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. http://video.mit.edu/embed/9110/

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 http://radar.oreilly.com/2014/04/ioth-the-internet-of-things-and-humans.html

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…

BEFORE.
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.

DURING.
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.

AFTER.
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.

 

PS.

Visuals…

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 http://newsroom.cisco.com/ioe 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. Bracelets, wristwatches, armbands 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

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 Box.com 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.

PS.

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

Internet of Everything @ THINGS EXPO in NYC

Internet of Everything from aojajena

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. http://strataconf.com/strata2014 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. http://fluentconf.com/fluent2014 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. http://solidcon.com/solid2014 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
learned.

Open Source matters more and more. Open Source is about sharing partial IP for free according to Wikinomics. OSCON. http://www.oscon.com/oscon2014 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. http://events.gigaom.com/structuredata-2014/ From smarter cars to savvier healthcare, today’s data strategies are driving business in compelling new directions.

User Experience. ROADMAP. http://events.gigaom.com/roadmap-2013/ 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. http://event.gigaom.com/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. http://event.gigaom.com/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?

Conclusion.

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).

Transformation of Consumption

Some time ago I’ve posted on Six Graphs of Big Data and mentioned Consumption Graph there. Then I presented Five Sources of Big Data on the data-aware conference, mentioned how retailers track people (time, movement, sex, age, goods etc.) and felt the keen interest from the audience about Consumption Data Source. Since that time I’ve thought a lot about consumption ‘as is’. Recently I’ve paid attention to the glimpses of the impact onto old model from micro-entrepreneurs, who 3D-prints at home and sell on Etsy. Today I want to reveal more about all that as consumption and its transformation. It will be much less about Big Data but much more about mid term future of Economics.

The Experience Economy

It was mentioned 15 years ago. The experience economy was identified as the next economy following the agrarian economy, the industrial economy, and the most recent service economy. Here is a link to 1998 Harvard Business Review, “Welcome to the Experience Economy”. Guys did an excellent job by predicting the progression of economic value. The experience is a real thing, like hard goods. Recall your feelings when you are back to the favorite restaurant where you order without looking into the menu. You got there to repeat the experience. Hence modern consumption is staged as experience, from the services and goods. Personal experience is even better. Services and goods without staging are getting weaker… Below is a diagram of the progression of economic value.

It would be useful to compare the transformation by multiple parameters such as model, function, offering, supply, seller, buyer and demand. The credit goes to HBR. I have improved the readability of the table in comparison to their. There is a clear trend towards experience and personalization. Pay attention to the rightmost column, because it will be addressed in more details later in this post.  To make it more familiar and friendly for you, I’ll appeal to your memories again: recall your visits to Starbucks or McDonalds. What is a driving force behind your will? How have you gained that internal feeling over past periods? Multiple other samples are available, especially from the leisure and hospitality industry. Pioneers of new economics are there already, others are joining the league. And yes… people are moving towards those fat guys from WALL-E movie…

The Invisible Economy

Staging experience is not enough. Starbucks provides multiple coffee blends, Apple provides multiple gadgets and even colors. But it is not enough. I am an example. I need curved phone (suitable for my butt shape, because I keep it in the back pocket). Furthermore, I need a bendable phone, friendly for sitting whet it’s in the pocket. While majority of manufacturers-providers are ignoring it, LG is planning something. Let’s see what it will be, there is evidence of curved and flexible one. But I am not alone with my personal [strange?] wills. Others are dreaming of other things. Big guys may not be nimble enough to catch the pace of transforming and accelerated demand. It’s cool to be able to select colors for New Balance 993 or 574, but it’s not enough. My foot is different that yours, I need more exclusivity (towards usability and sustainability) than just colors. Why not to use some kind of digitizer to scan my foot and then deliver my personal shoes?

“The holy place is never empty” is my free word translation of Ukrainian proverb. It means that opportunity overlooked by current guys is fulfilled by others, new comers. There is a rising army of craftsmen and artists producing at home (manually of on 3D printers) and selling on Etsy. Fast Company has a great insight on that: “… Micro-entrepreneurs are doing something so nontraditional we don’t even know how to measure it…” There are bigger communities, like Ponoko. It is new breed of doers, called fabricators. And Ponoko is a new breed of the environment, where they meet, design, make, sell, buy and interact. The conclusion here is straightforward – our demand is fulfilled by new guys and in different way we used to. You can preview 3D model or simulation being thousand miles away and your thing will be delivered to your door. You can design your own thing. They can design for you and so on. And this economy is growing. Hey, big guys, it’s a threat for you!

The most existing in economy transformation is a foreseen death of banks. Sometimes banks are good, but in majority of modern cases they are bad. We don’t need Wells Fargo and similar dinosaurs. Amazon, Google, Apple, PayPal could perform the same functions more efficiently and make less evil to the people. There are emerging alternatives [to banks] how to fund initiatives, exchange funds between each other. Kickstarter and JumpStartFund are on the rise. Even for very serious projects like Hyperloop. Those things are still small (that’s why the section is called Invisible), but they are gaining the momentum and will hit the overall economy quite soon and heavy, less than in five years.

3D Printing

Here we are, taking digital goods and printing them into hard goods. Still early stage, but very promising and accelerating. MakerBot Replicator costs $2,199 which is affordable for personal use. There is a model priced at $2,799, which is still qualified for personal use. What does it mean for consumption? The World is being digitized. We are creating a digital copy of our world, everything is digitized and virtualized. Then digital can be implemented in the physical (hard good) on 3D printer. There are very serious 3D printers by Solid Concepts, that are capable to print the metal gun, which survives 500 round torture test. As soon as internal structure at molecular level is recreated and we achieve identical material characteristics, the question left is about cost reduction for the technology. As soon as 3D printing is cheap, we are there, in new exciting economy.

Let’s review other, more useful application of technology than guns. We eat to live, entertain to live good, and we cure diseases (which sometimes happen because of lifestyle and food). So, food first. 3D printed meat is already a reality. Meat is printed on bioprinter. Guess who funded the research? Sergey Brin, the googler. Modern Meadow creates leather and meat without slaughtering the animals. Next is health. The problem of waiting lists for organ exchange is ending. Your organs will be 3D printed. It is better than transplant because of no immune risks anymore. And finally, drugs. Recall pandemic situations with flue. Why you have to wait for vaccine for a week? You can 3D print your drugs from the digital model instantly, as soon as you download the digital model over the Internet. Downloaded and printed drugs is additional argument for Personalized Medicine in my recent post on the Next Five Years of Healthcare. I assume that answering essential application of technology to the basic aspects of life such as food, lifestyle and healthcare is sufficient to start taking it [technology] seriously. You can guess for other less life-critical applications yourself.

4D Printing

3D printing is on the rise, but there is even more powerful technology, called 4D printing. Fourth dimension is delayed in time and is related to the common environment characteristics such as temperature, water or some more specific like chemical. When external impact is applied, the 3D-printed strand folds into new structure, hence it uses its 4th dimension. It is very similar to the protein folding. There are tools for design of 4D things. One of them is cadnano for three-dimensional DNA origami nanostructures. It gives certainty of the stability of the designed structures. Another tool is Cyborg by Autodesk. It’s set of tools for modeling, simulation and multi-objective design optimization. Cyborg allows creation of specialized design platforms specific for the domains, from nanoparticle design to tissue engineering, to self-assembling human-scale manufacturing. Check out this excellent introduction into self-assembly and 4D printing by Skylar Tibbits from MIT Media Lab:

Forecast [on Consumption]

We will complete digitization of everything. This should be obvious for you at this stage. If not, then check out slightly different view on what Kevin Kelly called The One. No bits will live outside of the one distributed self-healing digital environment. Actually it will be us, digital copy of us. Data-wise it will be All Data together. Second reference will be to James Burke, who predicted the rise of PCs, in-vitro fertilization and cheap air travel in far 1973. Recently Burke admitted: “…The hardest factor to incorporate into my prediction, however, is that the future is no longer what it has always been: more of the same, but faster. This time: faster, yes, but unrecognisably different…” And I see it in same way, we are facing different future than we used to. It’s a bit scary but on the other hand it is very exciting. In 30 years we will have nano-fabricators, which manipulate at the level of atoms and molecules, to produce anything you want, from dirt, air, water and cheap carbon-rich acetylene gas. As you may already feel, those ingredients are virtually free, hence production of the goods by fabricator is almost free. Probably food will be a bit more expensive, but also cheap. By the way, each fabber will be able to copy itself… from the same cheap ingredients. We will not need plenty of wood, coal, oil, gas for nanofabrication. This is good for ecology. But I think we will invent other ways how to spoil Earth.

The value will shift from equipment to the digital models of the goods. Advanced 3D (and 4D models) will be not free; the rest will be crowdsourced and available for free. Autodesk, not a new company, but one of those serious, is a pioneer there with 123D apps platform. They are moving together with MakerBot. You can buy MakerBot Replicator on Autodesk site and vice versa, you will get Autodesk software together with MakerBot you bought elsewhere. It’s how it all is starting. In few years it will take off at large scale. Then we will get different economy, with much personal, sustainable and sensational consumption.

It would be interesting to draw parallels with the creation of Artificial Intelligence, because in 2030 we should have human brain simulated on non-biological carrier. Or may be we will be able to 4D or 5D-print more powerful brains than human on biological, but non-human carrier? Stay tuned.

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 the 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. The 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 a 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, OpenStreetMaps, Flickr, Picasa, Instagram.

Interesting things happen with the rise of personal genetic testing (verifying DNA for millions 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 the 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 the creation of such data set is the digitization of our world or making a 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 a new type of data — Social. It should not be mixed for Crowdsourced data, because of completely different nature of it. The social data is the 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 favorite. 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 with “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 favorite. 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 becomes Big Data, then analyze it properly and operate proactively. Machine data is probably the most important data source for Big Data during the 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.

How to Crowdsomething or Crowdeverything

Crowdsourcing is an interesting phenomena that works, but it is not obvious how does it work in details. This post is about crowdsourcing, when it works and when it doesn’t.

The Evidence: Crowdsourcing Works

We all benefited from successful crowdsourcing already, I’m sure WordPress backend is running on Linux servers; Linux is probably the most iconic product of the crowd. Linux allowed to cut hosting costs tremendously. Without Linux we would not have the Cloud. Free BSD was also suitable, but reality is that Cloud was enabled by Linux and LAMP, when you pay for the hardware and don’t pay for the software. Scaling became pretty cheap. Amazon, Google, dotCloud, Rackspace – all of them – have to say ‘thank you, the crowd’ – for building Linux. MS has to eat own dog food, run Azure on Windows without selling Windows licenses anymore… Otherwise they can’t compete with Linux-powered clouds.

The Evidence: Crowdsourcing Doesn’t Work

Try or recall of the usage of OpenOffice or other crowdsourced (and open sourced) apps and we will be on the same page. Many crowdsources apps suck. There is no clue how to polish them and bring to the better quality.

Why Linux rocks then? Because it is not a crowdsourcing. It is open source but it is controlled design and development. There is huge authority known as Linus Torvalds, who is a driver, designer, manager, inspirator, cop and God. Without such person there would be no Linux. Hence we see the dialectic shift from crowdsourcing to semi-crowdsourcing. As usually, the truth is somewhere in the middle, between single person design and total swarming by the crowd.

The Evidence: Crowdsourcing Works via Funding

Let’s take another product – already mentioned OpenOffice. Oracle has supported it. Financially. Without such support the quality would be even worse. Let’s take Redis. VMware (EMC2) supports it. Without VMware support Salvatore Sanfilippo would not probably continue it. VMware put him on staff and pays for continuation of the work on Redis. Let’s take Hadoop. There is a dedicated team at Yahoo! working on Hadoop, starting from Doug Cutting and his guys. Now Hadoop is maintained by Yahoo, Cloudera and others. BTW Doug Cutting works at Cloudera and he works on Hadoop.

The Shift: Open Source is No More the Crowdsourcing

Shift happened. Open source is not a crowdsourcing. Literally all modern companies produce open source which is not a crowdsource. Samples are Thrift and Cassandra from Facebook, Protocol Buffers from Google, Voldemort by LinkedIn, set of Scala-related projects by Twitter.

The open source is a channel of communication. Sharing of information is very important nowadays. Companies that share get indirect pay off. It is about new rules and relations in business, gamified business. It is good to see that outsourcing companies do the same! CloudMade shared Leaflet library for mobile mapping, the library is used by Flickr (Yahoo!) for World map and by dozen of other big names. Obviously the pay off is an advertisement and better karma for the CloudMade. ELEKS shared distributed storage for events called EventStore. It is a trend already and other should follow. Open source but not a crowdsource.

Where Funding Comes From?

At this point the reader should be comfortable with understanding that somebody has to pay so that the crowd produce something useful. Payments can go directly to the contributors, or indirectly. Sample of direct payments are maintenance and evolution of Hadoop or Redis. Sample of indirect payments is Wikipedia. Many people and organizations pay, but money does not go to those who write the pages.

This phenomena when multiple people (and organizations) pay is called crowdfunding. It is natural intersection between demand and supply to achieve something or create something. People from the village helped their representative to win some sport competition. People from same tribe gather money to solve somebodies problem. People from same religion gather money to do worship and rituals.

There is virtually unlimited number of people organization, virtual networks, by interests, by need, by whatever, for short, mid and long terms. But the fact is that people do collaborate and they are capable to fund the mutual goal. Everybody gives depending on capabilities (donations) or interest (share in future product). Wikipedia is a donation-based venture. Kickstarter and Quirky are interest-based ventures. Crowdfunding is what the crowdsourcing needs to work.

Big Commercial Funding vs. Crowdfunding

Big companies have adopted the variation of crowdsourcing known as crowddesign. BMW designs the cars by the crowd for years. BMW has success. Mitsubishi decided to follow the BMW, with Lancer Evolution X, and failed. Something went wrong, but what? The wisdom is hidden in pharmaceutical companies. They use crowddesign and crowdresearch for years and very successfully, e.g. InnoCentive company. The recipe is to keep core intellectual property behind the wall, while outsource some IP to the crowd. Gold mining company Goldcorp did an ultimate sharing of IP to the crowd and still succeeded. But more reliable (from business perspective) formula is to balance how much to share and how much to keep inside the company. Fundind (expenses) are partially on the interested company and partially on the crowd. Important aspect is a prize at the end. The crowdresearch, crowddesign is organized as a challenge, hence participants are motivated to win a grand prize at the end. Governments follow the same recipe. Right now we are participating in White House design challenge for better EMR, THE PATIENT RECORD. Great samples how it works could be found in Wikinomics and Macrowikinomics books.

On the other hand, small projects could be funded by the crowd. What if you want to make a movie for the Sundance festival? It is possible to gather money on Kickstarter. Here is evidence of 14 selected movies that were crowdfunded by Kickstarter. Quirky works differently, but it’s model also cool. Quirky triggers crowddesign and promise rewards for the winners and finalists. Anyhow the system is looped, there must be a feedback loop between those who produce and those who get paid.

Enterprise 2.0+

What if all that stuff is running within your enterprise? Crowdresearch is a equivalent to Ideation. There could be same model with grand prize for the winner and bonuses for the finalists. It is called process gamification. The crowd has the power, it is underused in enterprises. There are still more orders than information sharing and swarming. Prototyping of new ideas could be done via crowdsourcing. Interested departments or managers could fund together based on donation or interest schemas, i.e. perform crowdfunding.

We can use same tools as used outside of the walled gardens of the enterprises to improve our enterprises. People can start something on their own, try to attract the others, build virtual teams and produce some end results. Based on the results, further plans are decided, either to fund, or crowdfund, or management-fund. Management can organize challenges for corporate initiatives and fund them partially, while rely on enthusiasm and gamified process for the crowd.

We have revealed the powerful modern mechanism for the businesses and the employees. Let’s establish it for the better operations and business results. At the end it is all about money, hence true way is to use this at your enterprise! This goes further than what is called Enterprise 2.0, it is more advanced than SLATES. It is much more about microeconomics rather than using some tools behind the firewalls.