Live Blogging #LlveWorx ’18, Day 2

Aiden Quilligan, Accenture Industry X.0, on AI:

  • Mindset and AI: must undo what Hollywood has done on this over years, pose it as human vs. machine.
  • We think it should be human PLUS machine.
  • he’s never seen anything move as fast as AI, especially in robotics
  • now, co-bots that work along side us
  • exoskeletons
  • what do we mean by AI?  Machine learning.  AI is range of technologies that can learn and then act. AI is the “new work colleague” we need to learn to get along with.
  • predictions: will generate #2.9 trillion in biz value and recover 6.2 billion hours of worker productivity in 2021.
  • myths:
    • 1) robots evil, coming for us: nothing inherently anti-human in them.
    • 2) will take our jobs. Element of truth in terms of repetitive, boring work that will be replaced. They will fill in for retiring workers. Some new industries created by them.  Believe there will be net creation of jobs.
    • 3) current approaches will still work.

6 steps to the Monetization of IoT, Terry Hughes:

  • Digital native companies (Uber) vs. digitally transforming companies
  • also companies such as Kodak that didn’t transform at all (vs. Fujifilm, which has transformed).
  • Forbes: 84% of companies have failed with at least one transformation program.  Each time you fail you lose 1/2 billion
  • steps:
    • 1) devices with potential
    • 2) cloud network communication
    • 3) software distribution
    • 4) partner and provider ecosystem
    • 5) create a marketplace.
    • 6) monetization of assets.
  • crazy example of software company that still ships packages rather than just download because of initial cost in new delivery system
  • 3 big software challenges for digitally transforming company
    • fragmented silos of software by product, business unit & software
    • messy and complex distribution channels
    • often no link between software and the hardware that it relates to
  • importance of an ecosystem
    • Blackberry example of one that didn’t have the ecosystem
  • 3rd parties will innovate and add value around a manufacturer’s core products
  • in IoT it’s a land grab for mindshare of 3rd-party innovators.
  • need strong developer program
  • tools for app development and integration
  • ease of building and publishing apps
  • path to discovery and revenue for developer
  • IDC: developer ecosystem allow enterprises to massively scale distribution
  • digitally native companies have totally different models (will get details later…)
  • hybrids:
    • GE Healthcare:  working with Gallus BioPharma
    • Heidelberg & Eig have digital biz model for folding carton printing. Pay per use
  • Ford is heading for mobility as a transformation

 


Bernard Marr: Why IoT, Combined With AI and Big Data, Fuels 4th Industrial Revolution

 

  • connecting everything in house to Internet
  • Spotify: their vision is they understand us better. Can correlate your activity on Apple Watch (such as spinning) & create a play list based on that)
  • FitBit: the photo will estimate your calorie content.
  • John Deere
  • ShotSpotter: the company that monitors gun shots
  • understanding customers & markets better than before:
    • Facebook: better at face recognition than we are. They can predict your IQ, your relationship status.
  • Lot of frightening, IMHO, examples of AI analyzing individuals and responding without consideration of ethics and privacy
  • 3) improving operations and efficiency:
    • self-driving boats
    • drones
    • medicine through Watson

panel on IoT:

  • Don’t be afraid of the cloud
  • Ryan Cahalane, Colfax: prepare for big, start small and move fast. They had remarkable growth with switch to IoT.  Not a digital strategy, but digital in everything they do. Have “connected welders,” for example.
  • Justin Hester, Hirotec: most importatnt strategic digital transformation decision your organization can make is the selection of a platform. The platform is the underlying digital thread that enables your team to meet  the unique and chanding needs of your organization and to scale those solutions rapidly. “Assisted reality” in ThingWorx
  • Shane O’Callahan, TSM (Ireland):  Make industrial automation equipment for manufacturing. Understanding your key value driver is where to start. Then start samll, scale fast and get a win!

Jeffrey Miller, PTC: Digital Transformation:

  • if you start with digital strategy you’re starting in wrong place Start with business strategy. 
  • Couple with innovation vision merged with digital strategy. Add business use cases.
  • Jobs: it’s not how much you spend on R & D, but “about the people you have, you you’re dled, and how much you get it”
  • create an environment for innovation
    • do we encourage experimentation?
    • is it ok to fail
  • identify digital technologies to provide the required operating capabilities:
    • have we conducted proofs of concept?
    • experimented, tested  and validated?
    • reviewed use cases & success studies?
    • delivered small, important, scalable successes?

Matt,  PTC: Bringing Business Value to AR:

  • augmented service guidance
  • remote expert guidance
  • manufacturing: machine setup and turnover, assembly and process
  • example of Bell & Howell towers to store online sales in WalMart stores for customer pickup: very expensive to send one to a store for salesperson to use in sales — now just use AR app to give realistic demo without expense.
  • service: poor documentation organization, wants accurate, relevant, onsite info for technician. Want to remove return visits because the repair wasn’t done 1st time, or there’s a new technician. Manuals in binders, etc. Instead, with AR, requirements are quick access to current info. Finally, a demo.

Suchitra Bose, Accenture: Manufacturing IIoT, Driving the Speed of Digital Manufacturing:

  • convergence of IT and OT
  • expanding digital footprint across your entire factory
  • PTC has wide range of case studies (“use cases” in biz speak…) on aspects of IoT & manufacturing.

I have seen the future, and it’s written in Chalk (PTC’s Vuforia Chalk, that is!)

I just had to take time out from my live blogging of PTC’s LiveWorx ’18 to focus on one of the topics Jim Heppelmann mentioned in passing in his keynote: the new variation on the company’s Vuforia AR app: Chalk.

Significant in its own right, I suspect Chalk will have an additional, critical impact: democratizing AR.

It is an app aimed at, and accessible to, both corporate audiences AND the general public.  Downloadable for both iPhone & iPads & Android devices, I suspect that it will quickly become popular both to support remote repair staff for companies and just plain folks who are trying, for example to help a family member far away to deal with a car or plumbing repair. Not to mention the fact (mandatory disclaimer: while I work part-time for Apple, I’m not privy to any corporate internal strategy) that the spiffy new $329 6th-generation iPad really facilitates AR, and Chalk was developed in conjunction with the Apple ARKit technology so it should really become popular.

Chalk has two components:

  • real-time video and voice sharing of the same view
  • Chalk Marks, simple handswipes that allow one of the participants to highlight the part that is the subject of the question.  The “Marks” appear to be anchored to the subjects they’re “drawn” on.

Real-world uses vary from a remote super-expert helping a field technician to identify and deal with a rare problem to your millennial helping Mom master her personal technology. I saw an amazing demo this morning with one mechanic in Germany (ok, he was actually 2′ away…) directing the mechanic working on a Mercedes how to add coolant.  As the press release announcing the app said:

“Today, remote assistance can be frustrating and cumbersome. People struggle for words to describe things that are unfamiliar, whether it be a new appliance or the back of a cable box. And when the problem can’t be described clearly, it becomes almost impossible for someone else to solve. Vuforia Chalk provides a simple and intuitive solution where people can now use Chalk Marks to get a common understanding of a problem, and the steps required to solve it.”

I’ve written before that I suspected many companies got into e-commerce in the 9o’s because a CEO’s kids got him to order a book from Amazon during the holidays & he came back raving about this new device.  I can’t help thinking that this will be just the kind of low-cost (heck, in this case, no-cost) introduction to AR And the IoT that will break down some companies’ skepticism, pay off with immediate bottom line benefits in cost savings and efficiency in service operations, and get them interested in most expensive AR such as PTC’s digital twins and predictive maintenance.  Or, as ABI analyst Eric Abbruzzes said:

“Mainstream augmented reality is at the beginning of a strong positive inflection point, and Vuforia Chalk is a great example of how AR can transition from enterprise-only to use in everyday life,” said Eric Abbruzzese, ABI Research. “We see Vuforia Chalk as a fundamentally disruptive form of remote communication that will be well received across multiple sectors and for multiple use cases.”

Now to get my granddaughter to download the app so we can collaborate on the 3D-printer that I got her for her 12th- birthday!

“All of Us:” THE model for IoT privacy and security!

pardon me in advance:this will be long, but I think the topic merits it!

One of my fav bits of strategic folk wisdom (in fact, a consistent theme in my Data Dynamite book on the open data paradigm shift) is, when you face a new problem, to think of another organization that might have one similar to yours, but which suffers from it to the nth degree (in some cases, even a matter of literal life-or-death!).

That’s on the likelihood that the severity of their situation would have led these organizations to already explore radical and innovative solutions that might guide your and shorten the process. In the case of the IoT, that would include jet turbine manufacturers and off-shore oil rigs, for example.

I raise that point because of the ever-present problem of IoT privacy and security. I’ve consistently criticized many companies’ lack of attention to seriousness and ingenuity, and warned that this could result not only in disaster for these companies, but also the industry in general due to guilt-by-association.

This is even more of an issue since the May roll-out of the EU’s General Data Protection Regulation (GDPR), based on the presumption of an individual right to privacy.

Now, I have exciting confirmation — from the actions of an organization with just such a high-stakes privacy and security challenge — that it is possible to design an imaginative and effective process alerting the public to the high stakes and providing a thorough process to both reassure them and enroll them in the process.

Informed consent at its best!

It’s the NIH-funded All of Us, a bold effort to recruit 1 million or more people of every age, sex, race, home state, and state of health nationwide to speed medical research, especially toward the goal of “personalized medicine.” The researchers hope that, “By taking into account individual differences in lifestyle, environment, and biology, researchers will uncover paths toward delivering precision medicine.”

All of Us should be of great interest to IoT practitioners, starting with the fact that it might just save our own lives by leading to creation of new medicines (hope you’ll join me in signing up!). In addition, it parallels the IoT in allowing unprecedented degrees of precision in individuals’ care, just as the IoT does with manufacturing, operating data, etc.:

“Precision medicine is an approach to disease treatment and prevention that seeks to maximize effectiveness by taking into account individual variability in genes, environment, and lifestyle. Precision medicine seeks to redefine our understanding of disease onset and progression, treatment response, and health outcomes through the more precise measurement of molecular, environmental, and behavioral factors that contribute to health and disease. This understanding will lead to more accurate diagnoses, more rational disease prevention strategies, better treatment selection, and the development of novel therapies. Coincident with advancing the science of medicine is a changing culture of medical practice and medical research that engages individuals as active partners – not just as patients or research subjects. We believe the combination of a highly engaged population and rich biological, health, behavioral, and environmental data will usher in a new and more effective era of American healthcare.” (my emphasis added)


But what really struck me about All of Us’s relevance to IoT is the absolutely critical need to do everything possible to assure the confidentiality of participants’ data, starting with HIPP protections and extending to the fact that it would absolutely destroy public confidence in the program if the data were to be stolen or otherwise compromised.  As Katie Rush, who heads the project’s communications team told me, “We felt it was important for people to have a solid understanding of what participation in the program entails—so that through the consent process, they were fully informed.”

What the All of Us staff designed was, in my estimation (and I’ve been in or around medical communication for forty years), the gold standard for such processes, and a great model for effective IoT informed consent:

  • you can’t ignore it and still participate in the program: you must sign the consent form.
  • you also can’t short-circuit the process: it said at the beginning the process would take 18-30 minutes (to which I said yeah, sure — I was just going to sign the form and get going), and it really did, because you had to do each step or you couldn’t join — the site was designed so no shortcuts were allowed!:
    • first, there’s an easy-to-follow, attractive short animation about that section of the program
    • then you have to answer some basic questions to demonstrate that you understand the implications.
    • then you have to give your consent to that portion of the program
    • the same process is repeated for each component of the program.
  • all of the steps, and all of the key provisions, are explained in clear, simple English, not legalese. To wit:
    • “Personal information, like your name, address, and other things that easily identify participants will be removed from all data.
    • Samples—also without any names on them—are stored in a secure biobank”
    • “We require All of Us Research Program partner organizations to show that they can meet strict data security standards before they may collect, transfer, or store information from participants.
    • We encrypt all participant data. We also remove obvious identifiers from data used for research. This means names, addresses, and other identifying information is separate from the health information.
    • We require researchers seeking access to All of Us Research Program data to first register with the program, take our ethics training, and agree to a code of conduct for responsible data use.
    • We make data available on a secure platform—the All of Us research portal—and track the activity of all researchers who use it.
    • We enlist independent reviewers to check our plans and test our systems on an ongoing basis to make sure we have effective security controls in place, responsive to emerging threats.”

The site emphasizes that everything possible will be done to protect your privacy and anonymity, but it is also frank that there is no way of removing all risk, and your final consent requires acknowledging that you understand those limits:

“We are working with top privacy experts and using highly-advanced security tools to keep your data safe. We have several  steps in place to protect your data. First, the data we collet from you will be stored on=oyters with extra security portection. A special team will have clearance to process and track your data. We will limit who is allowed to see information that could directly identy you, like your name or social security number. In the unlikely event of a data breach, we will notify you. You are our partner, and your privacy will always be our top priority.”

The process is thorough, easy to understand, and assures that those who actually sign up know exactly what’s expected from them, what will be done to protect them, and that they may still have some risk.

Why can’t we expect that all IoT product manufacturers will give us a streamlined version of the same process? 


I will be developing consulting services to advise companies that want to develop common-sense, effective, easy-to-implement IoT privacy and security measures. Write me if you’d like to know more.

Mycroft Brings Open-Source Revolution to Home Assistants

Brilliant!  Crowd-funded (even better!) Mycroft brings the rich potential of open-source to the growing field of digital home assistants.   I suspect it won’t be long until it claims a major part of the field, because the Mycroft platform can evolve and grow exponentially by capitalizing on the contributions of many, many people, not unlike the way IFTTT has with its crowd-sourced smart home “recipes.”

According to a fascinating ZD Net interview with its developer, Joshua Montgomery, his motivation was not profit per se, but to create a general AI intelligence system that would transform a start-up space he was re-developing:

“He wanted to create the type of artificial intelligence platform that ‘if you spoke to it when you walked in the room, it could control the music, control the lights, the doors’ and more.”

                         Mycroft

Montgomery wanted to do this through an open-source voice control system but for there wasn’t an open source equivalent to Siri or Alexa.  After building the natural language, open-source AI system to fill that need (tag line, “An Artificial Intelligence for Everyone”) he decided to build a “reference device” as the reporter terms it (gotta love that techno speak. In other words, a hardware device that could demonstrate the system). That in turn led to a crowdsourced campaign on Kickstarter and Backerkit to fund the home hub, which is based on the old chestnut of the IoT, Raspberry Pi. The result is a squat, cute (looks like a smiley face) unit, with a high-quality speaker.  

Most important, when the development team is done with the AI platform, Mycroft will release all of the Mycroft AI code under GPL V3, inviting the open-source community to capitalize and improve on it.  That will place Mycroft squarely in the open-source heritage of Linux and Mozilla.

Among other benefits, Mycroft will use natural language processing to activate a wide range of online services, from Netflix to Pandora, as well as control your smart home devices.

Mycroft illustrates one of my favorite IoT Essential Truths: we need to share data, not hoard it. I don’t care how brilliant your engineers are: they are only a tiny percentage of the world population, with only a limited amount of personal experience (especially if they’re callow millennials) and interests. When you go open source and throw your data open to the world, the progress will be greater as will be the benefits — to you and humanity.

Liveblogging from Internet of Things Global Summit

Critical Infrastructure and IoT

Robert Metzger, Shareholder, Rogers Joseph O’Donnell 

  • a variety of constraints to direct government involvement in IoT
  • regulators: doesn’t trust private sector to do enough, but regulation tends to be prescriptive.
  • NIST can play critical role: standards and best practices, esp. on privacy and security.
  • Comparatively, any company knows more about potential and liabilities of IoT than any government body. Can lead to bewildering array of IoT regulations that can hamper the problem.
  • Business model problem: security expensive, may require more power, add less functionality, all of which run against incentive to get the service out at lowest price. Need selective regulation and minimum standards. Government should require minimum standards as part of its procurement. Government rarely willing to pay for this.
  • Pending US regulation shows constant tension between regulation and innovation.

             2017 IoT Summit

Gary Butler, CEO, Camgian 

  • Utah cities network embedding sensors.
  • Scalability and flexibility needed. Must be able to interface with constantly improving sensors.
  • Expensive to retrofit sensors on infrastructure.
  • From physical security perspective: cameras, etc. to provide real-time situational awareness. Beyond human surveillance. Add AI to augment human surveillance.
  • “Dealing with ‘data deluge.'”  Example of proliferation of drones. NIST might help with developing standards for this.
  • Battery systems: reducing power consumption & creating energy-dense batteries. Government could help. Government could also be a leader in adoption.

 

Cyber-Criminality, Security and Risk in an IoT World

John Carlin, Chair, Cybersecurity & Technology Program, Aspen Institute

  • Social media involved in most cyberwar attacks & most perps under 21.  They become linked solely by social media.
  • offensive threats far outstrip defenses when it comes to data
  • now we’re connecting billions of things, very vulnerable. Add in driverless cars & threat even greater. Examples: non-encrypted data from pacemakers, and the WIRED Jeep demo.

Belisario Contreras, Cyber Security Program Manager, Organization of American States

  • must think globally.
  • criminals have all the time to prepare, we must respond within minutes.
  • comprehensive approach: broad policy framework in 6 Latin American countries.

Samia Melhem, Global Lead, Digital Development, World Bank

  • projects: she works on telecommunications and transportation investing in government infrastructure in these areas. Most of these governments have been handicapped by lack of funding. Need expert data integrators. Integrating cybersecurity.

Stephen Pattison, VP Public Affairs, ARM

  • (yikes, never thought about this!) cyberterrorist hacks self-driving car & drives it into a crowds.
  • many cyber-engineers who might go to dark side — why hasn’t this been studied?
  • could we get to point where IoT-devices are certified secure (but threats continually evolve. Upgradeability is critical.
  • do we need a whistleblower protection?
  • “big data starts with little data”

Session 4: Key Policy Considerations for Building the Cars of Tomorrow – What do Industry Stakeholders Want from Policymakers?

Ken DiPrima, AVP New Product Development, IoT Solutions, AT&T

  • 4-level security approach: emphasis on end-point, locked-down connectivity through SIM, application level …
  • deep in 5-G: how do you leverage it, esp. for cars?
  • connecting 25+ of auto OEMs. Lot of trials.

Rob Yates, Co-President, Lemay Yates Associates

  • massive increase in connectivity. What do you do with all the data? Will require massive infrastructure increase.

Michelle Avary, Executive Board, FASTR, VP Automotive, Aeris

  • about 1 Gig of data per car with present cars. Up to 30 with a lot of streaming.
  • don’t need connectivity for self-driving car: but why not have connectivity? Also important f0r the vehicle to know and communicate its physical state. Machine learning needs data to progress.
  • people won’t buy vehicles when they are really autonomous — economics won’t support it, will move to mobility as a service.

Paul Scullion, Senior Manager, Vehicle Safety and Connected Automation, Global Automakers

  • emphasis on connected cars, how it might affect ownership patterns.
  • regulatory process slow, but a lot of action on state level. “fear and uncertainty” on state level. Balance of safety and innovation.

Steven Bayless, Regulatory Affairs & Public Policy, Intelligent Transportation Society of America

  • issues: for example, can you get traffic signals to change based on data from cars?
  • car industry doesn’t have lot of experience with collaborative issues.

How Are Smart Cities Being Developed and Leveraged for the Citizen?

Sokwoo Rhee, Associate Director of Cyber-Physical Systems Program, National Institute of Standards and Technology (NIST)

  • NIST GCTC Approach: Smart and Secure Cities. Partnered with Homeland Security to bring in cybersecurity & privacy at the basis of smart city efforts “Smart and Secure Cities and Communities Challenge”

Bob Bennett, Chief Innovation Officer, City of Kansas, MO

  • fusing “silos of awesomeness.”
  • 85% of data you need for smart cities already available.
  • “don’t blow up silos, just put windows on them.”
  • downtown is 53 smartest blocks in US
  • can now do predictive maintenance on roads
  • Prospect Ave.: neighborhood with worst problems. Major priority.
  • great program involving multiple data sources, to predict and take care of potholes — not only predictive maintenance but also use a new pothole mix that can last 12 years 
  • 122 common factors all cities doing smart cities look at!
  • cities have money for all sorts of previously allocated issues — need to get the city manager, not mayor, to deal with it
  • privacy and security: their private-sector partner has great resoures, complemented by the city’s own staff.

Mike Zeto, AVP General Manager, IoT Solutions, AT&T

  • THE AT&T Smart Cities guy. 
  • creating services to facilitate smart cities.
  • energy and utilities are major focus in scaling smart cities, including capital funding. AT&T Digital Infrastructure (done with GE) “iPhone for cities.”
  • work in Miami-Dade that improved public safety, especially in public housing. Similar project in Atlanta.
  • privacy and security: their resources in both have been one of their strengths from the beginning.

Greg Toth, Founder, Internet of Things DC

  • security issues as big as ever
  • smart city collaboration booming
  • smart home stagnating because early adopter boom over, value not sure
  • Quantified-Self devices not really taking hold (yours truly was one of very few attendees who said they were still using their devices — you’d have to tear my Apple Watch off).
  • community involvement greater than ever
  • looming problem of maintaining network of sensors as they age
  • privacy & security: privacy and security aren’t top priorities for most startups.

DAY TWO:

IoT TECH TALKS

  • Dominik Schiener, Co-Founder , IOTA speaking on blockchain
    • working with IoT version of blockchain for IoT — big feature is it is scaleable
    • why do we need it?  Data sets shared among all parties. Each can verify the datasets of other participants. Datasets that have been tampered are excluded.
    • Creates immutable single source of truth.
    • It also facilitates payments, esp. micropayments (even machine to machine)
    • Allows smart contracts. Fully transparent. Smart and trustless escrow.
    • Facilitates “machine economy”
    • Toward “smart decentralization”
    • Use cases:
      • secure car data — VW. Can’t be faked.
      • Pan-European charging stations for EVs. “Give machines wallets”
      • Supply chain tracking — probably 1st area to really adopt blockchain
      • Data marketplace — buy and sell data securely (consumers can become pro-sumers, selling their personal data).
      • audit trail. https://audit-trail.tangle.works
  • DJ Saul, CMO & Managing Director, iStrategyLabs IoT, AI and Augmented Reality
    • focusing on marketing uses.

Raising the bar for federal IoT Security – ‘The Internet of Things Cybersecurity Improvement Act’

  • Jim Langevin, Congressman, US House of Representatives
    • very real threat with IoT
    • technology outpacing the law
    • far too many manufacturers don’t make security a priority. Are customers aware?
    • consumers have right to know about protections (or lack thereof)
    • “failure is not an option”
    • need rigorous testing
  • Beau Woods, Deputy Director, Cyber Statecraft Initiative, Atlantic Council
    • intersection of cybersecurity & human condition
    • dependence on connected devices growing faster than our ability to regulate it
    • UL developing certification for medical devices
    • traceability for car parts
  • John Marinho, Vice President Cybersecurity and Technology, CTIA
    • industry constantly evolving global standards — US can’t be isolated.
    • cybersecurity with IoT must be 24/7. CTIA created an IoT working group, meets every two weeks online.
    • believe in public/private partnerships, rather than just regulatory.

Session 9: Meeting the Short and Long-Term Connectivity Requirements of IoT – Approaches and Technologies

  •  Andreas Geiss, Head of Unit ‘Spectrum Policy’, DG CONNECT, European Commission
    • freeing up a lot of spectrum, service neutral
    • unlicensed spectrum, esp. for short-range devices. New frequency bands. New medical device bands. 
    • trying to work with regulators globally to allow for globally-usable devices.
  • Geoff Mulligan, Chairman, LoRa Alliance; Former Presidential Innovation Fellow, The White House
    • wireless tradeoffs: choose two — low power/long distance/high speed.
    • not licensed vs. unlicensed spectrum. Mix of many options, based on open standards, all based on TCP/IP
    • LPWANs:
      • low power wide area networks
      • battery operated
      • long range
      • low cost
      • couple well with satellite networks
    • LoRaWAN
      • LPWAN based on LoRa Radio
      • unlicensed band
      • open standards base
      • openly available
      • open business model
      • low capex and opex could covered entire country for $120M in South Korea
      • IoT is evolutionary, not revolutionary — don’t want to separate it from other aspects of Internet
  • Jeffrey Yan, Director, Technology Policy, Microsoft
    • at Microsoft they see it as critical for a wide range of global issues, including agriculture.
  • Charity Weeden, Senior Director of Policy, Satellite Industry Association
    • IoT critical during disasters
    • total architecture needs to be seamless, everywhere.
  • Andrew Hudson, Head of Technology Policy, GSMA
    • must have secure, scalable networks

Session 10: IoT Data-Ownership and Licencing – Who Owns the Data?

  • Stacey Gray, Policy Lead IoT, Future Privacy Forum 
    • consumer privacy right place to begin.
    • need “rights based” approach to IoT data
    • at this point, have to show y0u have been actually harmed by release of data before you can sue.
  • Patrick Parodi, Founder, The Wireless Registry
    • focus on identity
    • who owns SSID identities? How do you create an identity for things?
  • Mark Eichorn, Assistant Director, Division of Privacy and Identity Protection, Federal Trade Commission 
    • cases involving lead generators for payday loan. Reselling personal financial info.
  • Susan Allen, Attorney-Advisor, Office of Policy and International Affairs, United States Patent & Trademark Office 
    • focusing on copyright.
    • stakeholders have different rights based on roles
  • Vince Jesaitis, Director, US Public Affairs, ARM
    • who owns data depends on what it is. Health data very tough standards. Financial data much more loose.
    • data shouldn’t be treated differently if it comes from a phone or a browser.
    • industrial side: autonomous vehicle data pretty well regulated.  Pending legislation dealing with smart cities emphasis open data.

2nd day liveblogging, Gartner ITxpo, Barcelona

Accelerating Digital Business Transformation With IoT Saptarshi Routh Angelo Marotta
(arrived late, mea culpa)

  • case study (didn’t mention name, but just moved headquarters to Boston. Hmmmmm).
  • you will be disrupted by IoT.
  • market fragmented now.

Toshiba: How is IoT Redefining Relationships Between Customers and Suppliers, Damien Jaume, president, Toshiba Client Solutions, Europe:

  • time of tremendous transformation
  • by end of ’17, will surpass PC, tabled & phone market combined
  • 30 billion connect  devices by 2020
  • health care IoT will be $117 billion by 2020
  • 38% of indiustry leaders disrupted by digitally-enabled competitors by 2018
  • certainty of customer-supplier relationship disruption will be greatest in manufacturing, but also every other market
    • farming: from product procurement to systems within systems. Smart, connected product will yield to integrated systems of systems.
  • not selling product, but how to feed into whole IoT ecosystem
  • security paramount on every level
  • risk to suppliers from new entrants w/ lean start-up costs.
  • transition from low engagement, low trust to high engagement, high trust.
  • Improving efficiencies
  • ELIMINATE MIDDLEMAN — NO LONGER RELEVANT
  • 4 critical success factors:
    • real-time performance pre-requisite
    • robustness — no downtime
    • scalability
    • security
  • case studies: energy & connected home, insurance & health & social care (Neil Bramley, business unit director for clients solutions
    • increase depth of engagement with customer. Tailored information
    • real-time performance is key, esp. in energy & health
    • 20 million smart homes underway in GB by 2020:
      • digitally empowering consumers
      • engaging consumers
      • Transforming relationships among all players
      • Transforming homes
      • Digital readiness
    • car insurance: real-time telematics.
      • real-time telematics data
      • fleet management: training to reduce accidents. Working  w/ Sompo Japan car insurance:
    • Birmingham NHS Trust for health (Ciaron Hoye, head of digital) :
      • move to health promotion paradigm
      • pro-actively treat patients
      • security first
      • asynchronous communications to “nudge” behavior.
      • avoiding hip fractures
      • changing relationship w/ the patient: making them stakeholders, involving in discussion, strategy
      • use game theory to change relationship

One-on-one w/ Christian Steenstrup, Gartner IoT analyst. ABSOLUTE VISIONARY — I’LL BE INTERVIEWING HIM AT LENGTH IN FUTURE:

  • industrial emphasis
  • applications more ROI driven, tangible benefits
  • case study: mining & heavy industry
    • mining in Australia, automating entire value train. Driverless. Driverless trains. Sensors. Caterpillar. Collateral benefits: 10% increase in productivity. Less payroll.  Lower maintenance. Less damage means less repairs.
    • he downplays AR in industrial setting: walking in industrial setting with lithium battery strapped to your head is dangerous.
    • big benefit: less capital expense when they build next mine. For example, building the town for the operators — so eliminate the town!
  • take existing processes & small improvements, but IoT-centric biz, eliminating people, might eliminate people. Such as a human-less warehouse. No more pumping huge amount of air underground. Huge reduction with new system.  Mine of future: smaller holes. Possibility  of under-sea mining.
  • mining has only had incremental change.
  • BHP mining’s railroad — Western Australia. No one else is involved. “Massive experiment.”
  • Sound sensing can be important in industrial maintenance.  All sorts of real-time info. 
  • Digital twins: must give complete info — 1 thing missing & it doesn’t work.
  • Future: 3rd party data brokers for equipment data.
  • Privacy rights of equipment.
  • “communism model” of info sharing — twist on Lenin.

 

Accelerating Digital Transformation with Microsoft Azure IoT Suite (Charlie Lagervik):

  • value networking approach
  • customer at center of everything: customer conversation
  • 4 imperatives:
    • engage customers
    • transform products
    • empower employees
    • optmize operations
  • their def. of IoT combines things/connectivity/data/analytics/action  Need feedback loop for change
  • they focus on B2B because of efficiency gains.
  • Problems: difficult to maintain security, time-consuming to launch, incompatible with current infrastructure, and hard to scale.
  • Azure built on cloud.
  • InternetofYourThings.com

 

Afternoon panel on “IoT of Moving Things” starts with all sorts of incredible factoids (“since Aug., Singapore residents have had access to self=driving taxis”/ “By 2030, owning a car will be an expensive self-indulgence and will no longer be legal.”

  • vehicles now have broader range of connectivity now
  • do we really want others to know where we are? — privacy again!
  • who owns the data?
  • what challenges do we need to overcome to turn data into information & valuable insight that will help network and city operators maximize efficiency & drive improvement across our transportation network?
  • think of evolution: now car will be software driven, then will become living room or office.
  • data is still just data, needs context & location gives context.
  • cities have to re-engineer streets to become intelligent streets.
  • must create trust among those who aren’t IT saavy.
  • do we need to invest in physical infrastructure, or will it all be digital?
  • case study: one car company w/ engine failures in 1 of 3 cars gave the consultants data to decide on what was the problem.

Live Blogging Gartner ITxpo Barcelona!

After a harrowing trip via Air France (#neveragain) I’m in lovely Barcelona, live-blogging Gartner ITxpo courtesy of Siemens — but they aren’t dictating my editorial judgment.

Keynoter is Peter Sondergaard, Sr. VP, Gartner Research:

  • start with high-scale traditional IT structures, but with new emphasis on cloud, etc. IT system now partially inside your org. and part outside.  We are half-way through transition to cloud: half of sales support now through cloud. More financial, HR & other functions. General trend toward cloud, but still some internal processes as necessary. Must clean up traditional inside processes.
    • “Ecosystems are the next evolution of Digital”
    • Must learn to measure your investments in customer experience.
    • Starting to explore VR & AR (personal shout out to PTC & clients such as Caterpillar!!)
    • must understand customer’s intent through advanced algorithms.  Create solutions to problems they don’t even know they have!
  • next domain of new platform: Things:
    • build strategies with two lenses: consumer preferences, AND the enterprise IoT lens.
    • leverage exponential growth in connected things
    • 27445 exabytes of data by 2020!
    • can’t just bolt on new systems on old ones: must rework existing systems to include devices — processes, workflow, much harder (i.e., my circular company paradigm).
  • intelligence: how your systems learn and decide independently
    • algorithms– algorithmic intelligence — drives decisions
    • now, AI, driven by machine learning. Machines learn from experience.
    • information is new code base
    • we will employ people to train things to learn from experience through neural networks
  • ecosystems
    • linear value supply chains transformed to ecosystems through electronic interchange.
    • others can build experiences, etc. that you haven’t thought out through APIs  — my “share data” Essential Truth. APIs implement business policies in the digital world.c
  • customers
    • customer driven

Where to start?

  • 70% of IoT implementation is through new organization within companies!

Now other Gartner analysts chime in:

  • insurance: engage your customers.
  • smart gov: must interact with those who implement. Must re-imaging public involvement sense/engage/interact
  • case study: Deakin University in Australia: digital platforms to enhance student experience.
  • case study: Trenitalia mass transit system switching to predictive maintenance! Huge cost savings. “Experience hands & beginners mind at work” — love that slogan!!!! “Listen to the train instead of scheduling maintenance”
  • blockchain: ecosystem, brilliant in simplicity. All can see transaction but no one can invade privacy. Use to solve many problems: data provenance, land registry, public infrastucture, AI.
  • Woo: use this to TRANSFORM THE WORLD!!!
  • ratz — I was preoccupied at time, they talked about a new mobility system for seniors — re my SmartAging paradigm!!
  • paradigm shift — partnering with competitors (much of what I wrote about in DataDynamite: share data, don’t hoard it!)  Think about Apple & Google driving car companies’ interfaces. “Do you join hands with digital giants or join hands with them?”).
  • ooh, love the digital assistant correcting his presentation. I can only dream of a future where there are millions added to grammar police!

 

 

Circular Company: Will Internet of Things Spark Management Revolution?

Could the IoT’s most profound impact be on management and corporate organization, not just cool devices?

I’ve written before about my still-being-refined vision of the IoT — because it (for the first time!) allows everyone who needs instant access to real-time data to do their jobs and make better decisions to share that data instantly —  as the impetus for a management revolution.

My thoughts were provoked by Heppelmann & Porter’s observation that:

“For companies grappling with the transition (to the IoT), organizational issues are now center stage — and there is no playbook. We are just beginning the process of rewriting the organization chart that has been in place for decades.”

If I’m right, the IoT could let us switch from the linear and hierarchical forms that made sense in an era of serious limits to intelligence about things and how they were working at thaFor companies grappling with the transition, organizational issues are now center stage—and there is no playbook. We are just beginning the process of rewriting the organization chart that has been in place for decades.t moment, to circular forms that instead eliminate information “silos” and instead give are circular, with IoT data as the hub. 

This article expands on that vision. I’ve tried mightily to get management journals to publish it. Several of the most prestigious have given it a serious look but ultimately passed on it. That may be because it’s crazy, but I believe it is feasible today, and can lead to higher profits, lower operating costs, empowering our entire workforces, and, oh yeah, saving the planet.

Audacious, but, IMHO, valid.  Please feel free to share this, to comment on it, and, if you think it has merit, build on it.

Thanks,

W. David Stephenson


The IoT Allows a Radical, Profitable Transformation to Circular Company Structure

 

by

W. David Stephenson

Precision assembly lines and thermostats you can adjust while away from home are obvious benefits of the Internet of Things (IoT), but it might also trigger a far more sweeping change: swapping outmoded hierarchical and linear organizational forms for new circular ones.

New org charts will be dramatically different because of an important aspect of the IoT overlooked in the understandable fascination with cool devices. The IoT’s most transformational aspect is that, for the first time,

everyone who needs real-time data to do their jobs better or
make better decisions can instantly 
share it.

That changes everything.

Linear and hierarchical organizational structures were coping mechanisms for the severe limits gathering and sharing data in the past. It made sense then for management, on a top-down basis, to determine which departments got which data, and when.

The Internet of Things changes all of that because of huge volumes of real-time data), plus modern communications tools so all who need the data can share it instantly. 

This will allow a radical change in corporate structure and functions from hierarchy: make it cyclical, with real-time IoT data as the hub around which the organization revolves and makes decisions.

Perhaps the closest existing model is W.L. Gore & Associates. The company has always been organized on a “lattice” model, with “no traditional organizational charts, no chains of command, nor predetermined channels of communication.”  Instead, they use cross-disciplinary teams including all functions, communicating directly with each other. Teams self-0rganize and most leaders emerge spontaneously.

As Deloitte’s Cathy Benko and Molly Anderson wrote, “Continuing to invest in the future using yesteryear’s industrial blueprint is futile. The lattice redefines workplace suppositions, providing a framework for organizing and advancing a company’s existing incremental efforts into a comprehensive, strategic response to the changing world of work.”  Add in the circular form’s real-time data hub, and the benefits are even greater, because everyone on these self-organizing teams works from the same data, at the same time.

You can begin to build such a cyclical company with several incremental IoT-based steps.

One of the most promising is making the product design process cyclical. Designers used to work in a vacuum: no one really knew how the products functioned in the field, so it was hard to target upgrades and improvements. Now, GE has found it can radically alter not only the upgrade process, but also the initial design as well:

“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. ‘We’re getting these offerings done in three, six, nine months,’ (Vice-President of Global Software William Ruh said). ‘It used to take three years.’”

New IoT and data-analytics tools are coming on the market that could facilitate such a shift. GE’s new tool, “Digital Twins,” creates a wire-frame replica of a product in the field (or, for that matter, a human body!) back at the company. Coupled with real-time data on its status, it lets everyone who might need to analyze a product’s real-time status (product designers, maintenance staff, and marketers, for example) to do so simultaneously.

The second step toward a cyclical organization is breaking down information silos.

Since almost every department has some role in creation and sales of every product, doesn’t it make sense to bring them together around a common set of data, to explore how that data could trigger coordinated actions by several departments? 

Collaborative big-data analysis tools such as GE’s Predix, SAP’s HANA, and Tableau facilitate the kind of joint scrutiny and “what-if” discussions of real-time data that can make circular teamwork based on IoT-data sharing really achieve its full potential.

The benefits are even greater when you choose to really think in circular terms, sharing instant access to that real-time data not only companywide, but also with external partners, such as your supply chain and distribution network – and even customers – not just giving them some access later on a linear basis.  For example, SAP has created an IoT-enabled vending machine. If a customer opts in, s/he is greeted by name, and may be offered “your regular combination” based on past purchases, and/or a real-time discount. That alone would be neat from a marketing standpoint, but SAP also opened the resulting data to others, resulting in important logistics improvements. Real-time machine-to-machine (M2M) data about sales at the new vending machines automatically reroute resupply trucks to those machines currently experiencing the highest sales. 

With the IoT, sharing data can make your own product or service more valuable. With the Apple HomeKit, you can say “Siri, it’s time for bed,” and the Hue lights dim, Schlage lock closes, and Ecobee thermostat turns down. By sharing real-time IoT data, each of these companies’ devices become more valuable in combinations than they are by themselves.

Hierarchical and linear management is outmoded in the era of real-time data from smart devices. It is time to begin to replace it with a dynamic, circular model with IoT data as its hub.

Live Blogging from SAP’s SCM CRM IoT 2016 – Day 2

Greg Gorbach, ARC Advisory Group, Industrial Internet of Things:

  • ARC is an analyst firm, in Boston.
  • new service models
  • new products
  • new production techniques
  • new business processes
  • new competitors
  • new partners
  • new workers
  • new business opportunities.
  • innovation improves competitiveness: value-based competitiveness raises value of output.
  • Drivers:
    • reduced machine or asset downtime
    • more rapid service response
    • improved process performance
    • improved personnel productivity
    • reduced machine or asset lifecycle costs
    • improved asset utilization/RoA
    • opportunity for business innovation
    • ability to sell products as a service
  • manufacturing momentum for digital transformation: factors include 3D printing, IoT technologies, changing economies of scale, new service models
  • goal is digital transformation
  • software transitioning from monolithic to microservices

Richard Howells, SAP:

  • IoT is all about re-imaging things:business process, customer experiences
  • SAP solutions for IoT
    • SAP Connected Assets
    • SAP Connected Manufacturing
    • SAP Network Logistics Hub
    • SAP Augmented Reality Solutions
  • SAP Predictive Maintenance and Service: leverage operational insights to drive innovation & new business models
    • Deere putting sensors everywhere, doing predictive maintenance of tractors. In some cases, leasing instead of selling, so they have incentive to keep it operating.
    • Kaeser Compressors
    • Asset Intelligence Network
    • Connective Manufacturing: leveraging big data to drive new insights into operations.
      • Example of Harley Plant in York, Pa.  Many new design options (1,700 options), but do 25% more bikes with 30% fewer people. Went from 21 days for a custom cycle top 6 hours.
      • Pepsi: improving asset utilization with SAP Connected Manufacturing: collect all downtime and loss data in real time.  Went from 65 to 85% asset use.
    • SAP Networked Logistics Hub
    • SAP AR Warehouse Picker
    • SAP AR Service Technician

Where is IoT going??

  • 68% of companies see IoT being strategic or transformational to their business.
  • 78% plan to invest in IoT  in next 24 mo. — 24% already have.
  • Increasing productivity and improving customer experience are top business benefits
  • Challenges to deploying IoT include unclear ROI, lack of industry standards, costs, and data security.

 

Next was my presentation on “Getting Started With the IoT,” in which I emphasized that companies that have hung back from the IoT are still in the majority, but had better heed John Chambers’ warning that they’ll be toast in just a few years if they don’t start now.  I emphasized that an ideal early focus is to build the efficiency or “precision” of your existing operations, and to build operating safety (especially in inherently dangerous settings such as construction sites), then move on to more radical transformation.  I cited GE’s rather modest goal (I think they’re understating it, based on their own internal results) of a 1% increase in productivity for the IoT as something that most companies could achieve, and then talked about GE’s Brilliant Factories as a model for increasing operating efficiency, zeroing in on my favorite example, the Durathon Battery plant, where a sensor on every battery and 10,000 on the assembly line give them tremendous flexibility to cope with differing situations and to increase efficiency.  Finally, I suggested that the companies begin to rethink the role of their products and to begin considering the “circular enterprise” vision I’ve articulated as they look to the future.


 

Kris Gorrepati, SAP “IoT: from Big Data to Smart Data to Outcomes.”

  • OK, I’d never heard of a Brontobyte before…
  • “IoT relevant to all industries.” Agreed.
  • Amazon Dash service (Whirlpool now building it in!)
  • Uses same curve that other SAP guys do: from connect to transform to reimagine (latter being empowering new biz models, value-added products and services.
  • HANA Cloud Platform for the IoT.

Live Blogging from SAP’s SCM CRM IoT 2016

I’m back in Sodom and Gomorrah in the desert, AKA Las Vegas, to speak at another SAP IoT conference: SCM CRM IoT 2016, and to live blog again!

Keynoters: Hans Thalbauer, sr. vp of extended supply chain solutions at SAP, and Dr. Volker G. Hildebrand, global vp or customer engagement & commerce for SAP Hybris:
Hildebrand:

  • theme: move beyond traditional CRM: look at entire customer journey
  • you have to meet customer expectations for convenience, relevance, reliability, and in real-time.
  • real lesson from Uber: customers upend markets, not companies; carry power of internet in their pocket; if you’re fighting alone, you have no chance of success;
  • when London cabbies went on strike, Uber membership went up 850% in 3 days.
  • “74% of execs. believe digital transformation is improving value for customers”
  • must thinking beyond CRM: 2 of 3 companies don’t think their CRM doesn’t support their future needs for customer engagement.
  • blend marketing & commerce.
  • personalization is key to digital commerce.
  • beyond service: customer served before, during & after buy; flawless field service. 53% abandon online purchase if they don’t be quick answers to questions.
  • why no app from cable provider allowing you to get assistance Uber-style? Instead, hold on phone.
  • One-to-one future is here.
  • Omnichannel selling
  • By 2020: 1 million fewer B2B sales reps (@Forrester)
  • EY: enabled collaboration with 15,000 client partners
  • “Engage your customers like never before:” commerce, marketing, service & sales.

Bob Porter, Pregis (protective packaging):

  • liked ease of use with Hybris (vs. Salesforce)

Thalbauer (digital transformation of supply chain):

  • end-consumer driven economy
  • very related to IoT
  • tech adoption accelerating
  • biz model transformation
  • instant notification if the equipment malfunctions
  • change of business transformation
  • disruption in every aspect of business:
    • customer-centric (demand sensing, omni-channel sales, same-day delivery)
    • individualized products (configured products, digitalized inventory, lot size of one)
    • resource scarcity (talent, sustainability, natural resources)
    • sharing economy (social networks, business networks, asset networks)
  • sweet: combo of 3-D printing at warehouse & Uber-based model for final delivery.
  • extended supply chain demo: sweet (literally): 3-D printing of chocolates at high-end stores! — wonderful example of IoT data-centric enterprise
  • SAP increasing pace of innovation
    • fastest-growing planning solution in history
    • only live logistics platform in the market
    • product innovation platform re-defined
    • demand-driven manufacturing
    • digital assets.

Next up: Sacha Westermann, Port of Hamburg, on how it uses IoT to streamline operations, improve efficiency & reduce accidents through “smartPORT”:

  • it’s very big (largest port in Germany), and very complex! Ships, rail (largest rail hub in Europe), trucking. 24/7.
  • big emphasis on environment: need to reduce emissions, improve sustainability.
  • can’t expand area, but must be able to handle more volume.
  • key factor is connectivity between all parties.
  • smartPORT includes energy & logistics.
  • smart maintenance: use mobile to call up SAP order & create messages, take photos. Example of malfunction with a drawbridge. Technician got new button from stock, installed it, customers didn’t even know there was a problem.
  • port monitor: digital map with all info to operate the harbor. Mobile version on iPad.
  • SmartSwitch for rail: sensors on the switches to measure conditions. Automated data flow to maintenance company.
  • dynamic info on traffic volumes: combines all real-time data on traffic. Detects available parking spaces. Created “PrePort Parking” as holding area for trucks that are early or late. Trucks park bumper-to-bumper for maximum efficiency.
  • special traffic lights: cycle changes based on real-time traffic flow. Warning messages if pedestrians cross.
  • smartROAD: smart sensing of the bridge-structural load — identifies interdependencies and to do predictive maintenance.
  • Take aways:
    • good application requires lot of data
    • must share data
    • data privacy critical for confidence
    • everyone gets just info they need
    • more participants, higher the benefit for each
    • open interfaces basic
    • application must be self-explanatory

Next up: me!, on 4 Essential Truths of IoT & how that translates into strategy.


 

Mike Lackey, IoT Extended Supply Chain, SAP explaining their IoT strategy & direction, with emphasis on “driving customer value”:

  • he’s using universe of 75 billion connected devices by 2022.
  • case study: STILL, the smart lift truck from Germany. Forklift sold as service, based on weight of materials carried. They will communicate among themselves, M2M.
  • “It is not about Things, it is about what the Things can do to radically transform business processes!”
  • oil & gas: reducing spills. They worked with the company that made the platform that failed in Deep Horizon — hadn’t been maintained in years.
  • Burbury: want to know exactly what you looked at, share the info among their stores. Creepy: invasion of privacy??
  • UnderArmour: why do you have to wear a band — build sensors right into clothes.
  • Hagleitner (I reported about them at last SAP event) provides supplies for corporate washrooms, etc. Paradigm shift: sensors let them know which dispensers need new materials. “big washroom data
  • applications: drive adoption with a few killer applications. Differentiate with “Thing to Outcome”
  • cloud: leading cloud experience for customers and partners at lowest TCO
  • platform: open big data platform. high-value services for SAP, customer & partner
  • Kaeser Compressors also made paradigm shift: no longer sell air compressors, but air — must guarantee it works constantly. Million data points per compressor daily. Differentiates them from competitors.
  • one tractor company now can recommend to farmers what they should plant based on data from sensors on the plows.
  • Asset Intelligence Network: great example of data sharing for mutual advantage. To be released soon.
  • Enables connected driving experience.
  • SAP IoT Starter Kit can get you started.
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