Category Archives: Web 3.0

Consumerism via IoT @ IT2I in Munich

We buy things we don’t need
with money we don’t have
to impress people we don’t like

Tyler Durden
Fight Club


Consumption sucks

That sucks. That got to be changed. Fight Club changed it that violent way… Thanks God it was in book/movie only. We are changing it different way, peacefully, via consumerism. We are powerful consumers in new economy – Experience Economy. Consumers don’t need goods only, they need experiences, staged from goods, services and something else.


Staging experience is difficult. Staging personal experience is a challenge for this decade. We have to gather, calculate and predict about literally each customer. The situation gets more complicated with growing Do It Yourself attitude from consumers. They want to make it, not just to buy it…

If you have not so many customers then staging of experience could be done by people, e.g. Vitsoe. They are writing on letter cards exclusively for you! To establish realistic human-human interface from the very beginning. You, as consumer, do make it, by shooting pictures of your rooms and describing the concept of your shelving system. New Balance sneakers maker directly provides “Make button”, not buy button, for number of custom models. You are involved into the making process, it takes 2 days, you are informed about the facilities [in USA] and so on; though you are just changing colors of the sneaker pieces, not a big deal for a man but the big deal for all consumers.

There are big etalons in Experience Economy to look for: Starbucks, Walt Disney. Hey, old school guys, to increase revenue and profit think of price goes up, and cost too; think of staging great experiences instead of cutting costs.


Webization of life

Computers disrupted our lives, lifestyle, work. Computers changed the world and still continue to change it. Internet transformed our lives tremendously. It was about connected machines, then connected people, then connected everything. The user used to sit in front of computer. Today user sits within big computer [smart house, smart ambient environment, ICU room] and wears tiny computers [wristbands, clasps, pills]. Let’s recall six orders of magnitude for human-machine interaction, as Bill Joy named them – Six Webs – Near, Hear, Far, Weird, B2B, D2D.

Nowadays we see boost for Hear, Weird, D2D. Reminder what they are: Hear is your smartphone, smartwatch [strange phablets too], wearables; Weird is voice interface [automotive infotaintent, Amazon Echo]; D2D is device to device or machine to machine [aka M2M]. Wearables are good with anatomical digital gadgets while questionable with pseudo-anatomical like Google Glass. Mobile first strategies prevail. Voice interop is available on all new smartphones and cars. M2M is rolling out, connecting “dumb” machines via small agents, which are connected to the cloud with some intelligent services there.

At the end of 2007 we experienced 5000 days of the Web. Check out what Kevin Kelly predicts for next 5000 days [actually less than 3000 days from now]. There will be only One machine, its OS is web, it encodes trillions of things, all screens look into One, no bits live outside, to share is to gain, One reads it all, One is us…


Webization of things

Well, next 3000 days are still to come, but where we are today? At two slightly overlapping stages: Identification of Everything and Miniaturization & Connecting of Everything. Identification difficulties delay connectivity of more things. Especially difficult is visual identification. Deep Neural Networks did not solve the problem, reached about 80% accuracy. It’s better than old perceptrons but not sufficient for wide generic application. Combinations with other approaches, such as Random Forests bring hope to higher accuracy of visual recognition.

Huge problem with neural networks is training. While breakthrough is needed for ad hoc recognition via creepy web camera. Intel released software library for computer vision OpenCV to engage community to innovate. Then most useful features are observed, improved and transferred from sw library into hw chips by Intel. Sooner or later they are going to ship small chips [for smartphones for sure] with ready-made special object recognition bits processing, so that users could identify objects via small phone camera in disconnected mode with better accuracy than 85-90%, which is less or more applicable for business cases.


As soon as those two IoT stages [Identification and Miniaturization] are passed, we will have ubiquitous identification of everything and everyone, and everything and everyone will be digitized and connected – in other words we will create a digital copy of us and our world. It is going to be completed somewhere by 2020-2025.

Then we will augment ourselves and our world. Then I don’t know how it will unfold… My personal vision is that humanity was a foundation for other more intelligent and capable species to complete old human dream of reverse engineering of this world. It’s interesting what will start to evolve after 2030-2040. You could think about Singularity. Phase shift.


Hot industries in IoT era

Well, back to today. Today we are still comfortable on the Earth and we are doing business and looking for lucrative industries. Which industries are ready to pilot and rollout IoT opportunities right away? Here is a list by Morgan Stanley since April 2014:

Utilities (smart metering and distribution)
Insurance (behavior tracking and biometrics)
Capital Goods (factory automation, autonomous mining)
Agriculture (yield improvement)
Pharma (critical trial monitoring)
Healthcare (leveraging human capital and clinical trials)
Medtech (patient monitoring)
Automotive (safety, autonomous driving)


What IoT is indeed?

Time to draw baseline. Everybody is sure to have true understanding of IoT. But usually people have biased models… Let’s figure out what IoT really is. IoT is synergistic phenomenon. It emerged at the interaction of Semiconductors, Telecoms and Software. There was tremendous acceleration with chips and their computing power. Moore’s Law still has not reached its limit [neither at molecular nor atomic level nor economic]. There was huge synergy from wide spread connectivity. It’s Metcalfe’s Law, and it’s still in place, initially for people, now for machines too. Software scaled globally [for entire planet, for all 7 billions of people], got Big Data and reached Law of Large Numbers.


As a result of accelerated evolution of those three domains – we created capability to go even further – to create Internet of Things at their intersection, and to try to benefit from it.


Reference architecture for IoT

If global and economic description is high-level for you, then here you go – 7 levels of IoT – called IoT Reference Architecture by Cisco, Intel and IBM in October 2014 at IoT World Forum. A canonical model sounds like this: devices send/receive data, interacting with network where the data is transmitted, normalized and filtered using edge computing before landing in databases/data storage, accessible by applications and services, which process it [data] and provide it to people, who will act and collaborate.



Who is IoT?

You could ask which company is IoT one. This is very useful question, because your next question could be about criteria, classifier for IoT and non-IoT. Let me ask you first: is Uber IoT or not?

Today Uber is not, but as soon as the cars are self-driven Uber will be. An only missing piece is a direct connection to the car. Check out recent essay by Tim O’Reilly. Another important aspect is to mention society, as a whole and each individual, so it is not Internet of Things, but it is Internet of Things & Humans. Check out those ruminations

Humans are consumers, just a reminder. Humans is integral part of IoT, we are creating IoT ourselves, especially via networks, from wide social to niche professional ones.


Software is eating the world

Chips and networks are good, let’s look at booming software, because technological process is depending vastly on software now, and it’s accelerating. Each industry opens more and more software engineering jobs. It started from office automation, then all those classical enterprise suites PLM, ERP, SCADA, CRM, SCM etc. Then everyone built web site, then added customer portal, web store, mobile apps. Then integrated with others, as business app to business app aka B2B. Then logged huge clickstreams and other logs such as search, mobile data. Now everybody is massaging the data to distill more information how to meet business goals, including consumerism shaped goals.

  1. Several examples to confirm that digitization of the world is real.
    Starting from easiest example for understanding – newspapers, music, books, photography, movies went digital. Some of you have never seen films and film cameras, but google it, they were non-digital not so long ago. Well, last example from this category is Tesla car. It is electrical and got plenty of chips with software & firmware on them.
  2. Next example is more advanced – intellectual property shifts to digital models of goods. 3D model with all related details does matter, while implementation of that model in hard good is trivial. You have to pay for the digital thing, then 3D print it at home or store. As soon as fabrication technology gets cheaper, the shift towards digital property will be complete. Follow Formula One, their technologies are transferred to our simpler lives. There is digital modeling and simulations, 3D printed into carbon, connected cars producing tons of telemetry data. As soon as consumer can’t distinguish 3D printed hard goods from produced with today’s traditional method, and as soon as technology is cheap enough – it is possible to produce as late as possible and as adequate as possible for each individual customer.
  3. All set with hard goods. What about others? Food is also 3D printed. First 3D printed burger from Modern Meadow was printed more than year ago, BTW funded by googler Sergey Brin. The price was high, about $300K, exactly the amount of his investment. Whether food will be printed or produced via biotech goo, the control and modeling will be software. You know recipes, processes, they are digital. They are applied to produce real food.
  4. Drugs and vaccines. Similar to the food and hard goods. Just great opportunity to get quick access to the brand new medications is unlocked. The vaccine could be designed in Australia and transferred as digital model to your [or nearby] 3D printer or synthesizer, your instance will be composed from the solutions and molecules exclusively, and timely.

So whatever your industry is, think about more software coding and data massage. Plenty of data, global scale, 7 billions of people and 30 billions of internet devices. Think of traditional and novel data, augmented reality and augmented virtuality are also digitizers of our lives towards real virtuality.


How  to design for IoT?

If you know how, then don’t read further, just go ahead with your vision, I will learn from you. For others my advice will be to design for personal experience. Just continue to ride the wave of more & more software piece in the industries, and handle new software problems to deliver personal experience to consumers.

First of all, start recognizing novel data sources, such as Search, Social, Crowdsourced, Machine. It is different from Traditional CRM, ERP data. Record data from them, filter noise, recognize motifs, find intelligence origins, build data intelligence, bind to existing business intelligence models to improve them. Check out Five Sources of Big Data.

Second, build information graphs, such as Interest, Intention, Consumption, Mobile, Social, Knowledge. Consumer has her interests, why not count on them? Despite the interests consumer’s intentions could be different, why not count on them? Despite the intentions consumer’s consumption could be different, why not count on them? And so on. Build mobility graph, communication graph and other specific graphs for your industry. Try to build a knowledge graph around every individual. Then use it to meet that’s individual expectations or bring individualized unexpected innovations to her. Check out Six Graphs of Big Data.

As soon as you grasp this, your next problem will be handling of multi-modality. Make sure you got mathematicians into your software engineering teams, because the problem is not trivial, exactly vice versa. Good that for each industry some graph may prevail, hence everything else could be converted into the attributes attached to the primary graph.



PLM in IoT era

Taking simplified PLM as BEFORE –> DURING –> AFTER…

Design of the product should start as early as possible, and it is not isolated, instead foster co-creation and co-invention with your customers. There is no secret number how much of your IP to share publicly, but the criteria is simple – if you share insufficiently, then you will not reach critical mass to trigger consumer interest to it; and if you share too much, your competitors could take it all. The rule of thumb is about technological innovativeness. If you are very innovative, let’s say leader, then you could share less. Examples of technologically innovative businesses are Google, Apple. If you are technologically not so innovative then you might need to share more.

The production or assembly should be as optimal as possible. It’s all about transaction optimization via new ways of doing the same things. Here you could think about Coase Law upside down – outsource to external patterns, don’t try to do everything in-house. Shrink until internal transaction cost equals to external. Specialization of work brings [external] costs down. Your organization structure should reduce while the network of partners should grow. In the modern Internet the cost of external transactions could be significantly lower than the cost of your same internal transactions, while the quality remains high, up to the standards. It’s known phenomenon of outsourcing. Just Coase upside down, as Eric Schmidt mentioned recently.

Think about individual customization. There could be mass customization too, by segments of consumers… but it’s not so exciting as individual. Even if it is such simple selection of the colors for your phones or sneakers or furniture or car trim. It should take place as late as possible, because it’s difficult to forecast far ahead with high confidence. So try to squeeze useful information from your data graphs as closer to the production/assembly/customization moment as possible, to be sure you made as adequate decisions as could be made at that time. Optimize inventory and supply chains to have right parts for customized products.

Then try to keep the customer within experience you created. Customers will return to you to repeat the experience. You should not sit and wait while customer comes back. Instead you need to evolve the experience, think about ecosystem. Invent more, costs may raise, but the price will raise even more, so don’t push onto cost reduction, instead push onto innovativeness towards better personal experiences. We all live within experiences [BTW more and more digitized products, services and experiences]. The more consumer stays within ecosystem, the more she pays. It’s experience economy now, and it’s powered by Internet of Things. May be it will rock… and we will avoid Fight Club.




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A Story behind IoE @ THINGS EXPO

This post is related to the published visuals from my Internet of Everything session at THINGS EXPO in June 2014 in New York City. The story is relevant to the visuals but there is no firm affinity to particular imagery. Now there story is more like a stand alone entity.

How many things are connected now?

Guess how many things (devices & humans) are connected to the Internet? Guess who knows? The one who produces those routers, that moves your IP packets across the global web – Cisco. Just navigate to the link and check the counter in right top corner. The counter doesn’t look beautiful, but it’s live, it works, and I hope will continue to work and report approximate number of the connected things within the Internet. Cisco predicts that by 2020, the Internet of Everything has the potential to connect 50 billion. You could check yourself whether the counter is already tuned to show 50,000,000,000 on 1st of January 2020…

Internet of Everything is next globalization

Old good globalization was already described in The World is Flat. With the rise of smart phones with local sensors (GPS, Bluetooth, Wi-Fi) the flatness of the world has been challenged. Locality unflattened the world. New business models emerged as “a power of local”. The picture got mixed: on one hand we see same burgers,  Coca-Cola and blue jeans everywhere, consumed by many; while on the other hand we already consume services tailored to locality. Even hard goods are tailored to locality, such as cars for Alaska vs. Florida. Furthermore, McDonald’s proposes locally augmented/extended menu, and Coca-Cola wraps the bottles with locally meaningful images.

Location itself is insufficient for the next big shift in biz and lives. A context is a breakthrough personalizer. And that personal experience is achievable via more & smaller electronics, broadband networks without roaming burden, and analytics from Big Data. New globalization is all about personal experience, everywhere for everyone.

Experience economy

Today you have to take your commodities, together with made good, together with services, bring it all onto the stage and stage personal experience for a client. It is called Experience Economy. Nowadays clients/users want experiences. Repeatable experiences like in Starbucks or lobster restaurant or soccer stadium or taxi ride. I already have a post on Transformation of Consumption. Healthcare industry is one of early adopters of the IoT, hence they deserved separate mentioning, there is a post on Next Five Years of Healthcare.

So you have to get prepared for the higher prices… It is a cost of staging of personal experience. Very differentiated offering at premium price. That’s the economical evolution. Just stick to it and think how to fit there with your stuff. Augment business models correspondingly. Allocate hundreds of MB (or soon GB) for user profiles. You will need a lot to store about everybody to be able to personalize.

Remember that it’s not all about consumer. There are many things around consumer. They are part of the context, service, ecosystem. Count on them as well. People use those helper things [machines, software] to improve something in biz process or in life style, either cost or quality or time or emotions. Whatever it is, the interaction between people and between people-machine is crucial for proper abstraction and design for the new economy.

Six Webs by Bill Joy

Creator of Berkeley Unix, creator of vi editor, co-founder of Sun Microsystems, partner at KPCB – Bill Joy – outlined six levels of human-human, human-machine, machine-machine interaction. That was about 20 years ago.

  • Hear – human & intimate device like phone, watch, glasses.
  • Near – human & personal while less intimate device like laptop, car infotainment.
  • Far – human & remote machines like TV panels, projections, kiosks.
  • Weird – human-machine via voice & gesture interfaces.
  • B2B – machine-machine at high level, via apps & services.
  • D2D – machine-machine as device to device, mesh networks.

About 10 years ago Bill Joy reiterated on Six Webs. He pointed to “The Hear Web” as most promising and exciting for innovations.

“The Hear Web” is anatomical graphene

The human body is anatomically the same through the hundreds of years. Hence the ergonomics of wearables and handhelds is predefined. Braceletswristwatchesarmbands are those gadgets that could we wear for now on our arms. The difference is in technology. Earlier it was mechanical, now it is electrical.


We are still not there with human augmentation to speak about underskin chips… but that stuff is being tested already… on dogs & cats. Microchip with information about rabies vaccination is put under the skin. Humans also pioneer some things, but it is still not mainstream to talk much about.

For sure “The Hear Web” was a breakthrough during recent years. The evolution of smartphones was amazing. The emergence of wrist-sized gadgets was pleasant. We are still to get clarity what will happen with glasses. Google experiments a lot, but there is a long way to go until the gadget is polished. That’s why Google experiments with contact lenses. Because GLASS still looks awkward…

The brick design of touch smartphone is not the true final one. I’ve figured out significant issue with iPhone design. LG Flex is experimenting with bendable, but that’s probably not significantly better option. High hopes are on Graphene.    Nokia sold it’s plastic phone business to Microsoft, because Nokia got multi-billion grant to research in graphene wearables. Graphene is good for electricity, highly durable, flexible, transparent. It is much better for the new generation of anatomically friendly wearables.

BTW there will be probably no space for the full-blown HTML5/CSS3/JavaScript browsers on those new gadgets. Hence forget about SaaS and think about S+S. Or client-server which is called client-cloud nowadays. Programming language could be JavaScript, as it works on small hardware already, without fat browsers running spreadsheets. Check out Tessel. The pathway from current medium between gadgets & clouds is: smartphone –> raspberry pi –> arduino.


D2D stands for Device-to-Device. There must be standards. High hopes are on Qualcomm. They are respected chipset & patents maker. They propose AllJoyn – open source approach for connecting things – during recent years. All common functionality such as discovery/onboarding, Wi-Fi comms, data streaming to be standardized and adopted by developers community.

AllSeen Alliance is an organization of supporters of the open source initiative for IoT. It is good to see there names like LG, Sharp, Haier, Panasonic, Technicolor (Thomson) as premier members, Cisco, Symantec and HTC as community members. And really nice to see one of etalons of Wikinomics – Local Motors!

For sure Google would try to push Android onto as many devices as possible, but Google must understand that they are players in plastic gadgets. It’s better to invest money into hw & graphene companies and support the alliance with money and authority. IoT needs standards, especially at D2D/M2M level.

How to design for new webs?

If you know how – then go ahead. Else – design for personal experience. Internet of Everything includes semiconductors, telecoms and analytics from Big Data.



Assuming you are in software business, let semiconductors continue with Moore’s Law, let telecoms continue with Metcalfe’s Law, while concentrate on Big Data to unlock analytics potential, for context handling, for staging personal experience. Just consider that Metcalfe’s Law could be spread onto human users and machines/devices.

Start design of Six Graphs of Big Data from Five Sources of Big Data. The relation between graphs and sources is many-to-many. Blending of the graphs is not trivial. Look into Big Data Graphs Revisited. Conceptualization of the analytics pipeline is available in Advanced Analytics, Part I. Most interesting graphs are Intention & Consumption, because first is a plan, second is a fact. When they begin to match, then your solution begin to rock. Write down and follow it – the data is the next currency. 23andme and Uber logs so much data besides the cap of service you see and consume…

Where we are today?

There are clear five waves of the IoT. Some of those waves overlap. Especially ubiquitous identification of people or things indoors and outdoors. If the objects is big enough to be labeled with RFID tag or visual barcode than it is easy. But small objects are not labeled neither with radio chip nor with optical code. No radio chip because it is not good money-wise. E.g. bottles/cans of beer are not labeled because it’s too expensive per item. The pallets of beer bottles are labeled for sure, while single bottle is not. There is no optical code as well, to not spoil the design/brand of the label. Hence it is a problem to look for alternative identification – optical – via image recognition.

Third wave includes image recognition, which is not new, but it is still tough today. Google has trained Street View brain to recognize house numbers and car plates at such high level of accuracy, that they could crack captcha now. But you are not Google and you will get 75-78% with OpenCV (properly tuned) and 79-80% with deep neural networks (if trained properly). The training set for deep learning is a PITA. You will need to go to each store & kiosk and make pictures of the beer bottles under different light conditions, rotations, distances etc. Some footage could be made in the lab (like Amazon shoots the products from 360) but plenty of work is your own.



Fourth wave is about total digitization of the world, then newer world will work with digital things vial telepresence & teleoperations. Hopefully we will dispense with all those power adapters and wires by that time. “Software is eating the World”. All companies become software companies. Probably you are comfortable with digital music (both consuming and authoring), digital publishing, digital photos and digital movies. But you could have concerns with digital goods, when you pay for the 3D model and print on 3D printer. While atomic structure of the printed goods is different, your concern is right, but as soon as atomic structure is identical [or even better] then old original good has, then your concern is useless. Read more in Transformation of Consumption.

With 3D printing of hard goods it’s less or more understandable. Let’s switch tp 3D printed food. Modern Meadow printed a burger year ago. It costed $300K, approximately as much as Sergei Brin (Googler) invested into Modern Meadow. Surprised? Think about printed newest or personal vaccines and so forth…

Who is IoT? Who isn’t?

Is Uber IoT or not? With human drivers it is not. When human-driven cabs are substituted by self-driving cabs, then Uber will become an IoT. There is excellent post by Tim O’Reilly about Internet of Things & Humans. CEO of Levie tweeted “Uber is a $3.5 billion lesson in building for how the world *should* work instead of optimizing for how the world *does* work.” IoT is not just more data [though RedHat said it is], IoT is how this world should work.

How to become IoT?

  • Yesterday it was from sensors + networks + actuators.
  • Today it becomes sensors + networks + actuators + cloud + local + UX.
  • Tomorrow it should be sensors + networks + actuators + cloud + local + social + interest + intention + consumption + experience + intelligence.

Next 3000 days of the Web

It was vision for 5000 days, but today only 3000 days left. Check it out.

Next 6000 days of the Web

Check out There will be no End of the World. We will build so big and smart web, that we as humans will prepare the world to the phase shift. Our minds are limited. Our bodies are weird. They survive in very narrow temperature range. They afraid of radiation, gravity. We will not be able to go into deep space, to continue reverse engineering of this World. But we are capable to create the foundation for smarter intelligence, who could get there and figure it out. Probably we would even don’t grasp what it was… But today IoT pathway brings better experiences, more value, more money and more emotions.


Let’s check Cisco internet of things counter. ~300,000 new things have connected to the Internet while I wrote this story.

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On the Internet of Everything

Five Waves of the “Internet of Things” on its Way of Transforming into “Internet of Everything”

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Mobile Programming is Commodity

This post is about why the programming for smart phones and tablets is commodity.

Mobiles are no more novelty.

Mobiles are substituting PCs. As we programmed in VB and Delphi 15 years ago, the same way we will program in Objective-C and Java today.  Because adoption rate for cell phone as technology (in USA) is fastest from other technologies, and the scale of adoption surpassed 80% in 2005. Smart phones are being adopted at same pace, surpassing 35% in 2011, just in several years since iPhone revolution happened in 2007. Go check out the evidence from New York Times since 2008 for cell phones , evidence from Technology Review since 2010 for smart phones , more details by Harvard Business Review on accelerated technology adoption.

Visionaries look further. O’Reilly.

The list of hottest conferences by direction from visionary O’Reilly:

  • BigData
  • New Web
  • SW+HW
  • DevOps

BigData still matters, matching approach to Gartner’s “peak of inflated expectations”. Strata, Strata Rx (Healthcare flavor), Strata Hadoop. Tap into the collective intelligence of the leading minds in data—decision makers using the power of big data to drive business strategy, and practitioners who collect, analyze, and manipulate data. Strata gives you the skills, tools, and technologies you need to make data work today—and the insights and visionary thinking O’Reilly is known for.

JavaScript got out of the web browser and penetrated all domains of programming. Expectations and progress for HTML5 .Web 2.0 abandoned, fluent created. Emerging technologies for new Web Platform and new SaaS. O’Reilly’s Fluent Conference was created to give developers working with JavaScript a place to gather and learn from each other. As JavaScript has become a significant tool for all kinds of development, there’s a lot of new information to wrap your head around. And the best way to learn is to spend time with people who are actually working with JavaScript and related technologies, inventing ways to apply its power, scalability, and platform independence to new products and services.

“The barriers between software and physical worlds are falling”. “Hardware startups are looking like the software startups of the previous digital age”. Internet of Things has longer cycle (according to Gartner’s hype cycle), but it is coming indeed. With connected machines, machine-to-machine, smart machines, embedded programming, 3D printing and DIY to assemble them (machines). Solid. The programmable world is creating disruptive innovation as profound as the Internet itself. As barriers blur between software and the manufacture of physical things, industries and individuals are scrambling to turn this disruption into opportunity.

DevOps & Performance is popular. Velocity. Most companies with outward-facing dynamic websites face the same challenges: pages must load quickly, infrastructure must scale efficiently, and sites and services must be reliable, without burning out the team or breaking the budget. Velocity is the best place on the planet for web ops and performance professionals like you to learn from your peers, exchange ideas with experts, and share best practices and lessons

Open Source matters more and more. Open Source is about sharing partial IP for free according to WikinomicsOSCON. OSCON is where all of the pieces come together: developers, innovators, business people, and investors. In the early days, this trailblazing O’Reilly event was focused on changing mainstream business thinking and practices; today OSCON is about how the close partnership between business and the open source community is building the future. That future is everywhere you look.

Digitization of conent continues. TOC.

Innovation in leadership and processes. cultivate.

Visionaries look further. GigaOM.

The list of conferences by direction from GigaOM:

  • BigData
  • UX
  • IoT
  • Cloud

BigData. STRUCTURE DATA. From smarter cars to savvier healthcare, today’s data strategies are driving business in compelling new directions.

User Experience. ROADMAP. As data and connectivity shape our world, experience design is now as important as the technology itself. It covers (and will cover) ubiquitous UI, wearables and HCI with all those new smarter machines (3D printed & DIY & embedded programming).

Internet of Things. MOBILIZE. Five years ago, Mobilize was the first conference of its kind to outline the future of mobility after Apple’s iPhone exploded onto the scene. We continue to track the hottest early adopters, the bold visionaries and those about to disrupt the ecosystem. We hope that you will join us at Mobilize and be the first in line to ride this next wave of innovation. This year we’ll cover: The internet of things and industrial internet; Mobile big data and new product alchemy; Wearable devices; BYOD and mobile security.

Cloud. STRUCTURE. Structure 2013 focused on how real-time business needs are shaping IT architectures, hyper-distributed infrastructure and creating a cloud that will look completely different from everything that’s come before. Questions we answered at Structure 2013 included: Which architects are choosing open source solutions, and what are the advantages? Will to-the-minute cloud availability be an advantage for Azure? What are the lessons learned in building a customized enterprise PaaS? Where is there still space to innovate for next-generation leaders?


To be strong programmer for today you have to be able to design and code for smart phones and tablets as your father and mother did 20 years ago for PC and workstations. Mobile programming is shaped by the trends, described in Mobile Trends for 2014.

To be strong programmer for tomorrow you have to tame the philosophy, technologies and tools of BigData (despite Gartners prediction of inflated expectations), Cloud,  Embedded and Internet of Things. It is much less Objective-C but probably still plenty of Java. Seems like the future is better suited for Android developers. IoT is positioned last in the list because its adoption rate is significantly lower than for cell phones (after 2000 dotcom burst).

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Five Sources of Big Data

Some time ago I’ve described how to think when you build solutions from Big Data in the post Six Graphs of Big Data. Today I am going to look in the opposite direction, where Big Data come from? I see distinctive five sources of the data: Transactional, Crowdsourced, Social, Search and Machine. All details are below.

Transactional Data

This is old good data, most familiar and usual for the geeks and managers. It’s plenty of RBDMSes, running or archived, on premise and in the cloud. Majority of transactional data belong to corporations, because the data was authored/created mainly by businesses. It was a golden era of Oracle and SQL Server (and some others). At some point the RDBMS technology appeared to be incapable of handling more transactional data, thus we got Teradata (and others) to fix the problem. But there was no significant shift for the way we work with those data sources. Data warehouses and analytic cubes are trending, but they were used for years already. Financial systems/modules of the enterprise architectures will continue to rely on transactional data solutions from Oracle or IBM.

Crowdsourced Data

This data source has emerged from the activity rather than from type of technology. The phenomenon of Wikipedia confirmed that crowdsourcing really works. Much time passed since Wikipedia adoption by the masses… We got other fine data sources built by the crowds, for example Open Street Maps, Flickr, Picasa, Instagram.

Interesting things happen with the rise of personal genetic testing (verifying DNA for million of known markers via 23andme). This leads to public crowdsourced databases. More samples available, e.g. amateur astronomy. Volunteers do author useful data. The size of crowdsourced data is increasing.

What differentiates it from transactional/enterprise data? It’s a price. Usually crowdsourced data is free for use, with one of creative commons licenses. Often, the motivation for creation of such data set is digitization of our world or making free alternative to paid content. With the rise of nanofactories, we will see the growth of 3D models of every physical product. By using crowdsourced models we will print the goods at home (or elsewhere).

Social Data

With the rise of Friendster–>MySpace–>Facebook and then others (Linkedin, Twitter etc.) we got new type of data — Social. It should not be mixed for Crowdsourced data, because of completely different nature of it. The social data is a digitization of ourselves as persons and our behavior. Social data is very well complementing the Crowdsourced data. Eventually there will be digital representation of everyone… So far social profiles are good enough for meaningful use. Social data is dynamic, it is possible to analyze it in real-time. E.g. put Tweets or Facebook posts thru the Google Predictive API to grab emotions. I’m sure everybody intuitively understands this type of data source.

Search Data

This is my favourite. Not obvious for many of you, while really strong data source. Just recall how much do you search on Amazon or eBay? How do you search on Wikis (not messing up with Wikipedia). Quora gets plenty of search requests. StackOverflow is a good source of search data within Information Technology. There are intranet searches within Confluence and SharePoint. If those search logs are analyzed properly, then it is clear about potential usefulness and business application. E.g. Intention Graph and Interest Graph are related to the search data.

There is a problem of “walled gardens” for search data… This problem is big, bigger than for social data, because public profiles are fully or partially available, while searches are kept behind the walls.

Machine Data

This is also my favourite. In the Internet of Things every physical thing will be connected. New things are designed to be connectable. Old things are got connected via M2M. Consumers adopted wearable technology. I’ve posted about it earlier. Go to Wearable Technology and Wearable Technology, Part II.

The cost of data gathering is decreasing. The cost of wireless data transfer is decreasing. The bandwidth of wireless transfer is increasing dramatically. Fraunhofer and KIT completed 100Gbps transmission. It’s fourteen times faster than the most robust 802.11ac. The moral is — measure everything, just gather data until it become Big Data, then analyze it properly and operate proactively. Machine data is probably the most important data source for Big Data during next years. We will digitize the world and ourselves via devices. Open Street Map got competitors, the fleet of eBees described Matterhorn with million of spatial points. More to expect from machines.

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Wearable Technology. Part II

This story is a logical continuation of the previously published Wearable Technology.

Calories and Workouts

Here I will show how two different wearable gadgets complement each other for Quantified Self.  For the beginning we need two devices, one is wearable on yourself, second is wearable by your bike.

First device is called BodyMedia, world’s most precise calories meter. It measures 5,000 data snapshots per minute from galvanic skin response, heat flux, skin temperature and 3-axis accelerometer. You can read more about BodyMedia’s sensors online. BodyMedia uses extensive machine learning to classify your activity as cycling, then measuring calories burned according to the cycling Big Data set used during learning. Check out this paper: Machine Learning and Sensor Fusion for Estimating Continuous Energy Expenditure for excellent description how AI works.

Second device is called Garmin Edge 500, simple and convenient bike computer. It has GPS, barometric altimeter, thermometer, motion detection and more features for workouts. You can read more about Garmin Edge 500 spec online. My gadgets are pictured herein.


On the Route

The route was proposed by Mykola Hlibovych, a distinguished bike addict. So I put my gadgets on and measured it all. Below is info about the route. Summary info such as distance, time, speed, pace, temperature, elevation is provided by Garmin. it tries to guess about the calories too, but it is really poor at that. You should know there is no “silver bullet” and understand what to use for what. Garmin is one of the best GPS trackers, hence don’t try to measure calories with it.

Juxtaposition of elevation vs. speed and temperature vs. elevation is interesting for comparison. Both charts are provided by distance (rather than time). 2D route on the map is pretty standard thing. Garmin uses Bing Maps.


Burning Calories

Let’s look at BodyMedia and redraw Garmin charts of speed, elevation and temperature along the time (instead of distance) and stack them together for comparison/analysis. All three charts are aligned along the horizontal time line. Upper chart is real-time calories burn, measured also in METS. The vertical axis reflects Calories per Minute. Several times I burned at the rate of 11 cal/min with was really hot. The big downtime between 1PM and 2:30PM was a lunch.

An interesting fact is observable on Temperature chart – the Garmin was warm itself and was cooling down to the ambient temperature. After that it starter to record the temperature correctly. Another moment is a small spike in speed during downtime window. It was Zhenia Novytskyy trying my bike to compare with his.


Thorough Analysis

For detailed analysis of the performance on the route there is animated playback. It is published on Garmin Cloud. You just need to have Flash Player. Click this link if WordPress does not render the embedded route player from Garmin Cloud. There is iframe instruction below. You may experience some ads from them I think (because the service is free) …

The Mud

Wearable technology works in different conditions:)





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Native Apps Beat Mobile Web

Mobile Web was a good thing on feature phones, but nowadays and in the near future Native Apps rock! Interested why? Stay with me.

No More the Phone

iPhone and Android are no more the phones, they are new personal computers, just smaller than PC 20 years ago. The irony is the recent issue with iPhone 4 antenna. When you close the metal circuit with your finger during the phone talk, then antenna could stop working properly, as result you are not able to to phone conversation – the phone does not work as the phone. It is ridiculous, but it clearly represents the trend: those devices are much more than phones, the “phone function” is not even #1 (as with iPhone 4 antenna). But besides “phone” those devices can many other things, such as taking pictures, play music, play videos, and run applications. Those apps seem more important than phone talks. It was an introduction, hope you got the point that devices are evolving towards non-phone ones.

Can Apps be SaaS?

Ordinary apps can be SaaS, best apps can’t be SaaS. Big difference between best apps and the rest is the efficient usage of the device hardware, its sensors. It is possible (historically) from OS API. There were always “silver bullets” that allowed “write once, run on many”, but all of them confirmed to be limited and restricted in comparison to the native apps. There is only one way to use new iPhone most efficiently, it’s via iOS programming, with direct access to entire API and underlying sensors. It is same as it was on desktop PC programming. You could use some library (e.g. from Adobe or Trolltech) and succeed with certain things, but get restricted or non-flexible with others. Situation with multiple platforms led to even more limitations. Ok, but what is that special thing that we want to have access to and control it with great flexibility? Sensors! New sensors. It’s obvious now that HTML-powered SaaS can’t leverage the power of new sensors, because it doesn’t know about them! Hybrids go with smaller handicap than pure HTML, but they are behind the schedule. Best apps are native apps and they make the pace.

New Sensors

You probably heard about Apple’s patent to infrared camera that could be used for DRM or substitute QR codes. Figure below is put to remind about it.

Infrared sensor is a beginning. Motorola Xoom has introduced barometer. Now it is available on Galaxy Nexus too. Barometer is relatively simple sensor, hence its support by HTML and different mobile middleware is not a problem, it’s just come with some delay in comparison to OS API. Another potential sensor could be humidity. Altimeter could come to iPhone soon too. All them will be supported by HTML with some delay to native support… But more advanced things like motion sensors and 3D GUI are obviously too tricky for HTML (see figure below). It is kind of Kinect embedded into iPhone. There are Apple patents for  hover sensing, more details on 3D hovering here.

Another sample is about old good WiFi, measurement of WiFi signal strength is a problem for non-native apps. If smbd wonder why we may need such WiFi strenght measurement, then the answer is – for in-door positioning, where GPS doesn’t work. Going into advanced requires working directly at the OS level. Native apps! But now I’d like to switch to Healthcare and sensors that could be useful there. You will get even more arguments for native apps.

Health Sensors

Thermometer could be embedded into the phone, We saw simple electronic thermometer is the stores. Infrared touchless thermometer might be bigger, though as a sensor both are simple. What about UV exposure sensor? You expose your phone to the sun and it (phone app) tells you about UV level and suggests based on the result. UV sensor could be more sophisticated than thermometer, probably some calibrations will be required. This is argument that its use could be efficient from OS level. Next similar to UV could be radiation sensor. The questions is about miniaturisation, when it fits into the phone body. Non-trivial programming expected, hence no chance for HTML or hybrid at the beginning.

Sounds like Sci-Fi? Well, switching to human. Heart monitoring is pretty straightforward, that sensor could be quickly available for HTML and JavaScript. What about breath analyzer? There could be a sensor for alcohol in the phone. Similar sensor could be a smoke analyzer at home, embedded into the phone. What about perspiration sensor and analyzer? It is easy and safe to use outside of hospital. Perspiration sensor sounds advanced. Glucose measurement sensor could become small enough to be fit into the phone. It is possible to implement mood sensor, that measure excitement or frustration level, and it could fit into device you continue to call “the phone”.


Hardware is booming right now. It is obvious. Plenty of manufacturing is now takes place in the states, not in Asia. History taught us during PC era: there are no silver bullets and you need to be close to the hardware as possible if you need all its features and use them in flexible and reliable way. Are hybrids the solution, to wrap all new sensors and allow SaaS within? I doubt, because those man-in-the-middle always introduce limitations in comparison to what you have by using OS’es API directly. What to build best apps? Build native apps and you will not regret. For next several years guaranteed. Mobile web will catch the pace, when all sensors are designed, adopted, HTML standardised and so on. It will take several years.

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Web 3.0

What is a future of the Web?

Is it Semantic Web as long time ago smtb called it? Spend few minutes to read so diverse definitions of Web 3.0 on wiki and return back here. Nobody argues with all those predictions, all of them will happen at some point in the future. My favorite prediction is smth like Kevin Kelly made public at the end of 2007, called “Next 5000 Days of the Web”

All those devices and sensors that will suck data into the web are related to our mobile devices. From the Mobile World Congress 2012 I brought information, announced by Eric Schmidt, that soon we will have 50,000,000,000 connected devices. Only imagine that number, almost ten devices per person. It is really huge!

But what we have today?

Today we see the boom of mobile apps. It is similar to what we have with the boom of apps for PCs 20-25 years ago. The history repeats itself, slightly at different level. Now we have app boom for smaller devices than PC. Years ago we have premium vendor of the app platform – Apple, and commoditizer – Microsoft. Today we have the same, premium vendor – Apple, and new commoditizer – Google/Android. But the big picture is similar, the apps are booming, there is brand new community of developers and users of them. There are new business models emerging how to monetize on this new boom.

How is it related to the Web at all? The web is in place, it is inevitable and we are all in the web, but there are nuances;) Surfing the web with Mobile Web is not the same as using the Native App. For business applications Mobile Web is logical choice, it smoothly substitutes awkward MEAP solutions. It is not a surprise that Gartner did not identify any MEAP vendors as Leaders in is Magic Quadrant. There are niche players, visionaries, but there are no leaders. It was not easy, hence many walls were broken by Mobile Web. Enterprise love Mobile Web, it has emerged and gaining popularity. Is it Web 3.0? What is a difference between web app for desktop, tablet, phone? There is almost no difference. Just few additional features like geolocation available from the browser, camera and so on. But delivery model is the same, SaaS-like familiar from PC times. Hence it is not a revolution to be named Web 3.0.

Revolution happened.

Revolution seems to be this application boom on modern phones and tablets. It smells like revolution. This observable on apps like Believe me or not, but Instagram was a threat to Facebook! Initially people published photos on Flickr or Picasa and sent link to the friends and colleagues to share them. With Facebook photo sharing feature, it got simplified, you just upload photos and there got shared automatically within your network. No need in Flickr or Picasa anymore? Then came Instagram, with opportunity to make pictures with the phone, apply some cool effect and instantly share, without connecting the device to the PC and without that annoying bulk upload. Instagram has a backend, synthesized from Facebook and Twitter, which is cool for the user. You don’t need Facebook anymore to share your pictures! Bingo!

Ok, Instagram is cool, Facebook even bought it to kill it as a competitor… But were is the web there? It is called Web Services. There is very rich and powerful web, full of clouds and web services. As Jeff Bezos once said, the future of the web was in Amazon Web Services. It is. We have got very popular S+S model, with native app on the phone/tablet and back end on AWS or so. There is good report by Vision Mobile that “Apps is a New Web“, dated 2010. We have got new ways of discovery of useful things, brand new UX, new monetization models. Enough arguments to call it New Web. May be not Web 3.0, but definitely it is no more Web 2.0.

To HTML5 Believers.

Those who hope on HTML5 as a standard, and return to old good SaaS approach could be pleased that for enterprises this works even today and will work tomorrow. But for the non-enterprise users it is not a case. First of all, all standards need few years (up to 5) to mature, after that the wide adoption happens. Second, hardware will evolve too. Web technologies will not keep the pace of hw evolution. Have you ever heard about new sensors planned for the new iPhone? E.g. infrared camera patent filled by Apple recently. It will serve for DRM, like preventing from recording the live show. It will serve to identify objects by infrared tags, instead of ugly QR tags. Infrared are invisible to the people, which means they are better, because they do not spoil the look of the object. OK, back to the infrared sensor – do you thing web tools like HTML will have support for infrared camera tomorrow? I think no. I even bet it will not. The pace of hardware is fast and web technologies will be few steps behind.


We have entered Web 3.0

New sensors like infrared camera will be added to the phones, tablets in the future. Other devices will emerge in the future. Recall 50,000,000,000 connected devices. There is no easy way to apply SaaS to all of them. There is strong M2M trend, observed during recent years. It is not Web 2.0 anymore. We have started from the user apps, now we are descending to the machine apps too… It is really smth brand new. I propose to call this new era Web 3.0. For semantic web we could chose another name, when it come. So far we are within smth new, and instead of calling it New Web, let’s call it Web 3.0.

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