Smart Aging: Kanega watch for seniors Kickstarter campaign ends today

Posted on 24th March 2015 in aging, data, design, health, Internet of Things, m-health, Quantified Self, seniors, SmartAging

“Independence with Dignity” is the motto for Jean Anne Booth’s Kanega Watch, which is in the last day of its Kickstarter campaign.

I’m not crazy about it, but in general I like what you see, and hope you get on board.  It addresses three major concerns for the elderly:

Kanega watchlike what I see, and hope you’ll get on board!  It’s designed to deal with three critical aging concerns:

  • falls
  • medication reminders
  • wandering.

I met the woman behind it at a conference in Boston last Summer, and even though the prototype at that point looked very unappealing, this looks more promising.

I’m in the process of creating a list of 10 objective criteria for evaluating devices and apps that fit with my “Smart Aging” paradigm shift, which combines Quantified Self devices to encourage healthy habits and change your relationship with your doctor into a partnership, and smart home devices, which make it easier to manage your home when elderly, so you can “age in place.”  Here’s how it stacks up against my criteria (which, BTW, are still in development — comments welcomed). Bear in mind that I haven’t seen current prototype, and the site doesn’t answer all of my questions. :

Is it easy to use?

  1. Does it give you a choice of ways to interact, such as voice, text or email?
  2. Does it give you reminders?
  3. Is it easy for you to program, or allow someone else to do it remotely?
  4. Does it have a large display and controls?
  5. Is it intuitive?
  6. Does it require professional installation?
  7. Is it flexible: can it be adjusted? Is it single purpose, or does it allow other devices to plug in and create synergies?

YES: voice-activated, rather than requiring buttons. No programming.


Does it protect privacy & security?

  1. Is storage local vs. cloud or company’s servers? Is data encrypted? Anomized?
  2. Do you feel creepy using it?
  3. Does it protect against exploitation by scam artists (such as identifying callers)?
  4. Is it password-protected?
  5. Is security “baked in” or an afterthought?

NO CLUE.


Does it complicate your life, or simplify it?

YES: doesn’t require a smartphone to function, and is voice-activated rather than using buttons.

Does it protect privacy and security?

NO CLUE.

 

Is it affordable?

  1. Are there monthly fees? If so, low or high?
  2. Is there major upfront cost?
  3. Does full functioning require accessories?

IFFY: You can get one by contributing $279 to the Kickstarter campaign. If that’s the retail price, it’s a little pricey, but lower than the Apple Watch base, $349, and probably a good price considering the value added services . Didn’t see anything about a monthly fee for the fall reporting & response service.

Does it stigmatize and/or condescend?

  1. Is it stylish, or does the design” shout” that it’s for seniors?
  2. Is the operation or design babyish?
  3. Would younger people use it?

NO: it doesn’t have a stigmatizing button, & uses a familiar form factor (watch).

Does it use open or proprietary standards?

NO CLUE.

 

Is the information shareable if you choose to do so?

NO CLUE.

 

Can you learn something from it to improve your life and empower yourself?

  1. For example, does health data encourage you to exercise more, or eat better?

NO: doesn’t give you feedback, measure your activity, etc.

Does it help you do something you couldn’t do before?

  1. Does it create a new range of services that were simply impossible with past technologies?

YES: The wandering alert (offers directions home) is new. Otherwise, just does some things such as medication alerts and calling for help that other devices have done.

Is it sturdy?

YES.

 

Does it have “loveability” (i.e., connect with the user emotionally)?

(This term was coined by David Rose in Enchanted Objects, and refers to products that are adorable or otherwise bond with the user)

YES: it has a Siri-like voice, which you can name, which gives reminders about taking meds and gives you directions home if you wander.
I’d give it about a 5 out of 10: I wouldn’t call this a must have — I’d like a little more of a multi-purpose tool that combines smart home and Quantified Self functions — like the Apple Watch (again, disclaimer that I work part time at Apple Store — but don’t have any proprietary info.) The watch is a little too clunky looking for me (prefer the Jony Ive-aesthetics of the Apple Watch), but it looks promising — and doesn’t require coupling to a smartphone, which is befuddling to a lot of seniors.

Kickstarter backers will begin receiving their Kanega watches in February 2016, with general market availability in summer 2016. The site doesn’t say anything about price.

Only a few hours left to join the Kickstarter campaign!

Yeah, I couldn’t figure out the names either. Turns out it’s from Cherokee: “Unalii” is “friend”, and “Kanega” is “speak.”  You learn something every day….

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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?

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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.”

 

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Apple ResearchKit will launch medical research paradigm shift to crowd-sourcing

Posted on 11th March 2015 in aging, collaboration, data, Essential Truths, health, Internet of Things, m-health, open data, paradigm shift, privacy, Quantified Self, security, seniors, SmartAging

Amidst the hoopla about the new MacBook and much-anticipated Apple Watch, Apple snuck something into Monday’s event that blew me away (obligatory disclaimer: I work part-time at The Apple Store, but the opinions expressed here are mine).

My Heart Counts app

Four years after I proselytized about the virtues of democratizing data in my Data Dynamite: how liberating data will transform our world book (BTW: pardon the hubris, but I still think it’s the best thing out there about the attitudinal shift needed to capitalize on sharing data), I was so excited to learn about the new ResearchKit.

Tag line? “Now everybody can do their part to advance medical research.”

The other new announcements might improve your quality of life. This one might save it!

As Senior VP of Operations Jeff Williams said in announcing the kit,  the process of medical research ” ..hasn’t changed in decades.” That’s not really true: as I wrote in my book, the Quantified Self movement has been sharing data for several years, as well as groups such as CureTogether and PatientsLikeMe. However, what is definitely true is that no one has harnessed the incredible power of the smartphone for this common goal until now, and that’s really incredible. It’s a great example of my IoT Essential Truth of asking “who else could use this data?”

A range of factors cast a pall over traditional medical research.

Researchers have had to cast a broad net even to get 50-100 volunteers for a clinical trial (and may have to pay them, to boot, placing the results validity when applied to the general population in doubt).  The data has often been subjective (in the example Williams mentioned, Parkinson’s patients are classified by a doctor simply on the basis of walking a few feet). Also, communication about the project has been almost exclusively one way, from the researcher to the patient, and limited, at best.

What if, instead, you just had to turn on your phone and open a simple app to participate? As the website says, “Each one [smartphone] is equipped with powerful processors and advanced sensors that can track movement, take measurements, and record information — functions that are perfect for medical studies.” Suddenly research can be worldwide, and involve millions of diverse participants, increasing the data’s amount and validity (There’s a crowdsourcing research precedent: lot of us have been participating in scientific crowdsourcing for almost 20 years, by installing the SETI@Home software that runs in the background on our computers, analyzing data from deep space to see if ET is trying to check in)!

Polymath/medical data guru John Halamka, MD wrote me that:

“Enabling patients to donate data for clinical research will accelerate the ‘learning healthcare system’ envisioned by the Institute of Medicine.   I look forward to testing out Research Kit myself!”

The new apps developed using ResearchKit harvest information from the Health app that Apple introduced as part of iOS8. According to Apple:

“When granted permission by the user, apps can access data from the Health app such as weight, blood pressure, glucose levels and asthma inhaler use, which are measured by third-party devices and apps…. ResearchKit can also request from a user, access to the accelerometer, microphone, gyroscope and GPS sensors in iPhone to gain insight into a patient’s gait, motor impairment, fitness, speech and memory.

Apple announced that it has already collaborated with some of the world’s most prestigious medical institutions, including Mass General, Dana-Farber, Stanford Medical, Cornell and many others, to develop apps using ResearchKit. The first five apps target asthma, breast cancer, cardiovascular disease, diabetes and Parkinson’s disease.  My favorite, because it affects the largest number of people, is the My Heart Counts one. It uses the iPhone’s built-in motion sensors to track participants’ activity, collecting data during a 6-minute walk test from those who are able to walk that long. If participants also have a wearable activity device connecting with the Health app (aside: still don’t know why my Jawbone UP data doesn’t flow to the Health app, even though I made the link) , they are encouraged to use that as well. Participants will also enter data about their heart disease risk factors and their lab tests readings to get feedback on their chances of developing heart disease and their “heart age.” Imagine the treasure trove of cardiac data it will yield!

 A critical aspect of why I think ResearchKit will be have a significant impact is that Apple decided t0 make it open source, so that anyone can tinker with the code and improve it (aside: has Apple EVER made ANYTHING open source? Doubt it! That alone is noteworthy).  Also, it’s important to note, in light of the extreme sensitivity of any personal health data, that Apple guarantees that it will not have access to any of the personal data.

Because of my preoccupation with “Smart Aging,” I’m really interested in whether any researchers will specifically target seniors with ResearchKit apps. I’ll be watching carefully when the Apple Watch comes out April 24th to see if seniors buy them (not terribly optimistic, I must admit, because of both the cost and the large number of seniors I help at The Apple Store who are befuddled by even Apple’s user-friendly technology) because the watch is a familiar form factor for them (I haven’t worn a watch since I got my first cell phone, and most young people I know have never had one) and might be willing to use them to participate in these projects.

N0w, if you’ll excuse me, I just downloaded the My Heart Counts app, and must find out my “heart age!”

 

Doh!  Just after I posted this, I saw a really important post on Ars Technica pointing out that this brave new world of medical research won’t go anywhere unless the FDA approves:

“As much as Silicon Valley likes to think of itself as a force for good, disrupting this and pivoting that, it sometimes forgets that there’s a wider world out there. And when it comes to using devices in the practice of medicine, that world contains three very important letters: FDA. That’s right, the US Food and Drug Administration, which Congress has empowered to regulate the marketing and research uses of medical devices.

“Oddly, not once in any of the announcement of ResearchKit did we see mention of premarket approval, 510k submission, or even investigational device exemptions. Which is odd, because several of the uses touted in the announcement aren’t going to be possible without getting the FDA to say yes.”

I remember reading that Apple had reached out to the FDA during development of the Apple Watch, so I’m sure none of this comes as a surprise to them, and any medical researcher worth his or her salt is also aware of that factor. However, the FDA is definitely going to have a role in this issue going forward, and that’s as it should be — as I’ve said before, with any aspect of the IoT, privacy and security is Job One.

 

 

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FTC report provides good checklist to design in IoT security and privacy

Posted on 7th March 2015 in automotive, data, government, health, home automation, Internet of Things, m-health, M2M, marketing, mobile, open data, privacy, Quantified Self, real-time regulation, security, smart home, strategy, US government, wearables
FTC report on IoT

FTC report on IoT

SEC Chair Edith Ramirez has been pretty clear that the FTC plans to look closely at the IoT and takes IoT security and privacy seriously: most famously by fining IoT marketer TrendNet for non-existent security with its nanny cam.

Companies that want to avoid such actions — and avoid undermining fragile public trust in their products and the IoT as a whole — would do well to clip and refer to this checklist that I’ve prepared based on the recent FTC Report, Privacy and Security in a Connected World, compiled based on a workshop they held in 2013, and highlighting best practices that were shared at the workshop.

  1. Most important, “companies should build security into their devices at the outset, rather than as an afterthought.” I’ve referred before to the bright young things at the Wearables + Things conference who used their startup status as an excuse for deferring security and privacy until a later date. WRONG: both must be a priority from Day One.

  2. Conduct a privacy or security risk assessment during design phase.

  3. Minimize the data you collect and retain.  This is a tough one, because there’s always that chance that some retained data may be mashed up with some other data in future, yielding a dazzling insight that could help company and customer alike, BUT the more data just floating out there in “data lake” the more chance it will be misused.

  4. Test your security measures before launching your products. … then test them again…

  5. “..train all employees about good security, and ensure that security issues are addressed at the appropriate level of responsibility within the organization.” This one is sooo important and so often overlooked: how many times have we found that someone far down the corporate ladder has been at fault in a data breach because s/he wasn’t adequately trained and/or empowered?  Privacy and security are everyone’s job.

  6. “.. retain service providers that are capable of maintaining reasonable security and provide reasonable oversight for these service providers.”

  7. ‘… when companies identify significant risks within their systems, they should implement a defense-in -depth approach, in which they consider implementing security measures at several levels.”

  8. “… consider implementing reasonable access control measures to limit the ability of an unauthorized person to access a consumer’s device, data, or even the consumer’s network.” Don’t forget: with the Target data breach, the bad guys got access to the corporate data through a local HVAC dealer. Everything’s linked — for better or worse!

  9. “.. companies should continue to monitor products throughout the life cycle and, to the extent feasible, patch known vulnerabilities.”  Privacy and security are moving targets, and require constant vigilance.

  10. Avoid enabling unauthorized access and misuse of personal information.

  11. Don’t facilitate attacks on other systems. The very strength of the IoT in creating linkages and synergies between various data sources can also allow backdoor attacks if one source has poor security.

  12. Don’t create risks to personal safety. If you doubt that’s an issue, look at Ed Markey’s recent report on connected car safety.

  13. Avoid creating a situation where companies might use this data to make credit, insurance, and employment decisions.  That’s the downside of cool tools like Progressive’s “Snapshot,” which can save us safe drivers on premiums: the same data on your actual driving behavior might some day be used become compulsory, and might be used to deny you coverage or increase your premium).

  14. Realize that FTC Fair Information Practice Principles will be extended to IoT. These “FIPPs, ” including “notice, choice, access, accuracy, data minimization, security, and accountability,” have been around for a long time, so it’s understandable the FTC will apply them to the IoT.  Most important ones?  Security, data minimization, notice, and choice.

Not all of these issues will apply to all companies, but it’s better to keep all of them in mind, because your situation may change. I hope you’ll share these guidelines with your entire workforce: they’re all part of the solution — or the problem.

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IFTTT DO apps: neat extension of my fav #IoT crowdsourcing tool!

Posted on 25th February 2015 in collaboration, communication, data, Essential Truths, home automation, Internet of Things, open data, paradigm shift

Have I told you lately how much I love IFTTT? Of course!  As I’ve said, I think they are a phenomenal example of my IoT “Essential Truth” question: who else can use this data?

IFTTT_DO_buttonNow, they’ve come up with 3 new apps, the “DO button,” “DO camera,” and “DO Note,” that make this great tool even more versatile!

With a DO “recipe,” you simply tap on the appropriate app, and the “recipe” runs. Presto! Change-o!

As a consultant who must bill for his time, I particularly like the one that lets you “Track Your Work hours” on Google Drive, but you’re sure to find your own favorites in categories such as play, work, home, families, and essentials. Some are just fun, and some will increase your productivity or help manage your household more easily (hmm: not sure where “post a note to your dog’s timeline” fits in (aside to my sons: feel free to “send notes to your data via email”.  If past experience is any indication, there should be many, many more helpful “Do” recipes as soon as users are familiar with how to create them.

As I’ve said before, it’s no reflection on the talented engineers at HUE, NEST, et. al., but there’s simply no way they could possibly visualize all the ways that their devices could be used and/or combined with others, and that’s why IFTTT, by adding the crowdsourcing component and democratizing data, is so important to speeding the IoT’s deployment.

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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.

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The #IoT Can Kill You! Got Your Attention? Car Security a Must

Posted on 11th February 2015 in automotive, data, government, Internet of Things, M2M, marketing, mobile, privacy, security, transportation, US government

The Internet of Things can kill you.

Got your attention? OK, maybe this is the wake-up call the IoT world needs to make certain that privacy and security are baked in, not just afterthoughts.

Markey_IoT_car_reportI’ve blogged before about how privacy and security must be Job 1, but now it’s in the headlines because of a new report by our Mass. Senator, Ed Markey (Political aside: thanks, Ed, for more than 30 years of leadership — frequently as a voice crying in the wilderness — on the policy implications of telecomm!), “Tracking & Hacking: Security & Privacy Gaps Put American Drivers at Risk,” about the dangers of not taking the issues seriously when it comes to smart cars.

I first became concerned about this issue when reading “Look Out, He’s Got an Phone,!” (my personal nominee for all-time most wry IoT headline…), a litany of all sorts of horrific things, such as spoofing the low air-pressure light on your car so you’ll pull over and the Bad Guys can get it would stop dead at 70 mph,  that are proven risks of un-encrypted automotive data.  All too typical was the reaction of Schrader Electronics, which makes the tire sensors:

“Schrader Electronics, the biggest T.P.M.S. manufacturer, publicly scoffed at the Rutgers–South Carolina report. Tracking cars by tire, it said, is ‘not only impractical but nearly impossible.’ T.P.M.S. systems, it maintained, are reliable and safe.

“This is the kind of statement that security analysts regard as an invitation. A year after Schrader’s sneering response, researchers from the University of Washington and the University of California–San Diego were able to ‘spoof’ (fake) the signals from a tire-pressure E.C.U. by hacking an adjacent but entirely different system—the OnStar-type network that monitors the T.P.M.S. for roadside assistance. In a scenario from a techno-thriller, the researchers called the cell phone built into the car network with a message supposedly sent from the tires. ‘It told the car that the tires had 10 p.s.i. when they in fact had 30 p.s.i.,’ team co-leader Tadayoshi Kohno told me—a message equivalent to ‘Stop the car immediately.’ He added, ‘In theory, you could reprogram the car while it is parked, then initiate the program with a transmitter by the freeway. The car drives by, you call the transmitter with your smartphone, it sends the initiation code—bang! The car locks up at 70 miles per hour. You’ve crashed their car without touching it.’”

Hubris: it’ll get you every time….

So now Senator Markey lays out the full scope of this issue, and it should scare the daylights out of you — and, hopefully, Detroit! The report is compiled on responses by 16 car companies (BMW, Chrysler, Ford, General Motors, Honda, Hyundai, Jaguar Land Rover, Mazda, Mercedes-Benz, Mitsubishi, Nissan, Porsche, Subaru, Toyota, Volkswagen (with Audi), and Volvo — hmm: one that didn’t respond was Tesla, which I suspect [just a hunch] really has paid attention to this issue because of its techno leadership) to letters Markey sent in late 2013. Here are the damning highlights from his report:

“1. Nearly 100% of cars on the market include wireless technologies that could pose vulnerabilities to hacking or privacy intrusions.

2. Most automobile manufacturers were unaware of or unable to report on past hacking incidents.

3. Security measures to prevent remote access to vehicle electronics are inconsistent and haphazard across all automobile manufacturers, and many manufacturers did not seem to understand the questions posed by Senator Markey.

4. Only two automobile manufacturers were able to describe any capabilities to diagnose or meaningfully respond to an infiltration in real-time, and most say they rely on technologies that cannot be used for this purpose at all. (my emphasis)

5. Automobile manufacturers collect large amounts of data on driving history and vehicle performance.

6. A majority of automakers offer technologies that collect and wirelessly transmit driving history data to data centers, including third-party data centers, and most do not describe effective means to secure the data.

7. Manufacturers use personal vehicle data in various ways, often vaguely to “improve the customer experience” and usually involving third parties, and retention policies – how long they store information about drivers – vary considerably among manufacturers.

8. Customers are often not explicitly made aware of data collection and, when they are, they often cannot opt out without disabling valuable features, such as navigation.”

In short, the auto industry collects a lot of information about us, and doesn’t have a clue how to manage or protect it.

I’ve repeatedly warned before that one of the issues technologists don’t really understand and/or scoff at, is public fears about privacy and security. Based on my prior work in crisis management, that can be costly — or fatal.

This report should serve as a bit of electroshock therapy to get them (and here I’m referring not just to auto makers but all IoT technologists: it’s called guilt by association, and most people tend to confabulate fears, not discriminate between them. Unless everyone in IoT takes privacy and security seriously, everyone may suffer the result [see below]) to realize that it’s not OK, as one of the speakers at the Wearables + Things conference said, that “we’ll get to privacy and security later.” It’s got to be a priority from the get-go (more about this in a forthcoming post, where I’ll discuss the recent FTC report on the issue).

I’ve got enough to worry about behind the wheel, since the North American Deer Alliance is out to get me. Don’t make me worry about false tire pressure readings.

PS: there’s another important issue here that may be obscured: the very connectedness that is such an important aspect of the IoT. Remember that the researchers spoofed the T.P.M.S. system not through a frontal assault, but by attacking the roadside assistance system? It’s like the way Target’s computers were hacked via a small company doing HVAC maintenance. Moral of the story? No IoT system is safe unless all the ones linking to it are safe.  For want of a nail … the kingdom was lost!

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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.

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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!

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