Mobility and Net Neutrality

Driving by car is strongly connected with a feeling of personal freedom. While we book flights just for one specific itinerary, and train tickets are usually only valid for a short period in time, we can get into our car whenever we want and drive any route that comes to our mind. Traffic jams, detours, or temporary road blocks apply to every driver the same way. And also speed limits or priority are not depending on how much we pay. This also holds true for toll that might be charged to cross a bridge or a tunnel – every driver is treated the same way.

However can we take this condition for granted that provides a network of roads in a neutral way to every user? Net neutrality is not a matter of fact in every industry, not even in all branches of mobility and logistics. Rail services e.g. charge special rates for express trains. While the tariff structure of rail companies are rather transparent in passenger services, this is not the case for transportation of goods. Depending on the buying power and on the negotiations of the customers‘ procurement, freight will be transported timely or might travel rather slowly to its destination.

In telecommunications, the rate structure is even more notorious for its lack of transparency. Voice and data plans vary by orders of magnitude regarding bandwidth or duration that comes with different plans.

Net neutrality in telecom services became an important issue when ‚over the top services‘ like Youtube or Netflix started to consume significant proportions of bandwidths. It became obvious that in the long run the carriers would be degraded to mere suppliers of infrastructure, just delivering a commodity instead of becoming ‚value added services‘ that could charge their customers extra for their precious entertainment programs.

Until now, users pay for using the service in terms of just transportation of the data packages, no matter whats in the date. Since they are the ones who pay, it should thus follow consequently that the services they want to access must not be charged by the telcos or otherwise the service would be charged twice. In Europa as in many parts of the world it is still mandatory for telcos to act neutrally regarding the services requested by the users.

‚Managed services‘ is what telcos are lobbying for in opposition to net neutrality. The argument goes that companies like Google (with Youtube in particular) or Facebook act like parasites on the infrastructure – skimming the profits without contributing to maintain it. Although there is very little facts provided to prove the allegations, it is not totally implausible. Advocates for net neutrality would respond though, that restricting net neutrality would give telcos a wrong incentive, not to invest in infrastructure to improve the situation, instead to shorten supply to be able to raise the price for services, or even worse, to exclude competition like Skype or Whatsapp and rather continue selling their own products like voice telephony or SMS.

With roads, the situation is fundamentally different. It is comparably easy to add new wires or build additional base transceiver stations to get more throughput for the network. It is much harder to build more roads, in urban environments it is often even impossible to increase the capacity for traffic. The consequence can be seen everyday: Traffic jams, overcrowded parking lots, polluted air. Worst examples are the big metropolitan centers in China and India, but the situation in most big cities in the US is also dire.

Road access without regulations thus leads to a classic example of the ‚tragedy of the commons‘. Each driver will ask herself, why she should be the one to refrain from the benefits of individual traffic and switch to public transport. Some cities have already introduced special tolls, like the congestion fee for entering central London.

Autonomous cars and car sharing services when becoming broadly available would indeed offer another model. Telecommunication carriers license frequencies in the electromagnetic spectrum from the state, for which they have to pay a considerable sum. In return they can offer differentiated rate plans to their customers, and realizing a significant upside for themselves. Cities or whole countries could offer mobility carriers a similar deal: Car sharing platforms would rent capacity from the public, and resell their added value mobility service to finance the infrastructure. Rate plans could be fine tuned and automatically adapt demand. It might in this way just become to expensive to use individual means of transport for a commute that you could as well do in public transport or by bicycle.

That this is no far fetched business model at all is shown by Uber. Uber’s surge pricing anticipates such mobility services reacting elastically to actual demand. A major outcry followed when people became aware that instead of being charged a few dollars like usually, they would suddenly face payments more expensive by orders of magnitude.

Since the times of Henry Ford, individual traffic in the own car has been woven into the culture of most societies. It is thus not easy see opportunities and risks from a more distanced, more objective vantage. It will also not be easy to find the right rules and regulations to make a system of managed services for mobility fair and supportive to the economy.

The worst would be a contemporary version of highwaymen. Second worse however would be to go on and waste space, pollute the air, and jam the vessels of urban life like it can be seen in many cities today. Net neutrality for mobility will therefore become an important issue.

My AlgorithmicMe: Our representation in data

Talk at Strata + Hadoop World Conference 2016, San Jose, Ca.

Today, algorithms predict our preferences, interests, and even future actions—recommendation engines, search, and advertising targeting are the most common applications. With data collected on mobile devices and the Internet of Things, these user profiles become algorithmic representations of our identities, which can supplement—or even replace—traditional social research by providing deep insight into people’s personalities. We can also use such data-based representations of ourselves to build intelligent agents who can act in the digital realm on our behalf: the AlgorithmicMe.

These algorithms must make value judgments, decisions on methods, or presets of the program’s parameters—choices made on how to deal with tasks according to social, cultural, or legal rules or personal persuasion—but this raises important questions about the transparency of these algorithms, including our ability (or lack thereof) to change or affect the way an algorithm views us.

Using key examples, Joerg Blumtritt and Majken Sander outline some of these value judgements, discuss their consequences, and present possible solutions, including algorithm audits and standardized specifications, but also more visionary concepts like an AlgorithmicMe, a data ethics oath, and algorithm angels that could raise awareness and guide developers in building their smart things. Joerg and Majken underscore the importance of higher awareness, education, and insight regarding those subjective algorithms that affect our lives. We need to look at how we—data consumers, data analysts, and developers—more or less knowingly produce subjective answers with our choice of methods and parameters, unaware of the bias we impose on a product, a company, and its users.

Let’s discuss all things Ethereum at our Ethereum Munich Meetup

More and more Big Data projects we with Datarella are involved in demand not only administrative or legal security layers but also state-of-the-art technical frameworks to provide the maximum security there is for business as well as petsonal data.

To provide our clients with sustainable solutions we have partnered with Ethereum and its spin-off Ethcore. The Ethereum framework enables us to build business models on top of it, using a very sophisticated blockchain approach, smart contracts and elements of the DAO concept and finally, our own product, the Datarella Data Trust.

Since the blockchain, smart contracts, DAO and especially Ethereum are quite new to the Big Data scene, we aim to make them better known to a broader audience. So we have started the Ethereum Munich Meetup as a platform for presentations, discussions and development of Ethereum-based ideas, concepts, use cases, projects, etc..

If you are interested in all things Ethereum feel invited to our regular meetups!

The Need For Encryption

With this post we do something quite unusual: we re-publish Apple’s letter to their customers. It’s not that we were such Apple fans that we would join  the company’s marketing efforts but the encryption of iPhones has been discussed for a long time. And this week, the FBI and the United States government, demanded that Apple builds a new version of iOS bypassing security and creating a backdoor. This might not be the first case of such a request a private company being asked to install a backdoor (we know that some have done it) but Apple does not retreat. Please read this letter and add your thoughts to that topic – either here at Datarella or other channels – we think that this needs to be discussed in public. And – maybe – there even are (secure) technical solutions to comply with the FBI’s requests AND privacy.

A message to our customers„, published by Apple, Inc., February 16:

February 16, 2016 

A Message to Our Customers

The United States government has demanded that Apple take an unprecedented step which threatens the security of our customers. We oppose this order, which has implications far beyond the legal case at hand.

This moment calls for public discussion, and we want our customers and people around the country to understand what is at stake.

The Need for Encryption

Smartphones, led by iPhone, have become an essential part of our lives. People use them to store an incredible amount of personal information, from our private conversations to our photos, our music, our notes, our calendars and contacts, our financial information and health data, even where we have been and where we are going.

All that information needs to be protected from hackers and criminals who want to access it, steal it, and use it without our knowledge or permission. Customers expect Apple and other technology companies to do everything in our power to protect their personal information, and at Apple we are deeply committed to safeguarding their data.

Compromising the security of our personal information can ultimately put our personal safety at risk. That is why encryption has become so important to all of us.

For many years, we have used encryption to protect our customers’ personal data because we believe it’s the only way to keep their information safe. We have even put that data out of our own reach, because we believe the contents of your iPhone are none of our business.

The San Bernardino Case

We were shocked and outraged by the deadly act of terrorism in San Bernardino last December. We mourn the loss of life and want justice for all those whose lives were affected. The FBI asked us for help in the days following the attack, and we have worked hard to support the government’s efforts to solve this horrible crime. We have no sympathy for terrorists.

When the FBI has requested data that’s in our possession, we have provided it. Apple complies with valid subpoenas and search warrants, as we have in the San Bernardino case. We have also made Apple engineers available to advise the FBI, and we’ve offered our best ideas on a number of investigative options at their disposal.

We have great respect for the professionals at the FBI, and we believe their intentions are good. Up to this point, we have done everything that is both within our power and within the law to help them. But now the U.S. government has asked us for something we simply do not have, and something we consider too dangerous to create. They have asked us to build a backdoor to the iPhone.

Specifically, the FBI wants us to make a new version of the iPhone operating system, circumventing several important security features, and install it on an iPhone recovered during the investigation. In the wrong hands, this software — which does not exist today — would have the potential to unlock any iPhone in someone’s physical possession.

The FBI may use different words to describe this tool, but make no mistake: Building a version of iOS that bypasses security in this way would undeniably create a backdoor. And while the government may argue that its use would be limited to this case, there is no way to guarantee such control.

The Threat to Data Security

Some would argue that building a backdoor for just one iPhone is a simple, clean-cut solution. But it ignores both the basics of digital security and the significance of what the government is demanding in this case.

In today’s digital world, the “key” to an encrypted system is a piece of information that unlocks the data, and it is only as secure as the protections around it. Once the information is known, or a way to bypass the code is revealed, the encryption can be defeated by anyone with that knowledge.

The government suggests this tool could only be used once, on one phone. But that’s simply not true. Once created, the technique could be used over and over again, on any number of devices. In the physical world, it would be the equivalent of a master key, capable of opening hundreds of millions of locks — from restaurants and banks to stores and homes. No reasonable person would find that acceptable.

The government is asking Apple to hack our own users and undermine decades of security advancements that protect our customers — including tens of millions of American citizens — from sophisticated hackers and cybercriminals. The same engineers who built strong encryption into the iPhone to protect our users would, ironically, be ordered to weaken those protections and make our users less safe.

We can find no precedent for an American company being forced to expose its customers to a greater risk of attack. For years, cryptologists and national security experts have been warning against weakening encryption. Doing so would hurt only the well-meaning and law-abiding citizens who rely on companies like Apple to protect their data. Criminals and bad actors will still encrypt, using tools that are readily available to them.

A Dangerous Precedent

Rather than asking for legislative action through Congress, the FBI is proposing an unprecedented use of the All Writs Act of 1789 to justify an expansion of its authority.

The government would have us remove security features and add new capabilities to the operating system, allowing a passcode to be input electronically. This would make it easier to unlock an iPhone by “brute force,” trying thousands or millions of combinations with the speed of a modern computer.

The implications of the government’s demands are chilling. If the government can use the All Writs Act to make it easier to unlock your iPhone, it would have the power to reach into anyone’s device to capture their data. The government could extend this breach of privacy and demand that Apple build surveillance software to intercept your messages, access your health records or financial data, track your location, or even access your phone’s microphone or camera without your knowledge.

Opposing this order is not something we take lightly. We feel we must speak up in the face of what we see as an overreach by the U.S. government.

We are challenging the FBI’s demands with the deepest respect for American democracy and a love of our country. We believe it would be in the best interest of everyone to step back and consider the implications.

While we believe the FBI’s intentions are good, it would be wrong for the government to force us to build a backdoor into our products. And ultimately, we fear that this demand would undermine the very freedoms and liberty our government is meant to protect.

Tim Cook

New Product Development in the Age of IoT

If you’re responsible for new product development in your company, you will be familiar with the several steps of that process. Experts mostly separate the new product development process into seven or eight steps, starting with idea generation and finishing with a post launch review. The fact that more and more things become smart; i.e. they either feature some intelligence or they are connected and controlled through the IoT, has significant implications on new product development, particularly on its very first phases.

Traditionally, ideation and screening of first product ideas have focused on research, brainstorming, SWOT analysis, market and consumer trends, and so forth. All these activities imply certain hypotheses and more or less tangible perceptions of products or product components. This works fine, as long as the final product is a one-way product; i.e. once produced and sold it won’t change (other than to age and break, ultimately). However, smart things aren’t on-directional, but bi-directional: they communicate, they change, and therefore their effects on consumers are far more complex and variable than those of their „dumb“ predecessors.

The smarter a thing, or a group of things, is, the more complex the situations they will create for their environment and their users. The much discussed self-driving cars which algorithms must decide whom to run over in case of an inevitable accoident provide a good example of the complexity future products will create.

Now – what are the implications of smarr things and the IoT on new product development? The answer is pretty easy – we just have to look at the discussions regarding the IoT: privacy, responsibility, sustainability, awareness, acceptance, relevance, and ethics. Is my data secure? Who takes responsibility of data provenance? Do I want this thing to be smart? Do I accept a thing’s decision? Do things add value? Do others accept me using my smart thing? Can I defend using my smart thing against my beliefs?

We can sort these crtical questions into three categories:

  • – philosophy (ethical aspects),
  • – sociology (responsibility/acceptance aspects) and
  • – psychology (awareness/relevance aspects).

Philosophy, sociology and psychology are the „new“ fields for benchmarking new product ideas. As distinct from present techniques of finding new product ideas, corporate innovation managers will have to broaden their scopes and companies will have to adapt by hiring and training their innovation departments towards these fields of expertise. Today, only very few companies seem to have inherited this new way of thinking: just look at how Apple creates and markets its products: there is no talk of product features, but of sustainable production chains, of family accounts or enhanced well-being.

Would you have thought that philosophy, sociology and psychology would play a pivotal role in new product development? Could that mean that philosophers sleeping in ceramic jars now can afford posh apartements, or formerly unemployed sociologists can choose their employers, or psychologists leave their universities to actually develop new products? And – will we see a lot mote useful, meaningful, usable and accepted products? I think so.

What you can expect from Datarella in 2016

We always take a little time in the very first days of a year to define Datarella’s main goals for this year. This time it was a pleasant task since 2015 went very well for Datarella: we achieved most of our goals and we could start without any legacy issues.

So, what to expect from Datarella in 2016? Beside our growing consulting business with fascinating projects and clients, we will focus on our product Data Trust and our project Data Coach.  Due to our tight schedule in 2015, we haven’t published much about Data Trust and Data Coach, yet.  I’d like to give a brief overview on both in this post.

Data Trust
Generally speaking, Data Trust is a secure data market model for Big Data projects. Sharing data between businesses makes much sense: Both, data processing and analytics scale with the data, and development, quality assurance, as well as support become very efficient. The problem: Many businesses are hesitant to share their data with partners for security reasons, to maintain their competitive advantage, and also obligatory compliance aspects regarding data protection.

Data Trust solves this deadlock: With it we provide a secure sharing solution for corporations. Datarella organizes each client’s original data in separate data buckets.

Data Trust enables businesses to put their data to work together with the data of their business partners with guaranteed data security and control. Without giving away their data, they can now profit from analytics, results, and predictions that are based on the joint data within their network of partners. Thus, Data Trust is a market model – it provides each participant of a market with unparalleled insights into the market.

Datarella Prediction Engine

The Datarella Prediction Engine runs on top of the separated data buckets. The Datarella Prediction Engine has been designed for gathering precise statements regarding future business success in the areas of media & advertising, eCommerce, finance, mobility and health.  Together with the Datarella Prediction Engine, Data Triust provides an absolutely trustful environment for clients to manage and analyze their company’s data.

Of course, the Datarella Data Trust can be audited.

Data Coach
Whereas Data Trust already is a product and is already creating value for our clients, Data Coach is still in an experimental phase. The user interface of Data Coach is an app that provides the user with body activity and environmental data.  The user shares this data with a closed professional graph and receives actionable insights into her health condition, behavior, training, etc. as feedback. Based in this feedback the user can react by changing her behavior.

The core of Data Coach is a blockchain environment that provides three essential elements of a professional network:

  1. Data Security
  2. Data Provenance
  3. Peer-to-Peer Architecture

Cryptographic hash functions and completely historicized data chains make data sharing absolutely secure. The user completely owns her data. And she always knows her data’s whereabouts and defines whi can use it, how and when.

An essential part of our Data Coach project is our partner Ethereum, that provides a decentralized blockchain platform we build Data Coach on.

We are running very early tests of Data Coach in the area of sports and entertainment. We are active,y looking for partners to establish a pilot project in the health sector. So, if you think Data Coach could add value to your business and customers or patients, please don’t hesitate to contact me.

If it’s smart, you can wear it!

It started with the first smart clothes in 2006, motivated luxury brands like TAG Heuer of LVMH to add some smartness to their watches and even lets farmers recognize patterns in dairy cow movements: wearables have become mainstream.

At our Wearable Data Hack this June, we partnered with Wearable Technologies, a Munich based company specialized in marketing wearables. One part of our hackathon’s prizes were tickets to the 2016 Wearable Technologies Conference, taking place in Munich, January 26-27. Looking at the impressive lineup of speakers at the conference, we’re sure that our winners will learn a lot about the near future of wearables!

If you also want to attend the conference, you might want to use this special link and our friends&family code Datarella_Friends for receiving a 15% discount. 

See you at the Wearable Technologies Conference 2016 in Munich!

Know And Speak To Your Customer Through The IoT

Equipped with sensors and microchips, ever more of objects can communicate, with each other and with human beings – the Internet of Things and Humans is born. Even washing powder is set to become smart through attached sensors on its packaging that detects when the product is being used, and that communicates with readers like smartphones when scanned. From today’s 5 billion to 21 billion by 2020, the number of wirelessly connected things will increase. 

Who will profit from the Internet of Things – or IoT – most?
Let’s look at the obvious applications, first: Stock-keeping and supply-chain management processes will have implemented technologies making objects communicating with each other, thus enabling businesses to follow the progress of their products from factories to shops to end-consumers. Espresso beans will tell baristas about the best temperature to keep them in stock. Batteries will inform their owners when reaching the last phase of their power-providing lives.
From our perspective, however, the biggest potential lies in customer relations. Brands are realizing that the best way to sell their products is to build personal relations with customers rather than to spend lavishly on marketing. Until today, many brands come only second behind retailers with regards to communication to their customers. Often, it’s the retailer who knows the customers‘ preferences best, and who is able to retain them through loyalty schemes. Ask yourself: where do you buy your stuff? Online retailers are the ones understanding their customers best, often they have the complete customer journey available.

Speak to your customer
Now it becomes cheaper to add sensors and microchips to products, and to connect them to the internet. The direct result of the IoT is a huge influx of customer or end-user data. Whereas nobody at manufacturers and brands had to – or better: was able to – gather data of actual customer behavior so far, there now arrives Big Data describing everything these companies have ever wanted to know. In theory, this data will help them develop their products and services more rapidly, fix any bugs more quickly and tailor products better to their end-users‘ needs.

In practice, the gigantic flood of data may imply significant structural changes for manufacturers: is the IT infrastructure set to cope with Big Data? Is the IT personnel eqipped with the necessary knowledge amd experience to handle data sampling and dara storage correctly? Are there adequate in-house resources to munge and analyze the data? And, after all, will anybody visualize and instrumentalize the data in a meaningful way? Sure, there already are best practices regarding individual steps of this Big Data process. And for each part of the process, there are tools that can be used a software-as-a-service. But it`s a long way from data sampling to a smoothly managed customer relation through objects.

A new era of competition
Managing and optimizing the company’s own customer relations through the IoT is just one part of the equation: when retailers start a direct communication with their end-users, retailers won’t passively stand on the sidelines. They will fight back trying to defend their position of being „first-to-the-customer‘. And on the operative level, retailers have a big advantage: they have always been the ones with a direct contact to the customer. They know their customers (or at least they should). Everybody who switched from working in a company without direct contact to the customer to an end-user shop has experienced a cultural clash: you won’t imagine the expectations of a typical end-user – i.e. ourselves. Let alone all additional privacy and cyber-security issues.

That said, the IoT offers manufacturers the biggest opportunity since the industrialization. They can understand and learn from their customers. They can even partner with them. But first they have to do their homework and provide all the needed resources in IT infrastructure, processes, and human resources.

Predictive modeling – an interview with Datarella CEO Joerg Blumtritt

Most of the time, we at Datarella deal with the very near future: what will be the most likely thing that individuals want or what they are up to next? We use mathematical models to predict human behavior. But not only human behavior is what Datarella is interested in. Research papers, internal customer data and external data are being used by Datarella to predict probabilities of success of specific products. We detecs patterns, weaknesses and events in behavior and product design and – together with our customers – we define workarounds, optimizations or completely new data-driven products.

Breaking Bad Habits with Self-Tracking

For the forth time, the Quantified Self Conference took place in Amsterdam. Quantified Self is a way to get „self knowledge through numbers“ as the two founders Kevin Kelly and Gerry Wolf put it, learning about one’s life by measuring various aspects of our bodily funcions, our actions, habits, and environment. With all kinds of tracking devices from simple step counters to complex sleep monitors, that are know generally available in every consumer electronics store, Quantified Self has matured from a nerdy, rather esoteric niche to a mainstream trend. In many countries, healthcare institutions are experimenting with self-tracking, and there is a plethora of self-tracking apps for iOS and Android smartphones.

„Self-tracking is about change. But change is more often not about doing, but about stopping to do something.“ Gerry Wolf introduced this year’s conference with a keynote about breaking routines. A routine, he remarked, is a method to fight entropy. It consumes energy to maintain routines. Routines are efficient, as long as the conditions remain unchanged, but it restrains our acting freely. Self-tracking for most people is about uncovering routines in daily life, making bad habits visible, and then guiding the change by supplying an indicator.

When self-tracking is used to break habits, it opens additional degrees of freedom. Thus self-tracking is not so much about self-discipline, about restricting actions, living according to more rules, but about pushing the boundaries, and reliefing from constrains that are not neccessary, but exist just because we are used to do things that way.

Bad habits can creep into all our everyday activities. And self-tracking is not limited to counting the steps or measuring blood pressure. There are already a few apps that support people by tracking their driving. Acceleration (respectively breaking), turing, and speed can easily be tracked with the sensors that sit on every smartphone. From the readings of these probes, indexes can be derived, that give feedback on the quality and safety of driving. Becoming aware of bad habits can not only help the driver to save energy by learning to drive more ecologically, but reduce stress and lower the risk of accidents. Self-tracking can by this help driviers to act more consciously, and thus give them more freedom on the road.