutting-edge research such as a Google/Novartis collaboration to measure glucose levels in tears via a contact lense, and an edible embedded microchip — the size of a grain of sand — and powered by stomach juices, which would transmit data by Bluetooth.
Elsewhere, a sampling of sensor design breakthroughs in recent months show the potential for radical reductions in costs and energy needs as well as increased sensitivity and data yield:

HOWEVER, as I said above, here’s what worries me. Are developers paying enough attention to security and privacy? That could be a real downfall for the IoT, since many sensors tend to be in place for years, and the nature of security challenges can change dramatically during that time.  Reducing price can’t be at the expense of security.

Let me know what steps you’re taking to boost sensor security, and I’ll mention them in a future post!

GE & IBM make it official: IoT is here & now & you ignore it at your own risk!

Pardon my absence while doing the annual IRS dance.

While I was preoccupied, GE and IBM put the last nail in the coffin of those who are waiting to launch IoT initiatives and revise their strategy until the Internet of Things is more ….. (supply your favorite dismissive wishy-washy adjective here).

It’s official: the IoT is here, substantive, and profitable.

Deal with it.

To wit:

The two blue-chips’ moves were decisive and unambiguous. If you aren’t following suit, you’re in trouble.

The companies accompanied these bold strategic moves with targeted ones that illustrate how they plan to transform their companies and services based on the IoT and related technologies such as 3-D printing and Big Data:

  • GE, which has become a leader in 3-D printing, announced its first FAA-approved 3-D jet engine part, housing a jet’s compressor inlet temperature sensor. Sensors and 3-D printing: a killer combination.
  • IBM, commercializing its gee-whiz Watson big data processing system, launched Watson Health in conjunction with Apple and Johnson & Johnson, calling it “our moonshot” in health care, hoping to transform the industry.  Chair Ginny Rometty said that:

“The Watson Health Cloud platform will ‘enable secure access to individualized insights and a more complete picture of the many factors that can affect people’s health,’ IBM says each person generates one million gigabytes of health-related data across his or her lifetime, the equivalent of more than 300 million books.”

There can no longer be any doubt that the Internet of Things is a here-and-now reality. What is your company doing to catch up to the leaders and share in the benefits?

 

The Internet of Things’ Essential Truths

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?

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.”

 

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!

Management Challenge: Lifeguards in the IoT Data Lake

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.

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