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!

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.

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.

(null)

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.

 

Data is the new media

Data storytelling, data journalism, and even data fiction – since the advent of Big Data, we find data more and more as tool of narratives. With pattern recognition, exploratory data analytics, and especially with data visualization, data has re-centered from the quantitative to the qualitative.

More and more applications support us in using data to tell a story. Dashboards like Tableau or DataLion plug into our data sources and translate the numbers into a visual format that can be much more easily digested. Even highly multivariate data can deliver straightforward meaning to us when we use tools like Gephi, or say, the notorious Palantir. These tools also make social media analytics and text mining feasible techniques to research society, advertising, and markets.

Jawbone Up not only tracks our sleep. The app also shares our data in a meaningful way with our friends - like we share our thoughts on Twitter.
Jawbone Up not only tracks our sleep. The app also shares our data in a meaningful way with our friends – like we share our thoughts on Twitter.
Data driven storytelling has conquered most non-fiction publication. News publishers like New York Times or The Guardian employ huge teams of infographic specialists to enrich their reports with meaningful data visualization. Some of their editors have put together awesome collections of beautiful examples, e.g. informationisbeautiful.net.

Our most personal data however is generated on our mobile and wearable devices. On our smartphones, wristbands, or smartwatches, some twenty sensors continuously track our behavior and our actions. There is a plenitude of apps making use of mobile data: To support our training, to guide our routes, to find friends nearby, to share images, etc. etc.

Many people already share their daily workout via apps like Strava or Runtastic. It is even quite common to let such apps automatically post your training results into your social media timeline, e.g. to Twitter or Facebook.
Many people already share their daily workout via apps like Strava or Runtastic. It is even quite common to let such apps automatically post your training results into your social media timeline, e.g. to Twitter or Facebook.

Apps like Jawbone Up or Strava not only track our workout, they also provide for an easy way to share what data they measured. We publish our training data the same way there, as we publish our stories on Twitter or Facebook. Our data becomes equivalent to the texts and images we post. The most highly integrated version of this data-as-story so far is Google Now.

Image on top: Google Now. Google Now follows the idea to display all kinds of information in the form of tiles, like Twitter or Facebook would display the posts of the people you follow in a timeline. Funny enough, Google obviously has no clue where my "place of work" seams to be.
Image on top: Google Now. Google Now follows the idea to display all kinds of information in the form of tiles, like Twitter or Facebook would display the posts of the people you follow in a timeline. Funny enough, Google obviously has no clue where my „place of work“ seams to be.

Data is media not only regarding the content. Advertising which has by and large been data driven for decades is facing a major transformation. Media planning and buying – the art of placing ads in the most efficient way, i.e. optimizing effect for a given budget – is changing dramatically. About 20% of all ads are placed programmatic now. Programmatic buying means that an algorithm decides which exact user would be appropriate to watch the ad instead of buying the spot via explicit insertion order, as it used to be. The decision if a certain user would match with the campaign’s objective is made by predictions based on the users‘ observed behavior. Data thus drives the ads we get displayed.

With the idea of ‚The Quantified Self‚, data starts to conquer even the concept of our identity. We are not only what we tell, how we appear, how we act voluntarily, but we are as well defined by our innards, by our bodies‘ functions, the data that comes from our physical being. The concept of ’self‘ is changing by this notion, overcoming the strict separation of mind and body, of conscious and unconscious. The physical aspects of our lives now get equal credit, as being veritable part of our being ourselves.

Data is becoming integral part to our stories. It pervades through all the media. We should learn to see data as part of our lives the same way, we are used to tell about things with words.

Further reading:

We are content!
Data stories: From facts to fiction.

Sharing Goods And Sharing Data: Both Is Fun, Big Business And A Social Responsibility

Around 2010, Lisa Gansky coined the term Sharing Economy, or Mesh companies, offering their customers efficient shared access to their products instead of selling their products to them. Recently, it’s being called Collaborative Consumption or Collaborative Economy. It’s all about finding ways to make better use of valuable resources that have remained unused. Convenient access is being made affordable to people who can’t afford different products, or simply don’t need to own those products since they would only use them infrequently.

Typical mesh businesses like AirBnB, LendingClub or Cookening, demonstrate the power of sharing in very different ways: AirBnB is on the way to pass Hilton as the world’s largest hotelier in 2015, that is 7 years after its inception. The US peer-to-peer lending company Lending Club has originated over 4 billion USD in loans – it was originally founded as a Facebook app in 2006. The typical Mesh business runs a stylish app with a high usability. It’s service is new, easy to use and affordable. But all that does not fully explain the tremendous speed they conquer one market after the other. Who is the driver behind the Sharing Economy and it’s success?

It’s the user.
It’s the user. The user offers and asks for private overnight stays on AirBnB, the user provides and lends money on LendingClub. Even with services like Zipcar, when the product is provided by a company, the user „uses“ a product instead of buying and owning it. He has to rely on other users‘ good maintenance of Zipcars, since if there were too many ‚abusers‘ the company had to raise rates and the product would become unaffordable for most people. The same is true for AirBnB and others: users have to be sure that landlords don’t sell cubbyholes to them, whereas – vice versa – landlords have to trust their guests not to steal the TV or destroy the flat. So, the user has to use the service and she has to behave in an orderly manner – this is the foundation for a properly working and successful Sharing Economy.

The Sharing Economy

Image: The Sharing Economy, Latitude

Now let’s adopt the principles of the Sharing Economy to the individual who shares her statuses with her social graph on Facebook, discusses the latest news on Twitter and shares her preferred fashion designs on Pinterest. She dos it because she wants to express herself and she wants to communicate with a wider circle of friends than she can meet in person. She communicates in both, synchronous and asynchronous ways. She has learnt that the more she adds to discussions, the more she gets in return. In Social Media, she experiences the Pay-it-Forward principle in action at its best.

Sharing Economy has attained full age in 2014
Let’s assume we can all agree on that: communication openly and actively, sharing ideas, opinions, homes, cars, money and much more with others is not an extravagant imagination of Utopia, Inc., but a multi-billion dollar business eclipsing traditional business models around the world. Furthermore, it’s not just a gigantic business but a sympathetic and friendly way of matching supply and demand of individuals. Who wouldn’t prefer an individually furnished private home over a standard hotel room?

If we agree on the power of sharing the above mentioned martial and immaterial goods, can we also agree on the power of sharing data? Our data? Our own body’s data? Can we agree on the tremendously positive and socially relevant effects of sharing the data we produce ourselves, day by day? If you own a smartphone (you most probably will), you produce about 20 MB of smartphone data (i.e. data racked with your smartphone’s sensors) each day. Perhaps you haven’t been aware of that fact, or you just didn’t know how relevant this data could be for yourself, and for your social graph, respectively. Do you know how much you move each day? The U.S. Surgeon General wants you to move at least 10,000 steps a day to prevent and decrease overweight and obesity. (It’s very easy to know your steps: just get yourself one of those fitness trackers.)

And, did you know that your Vitamin D level is one of the key drivers of your well-being? Most North Americans, North and Central Europeans suffer from a Vitamin D deficiency. Do you actually know your Vitamin D level? Do you know that you can find it out yourself?

Let’s get more complex regarding data: microbes in the human body are responsible for how we digest food and synthesize vitamins, our overall health and metabolic disorders. The aggregate of microbes is called microbiome. Do you have any idea about your individual microbiome? Do you know that you can find out about your microbiome yourself, by using a simple kit?

Sharing Data still in its infancy, but…
So far, we have talked about the relevance and value of our data for ourselves. But – weren’t you interested in preventing your first stroke because thousands of other men at your age have provided their heart and respiratory rates anonymously and based on the analysis of this data you had been warned early enough to take appropriate action?

Wouldn’t you agree that the Pay-it-Forward principle works perfectly in the field of personal body data? The difference to the AirBnB model is that you provide your body data anonymously. It will be aggregated and used in a way that nobody knows that’s you behind your data. Since data analysis and respective actions or recommendations rely on big data, it’s necessary that many people participate and share their own data.

… will become a Social Responsibility in 2017
Today, in late summer of 2014, many people are sceptical and hesitate to provide their data. We think that personal body data sharing will be regarded as quite normal within a few years. If this movement takes up the same speed as the Sharing (Goods) Economy, it will be accepted as „normal“ within 2-3 years. We believe, that data sharing will become a social responsibility, comparable to fasten one’s seat belt or wearing a bike helmet. It probably won’t be called data sharing since this is a B2B term. There already is a very good term  – the Quantified Self, or QS. The term itself does not include the sharing element. But for every active members of the QS movement sharing is a relevant part of the quantification process because the value of an individual’s data is even bigger if used for general purposes.

We regard data sharing – our Quantified Self – as one of the most important movements of modern times and we would love to know how you think about it: please comment, provide us with your feedback: do you already share your data? How do you do it? Or, are you still sceptical?

Feature Image: Max Gotzler of Biotrakr, presenting findings of a Testosterone study at #QSEU14

Global Sleep Patterns

Sleep is one of the most interesting aspects of life: during sleep we don’t act consciously (apart from a few natural processes inside our body) and therefore some people try to minimize sleep to get most out of their lives. Others maximize their sleep: for them sleep simply is the greatest activity they could think of. The Quantified Self folks try to optimize their sleep; i.e. to maximize their sound sleep phases and minimize light sleep and times of being awake.

The guys from Jawbone looked at their UP band user’s sleep data and could provide us with this interesting global sleep pattern. Since Jawbone’s data are more detailed and accurate than the American Time Use Survey, this view on the different sleep patterns provides great insights in how inhabitants of cities behave, or how active a city is, seen as a whole. The average hours of sleep shown in the feature visual above do not include time awake in bed.

Science tells us that we should sleep between 7 to 8 hours per night and we should sleep during the same cycles in order to maximize recovery and relaxation from our daily routines. Now look at people living in Tokyo: with 5h 44min they sleep least, whereas Melburnians (yes, without the „o“) sleep most with 6h 58min – which is just the bottom end of the recommended length. Again Australians, this time the folks in Brisbane, go to bead earliest, at 10.57pm. The night-owls in Moscow hit the pillow almost 2 hours later, at 12:46am. But Muscovites (rise latest at 8:08am) sleep only 29 min less than the Brisbanian who raise at 6:29am.

sleep cycle

Image: The author’s sleep pattern on August, 19 – provided by the Jawbone UP app

How about you? Do you know how long you sleep? Do you know about the quality of your sleep?

Above, you see a snapshot of my own sleep: On August, 19, I had 5h 22min of sound sleep and 1h 52min of light sleep – which is a pretty good ratio. I reached my goal of 7 hours of sleep as well, which is partially due to the fact that we are in the middle of school vacation and there is no need to mange kids in the mornings. So, don’t worry if your sleep looks less sound – I wake 1 time every 3-4 nights.

The most relevant criteria defining my personal sleep are
– duration
– cycle; i.e do I go to bed and rise at roughly the same times?
– alcohol input
– time without staring at a display before going to bed
– general family mood during the evening

I’d love to know about your experiences with your sleep. Do you track? What makes you sleep sound or light, short or long?