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!

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.

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.

Telling the story of people’s lives – Strata+Hadoop, Feb 15, San Jose

We can draw a colorful picture of people’s everyday lives from the data we collect via smartphones. To tell the data-story, we need to translate the raw measurements into meaningful events, like “driving a car”, “strolling in a mall”, or even more intimate, like “being nervous”. We will show how to access the phone’s data, how to derive complex events from the phone’s raw data, and how to bring it into a meaningful story, and how to make it work for businesses.

Cases we’ll show: an app for the automotive industry to support ecological driving, learning about preferences of Chinese passengers at an international airport, and supporting people suffering from osteoporosis to stabelize their condition and maintain mobility.

More on Strata+Hadoop

 

Wearable Data Hack Munich 2015

Today, we would like to announce something special. Something we can’t wait to take place and until mid June it’s going to be tough to sit tight. Please, feel invited to our Wearable Data Hack Munich 2015!

The Wearable Data Hack Munich 2015 is the first hack day on wearable tech applications and data. It will take place right after the launch of Apple Watch – the gadget we expect to rise the tide for all wearables. Withe the Wearable Data Hack Munich 2105, we aim to kick-off app development for the emerging smartwatch and wearable tech market. During this weekend you will have the first occasion to share your views and ideas and jointly gather experience with the new data realm.

Apple calls the Apple Watch “Our most personal device ever”. And with good cause: The data from wearable tech, smartphones and smartwatches are really the most personal data ever. Our mobile devices accompany every step we take, every move we make. A plentitude of sensors on the devices draw a multidimensional picture of our daily lives. Applications of wearable data range from fitness to retail, from automotive to health. There is hardly an industry that cannot make direct use of it. And yet, wearable apps still are in their childhood. The Apple Watch will be hitting the street in April and will get the ball rolling.

The Wearable Data Hack Munich 2015 is jointly organized by Stylight and Datarella.

THREADS TO BE PURSUED
Developers, data geeks and artists will pursue one or more of these threads:
– Data-driven business models for wearables
– Data-driven wearables
– Smartphone app (Stand alone / combined with smartphone)
– User Experience
– API
– Open Data
– mHealth / Medical Data

So let’s explore what we can do with this data! Let’s play with the possibilities of our wearable gadgets and mobile sensors.

APPLICATION
To apply for the Wearable Data Hack Munich 2015, please send us an email with
– your name
– your profession
– your take on wearable data
– 3 tags describing yourself best.
Don’t wait for too long – the number of participants is limited.

For more information, please have a look here! See you at Wearable Data Hack Munich 2015!

The Datarella World Map Of Behavior

Every smartphone user produces more than 20 MB of data collected by her phone’s sensors per day. Now, imagine the sensor data of 2 billion smartphone users worldwide, translated into realtime human behavior, shown on a global map. That is the vision of the Datarella World Map of Behavior.

A typical 2015 generation smartphone sports up to 25 sensors, measuring activities as diverse as movements, noise, light, or magnetic flux. Most smartphone users aren’t even aware of the fact that their phone’s camera or microphone never are really „off“ but that they constantly collect data about the noise level or the intensity of light the user is experiencing.

Actions speak louder than words
Actions speak louder than words – if we want to really know a person we have to know how she behaves, and not only what she says. And that’s not only true for politicians. We all form our opinions on others by looking at their actions, more than their words. Many inter-personal problems result from NOT looking at people’s actions, but focusing on other aspects, such as their looks or their words. Behind superficial distinctions such as physical appearances, over time we often realize similarities with other people based on their and our actions.

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Our vision of a World Map of Behavior
At Datarella, our vision is to show the actions of people around the world on a global map. By displaying the actions of people of all continents, we want to tell stories about the differences and similarities of global human behavior – to draw a picture of human co-existence. There already are snapshots of global behavior, provided by data focused companies, such as Jawbone, who map sleep patterns worldwide. From that we know that Russians get up latest, and Japanese get the least sleep in total . And there are different behavior-related maps, showing the world’s most dangerous places, defined by the number and seriousness of crimes or actions of war.

Co-operation & Participation
To create the World Map of Behavior is our ambitious project for 2015 that we won’t complete alone. We need your support: if you are an expert in the field of mobile sensor data or if your company already focuses on collecting and interpreting mobile sensor data in the fields of mobility, finance, health or transport and travel. If you are interested to play a role in this project, please send us an email with a brief description of how you would like to contribute. We are looking forward to hearing from you!

The Datarella Data Timeline

If you live in the Americas, Europe, Asia-Pacific or Australia, you probably are one of 1.76 billion smartphone users. What do you do with your smartphone? You use apps for texting, social networking, gaming, etc. And you produce data. Lots of data. Every move you make, every breath you take, your behavior results in data: location, movement, body functions, external situational data. And you produce all this data on the fly, passively, without any effort.

But where is this data? Can you see it? Do you know exactly which data you produce? At which times or to what extent? Do you know that your data can be of tremendous value for you? That you can improve your health by using it? Can you imagine knowing all about your data, that you see your data, in realtime, presented in a meaningful way that lets you understand its value?

Welcome to the Datarella Data Timeline 

The Data Timeline  is an app-centric timeline of your own behavior data and that of your social network. Using the Data Timeline is like looking into a mirror: you see yourself – not your external appearance but the visualization of your body data, you see the most complete picture of your Self. he Data Timeline presents your data in a contextualized way: you see the data and its meaning in individual contexts – your personal data moments.

Reason Why

Each day, an individual produces about 20MB of smartphone data. This data can be of vital relevance for her: it can save her life. Or it can help her optimize her life. Or the life of her friends. In order to make meaning of this data, the user needs her own individual data timeline, she needs to see her data, in its contexts and visualized in a comprehensible way.

You and your friends

And it’s not only your Self – but you see real time snapshots of your friends, too! You know about their well-being, their movements or the noise levels they are exposed to. So you already know that your significant other is quite exhausted before she returns from work and you could prepare a candlelight dinner for her. You can share snapshots of your Self with your friends right away from the Data Timeline using the integrated Twitter, Facebook et al. sharing options. You decide with whom you want to share your data.

Own your body’s data

You should own your body’s data – because your data can reveal much more than even your doctors know. Your data is a very important part of you. You can use the Data Timeline to get the meaning of your data and – if you are a geek – you might even download all your raw data, dive into it and analyze it with your own tools. You have full ownership, full transparency and full visibility of your data.

The Data Timeline essentially is a platform matching users with Quantified Self services. It comes as in the user-centric format of an app timeline displaying the user’s individual behavior data and that of her social network. It shows an individual’s body and environmental data in its specific contexts. Passively tracked data are complemented with interaction data, such as status updates, comments and answered surveys.

The Data Timeline provides the user with her data itself and – via its API – the Data Timeline matches the user’s individual needs based on her data with respective Quantified Self services in the areas of health, fitness & well-being, travel & tourism as well as finance.

The Data Timeline’s technology is based on open source software, but all algorithms, especially for the Complex Event Processing Engine, are fully owned by Datarella. All raw personal data of individuals is owned by its respective users, according to German law. However, all aggregated data, all visualizations, and all analysis based on that data is owned by Datarella.