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 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.

 

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

Could we learn all about behavior change by simply applying some makeup?

At the Quantified Self Conference 2014 in Amsterdam, I attended a breakout session with the promising title „The Future Of Behavior Change“, moderated by Lukasz Piwek, a Research Fellow in Behavioral Change at the Bristol Business School.

Our curious and engaged crowd discussed that matter extensively and – unsurprisingly – left the session with more questions than answers. But: „it’s not about the answers, it’s about find more questions. Answers are so temporary“, as Ernesto Ramirez, the Quantified Self Lab’s Program Director elegantly tweeted. As a good researcher, Lukasz himself offered some possible answers but concluded that definitely more research is needed.

Based on his studies at Bristol Business School, he thinks that these factors lead to a lack of long-term user involvement while using Quantified Self tools, which is a major condition for behavior change:

– Lack of data validity and reliability
– Oversimplification of inferences

Regarding his first point, I take the risk of completely ignoring (his) research and putting my shirt on good judgement (which I usually avoid to do): I claim that people don’t bother at all, ignore any scientific shortcomings and look for comprehensibility and usability of Quantified Self tools: as long as variances of results; e.g. number of steps taken or burned calories stay small, users will accept them and stick to these tools. As often, critics as experts or journalists, point to flaws in technology, but individual users remain unaffected. We see this behavior in many seemingly sensitive topics, such as the privacy discussion. My guess would be: ask a hundred users if they are concerned about validity or reliability of collected data, and 95 would say „no“. We will do that via our explore app and I’ll hand the results in later.

Lukasz‘ second finding is a bit more challenging. I must admit that I’d prefer to base my claim on reasonable data. And yet, I don’t believe in this finding either: most people I know – during the session Carine Coulm  introduced the term ’normal people‘; and I refer to exactly these people – prefer simplification over complex relations. They want the world to be explained to them, to be constructed, rather than some analytic and deconstruction work creating more problems than results. Most people want simple solutions. Therefore, I suspect that users of quantified self tools have to experience simple (positive) results in order to stay motivated.

At the 2014 M-Days conference, Frank Kressmann of Braun GmbH, a Procter & Gamble subsidiary, presented the first interactive electric toothbrush. It communicates via Bluetooth with an app, shows how long a user has been brushing her teeth, and gives some recommendations on the optimal pressure on the toothbrush head. P&G had 8,000 users tested this toothbrush who really seemed enjoying their oral hygiene: they extended their average duration of brushing their teeth from 45 sec to 2 min and 16 sec, a 300% change. Obviously, there’s an enormous impact of making a toothbrush interactive – it results not just in a minor, but in a significant behavior change.

Which brings me to the title of this post: Could we learn all about behavior change by simply applying some makeup?: Imagine Whitney, dressing herself up for tonight’s party. Before Whitney will leave her house she puts on her make-up. She does that in front of her mirror. At some point, she decides that it’s enough and she’s satisfied with what she sees in the mirror. She is ready to go.

Here are the ingredients of that behavior:
– Goal: looking good for tonight’s party
– Means: the makeup
– Control and measuring module: the mirror
– Feedback loop: many small make-up actions and their results
– Benchmark: Whitney’s look before vs after makeup
– Result 1: looking good
– Result 2: compliments from her friends

With her first glance into the mirror, Whitney realizes that she wants to enhance her look. (Maybe she doesn’t but for my example I need her to do so.) By knowing the instrument for an action which will change her look for the better, using the instrument and experiencing immediate effects of her actions, Whitney learns that specific actions lead to required results.

Couldn’t we therefore simply add mirrors to people’s daily behavior in order to motivate them to change their behavior? It doesn’t have to be real mirrors; it could be a tool mimicking the function of a mirror, such as an app which asks her user things about her actual behavior. Or an app like the one connected with the toothbrush – which mirrors the cleaning of its user’s teeth. The user would then be reminded of her actual behavior and „see it“ like she would see it in a mirror. Do you yourself use a mirror on a regular basis? Which kind of mirrors (in a metaphorical sense) could you imagine?

I’ll elaborate on behavior change in forthcoming posts. But for moment, I encourage you to put on some makeup (as our CEO Joerg in this post’s feature visual, by courtesy of Kira, photographed by Yuki) in front of your mirror and share the results with us!

Smartphone Geiger Counter

Smartphones carry versatile sensors. With appropriate apps, expensive instruments can be very well replaces - even sometimes the Geiger counter.
Smartphones carry versatile sensors. With appropriate apps, expensive instruments can be very well replaces – even sometimes the Geiger counter.
When photons, the particles of light, hit the chip of a smartphone’s camera, they excite electrons on the chip’s surface and change the conductivity or even generate voltage within the small area arround the impact.

Gamma rays which are often products of radioactive decay, are also electromagnetic waves, just like light, however much more energetic. That means: as radioactive radiation can expose a chemical photographic film, it can as well effect the camera chip in the smartphone.

A team of researchers at the Idaho National Laboratory in Idaho Falls have used this property to change common smartphones into detectors for radioactive radiation. The radiation is recorded via the camera an an app, which calculates the radiation intensity from the data collected.

With this approach we learn again, how versatile mobile devices can be deployed. Up to thirty sensors in each smartphone measrure all kinds of variables like temperature, magnetism, brightness, sound and many more. With a little creativity we can combine these measurements and get valuable data about the environment around the smartphone and its user, that not rarely can replace expensive, specialized methods.

Here the link to the original publication:
Joshua J. Cogliati, Kurt W. Derr, Jayson Wharton: Using CMOS Sensors in a Cellphone for Gamma Detection and Classification

explore yourself – Datarella’s behavioral analytics app

With explore you will learn more about yourself and your own behavior. You may answer survey about yourself, your daily activities, your habits and opinions. You may answer all surveys, or you select the ones which are of interest for you. Additionally to the questions asked by our editorial team, explore tracks your locations and movements.

Data gathered with explore will be presented to you anonymously and in a non-traceable format – either as clear charts, on maps or as texts. That said, your data will be analyzed, but nobody knows that it’s your data. In the same way, you don’t know who else is a member of explore or who is behind individual data points.

3 reasons to use explore

  • You can participate in the explore contest and win cool prizes!
  • You can compare yourself with other explore members!
  • You will know yourself better!

The goal of using explore is to understand yourself better. The combination of actively answered surveys and the collected location and movement data provide a comprehensive perspective on your daily behavior and your attitudes.

You will find questions from the following sectors of daily life:

  • Fitness & Health
  • Jobs & Career
  • Recreation & Family
  • Traveling & Transport
  • Vacation
  • Sports
  • Media
  • Personal Finance

You will be amazed about how much you still can learn about yourself and, with a little luck, your collected points will earn you a prize in our ongoing contest!

explore is available in the following languages:

  • English
  • German
  • Chinese
  • Russian

Download explore from Google Play!

Datarella Consulting

Datarella Consulting

We specialize in Big Data and Quantified Self consulting. Our Big Data experts provide researchers and customers in the healthcare, media, tourism, finance and retail industries with analysis based on data science models and help them to comprehend the human behavior better. Our QS consultants help companies to understand the Quantified Self framework and to instrumentalize Quantified Self tools in order to build loyalty programs, mobility concepts, self-care schemes and inhouse communication analyses. 

BIG DATA CONSULTING

  • Analyze what’s there; collect data to fill the gaps.
  • Interpret the results; get the meaning out of the analyses.
  • Decide based on the insights.
  • Implement your decision.
  • Control and feedback KPIs from the day-to-day operations.

The most important and economically most precious foresight is: “What will people do next?“ If we know what our customers will need, want and what they’ll be willing to pay for, we can prepare our business to meet the demand. More often than ever, a new product, service, or technology enters the market and breaks it up before any of the established companies are able to adopt. In these times of Darwinian competition, when small startups or even unbound individuals can bring up something new that totally disrupts your business, it a view into the future as clear as possible is not an option, it is mandatory. With the Big Data revolution, everything gets quantified. Our whole reality is datarized and put into databases, waiting for our queries. Data Science provides the tools at hand to mine and combine the available information – numbers, texts, images, video, geolocation, sensor data etc. – into comprehensive models for mapping our social, political, cultural and in particular our technological environment.

Cultural Foresight
It is no longer sufficient to look into your regional market to get visibility. In countries like Turkey or Brazil, many aspects of future life that we will see all over the globe are already evolving. While mature markets like the US or Germany struggle with the digital divide between the so called digital natives and those who had to “immigrate” into the digital realm, societies like Ghana or Nigeria just skipped the “land line age” and become the mobile natives now. It is thus necessary to broaden the perspective.

Comprehensive View
Input from our global expert panel that we survey via our smartphone-based technology at iognos and the models built on the corpus of the data collected allows us to make a good guess, what’ll be next.

Have a look at our Big Data workshops!

QUANTIFIED SELF CONSULTING

  • Loyalty Programs
  • Mobility Concepts
  • Self-Care Schemes
  • Inhouse Communication Analysis
  • OS Social Research
  • Custom Inhouse QS Products

The Quantified Self, an international collaboration of users and makers of self-tracking tools, has been started by WIRED editors Kevin Kelly and Gary Wolf in 2007. However, the origins can be traced back to the very first person who weighed herself in order to track her body weight. Virtually everybody tracks herself one way or another. According to Pew Internet, 60% of U.S. Americans tracked themselves using a special tracking tool and about 46% changed their behavior based on their tracking data.

Based on the experiences with our Quantified Self app explore, our QS experts help you to understand the Quantified Self framework and its potential impact on your company. Could you use QS tools to analyze the communication behavior of your employees? Should you implement a QS self-care scheme to prevent illnesses and minimize your sickness absence rate? Or would sou rather integrate QS into your existing customer loyalty program in order to open up a direct, individual communication channel to your customer and maximize retention?

For a start, you might kick-off with a Quantified Self workshop, to get familiar with the potential of this innovative framework.

Virtuelle Assistenten – Eine neue Generation von Apps wächst heran

Wer Siri oder Google Now kennt, weiss die Dienste der virtuellen Assistenten zu schätzen. Von einfachen Arbeiten wie der raschen ins Smartphone gesprochenen Erinnerung oder einem Kalendereintrag angefangen, über Kleidertips für den Folgetag aufgrund der integrierten Wetterdaten bis hin zur geänderten Routenplanung aufgrund neuer Stauinformationen: es ist einfach sehr praktisch, wenn jemand „mitdenkt“.

Mitdenkende Apps sind aktuell stark im Kommen: Auf dem Bloomberg Next Big Thing Summit stellten sie den Löwenanteil der Geschäftsmodelle, die man im Silicon Valley für die meistversprechenden hält. Da Apps natü+rlich nicht wirklich mitdenken, sondern relevante Daten aus dem jeweiligen Nutzer Kontext integriert, spricht man von contextually aware applications. Über Sensoren sammeln Smartphones Daten über die Bewegungen, Aufenthaltsorte, Interaktionen, Geräusch- und Lichtszenarien,  in denen sich der Nutzer bewegt.  Insbesondere die Gesundheitsbranche steht hier vor eine Revolution: Apps und tragbare medizinische Geräte können beispielsweise herzinfarktgefährdeten Menschen rechtzeitig vor dem Infarkt mitteilen, dass eine Notsituation bevorsteht. So wird aus einem Life-logging für Nerds schnell eine praktische massentaugliche Anwendung, die kein Betroffener mehr missen möchte. Oder, wie Gary Wolf auf der Quantified Self Conference 2013 treffend bemerkte:

„Quantifying yourself will be regarded as a responsibility.“

Damit den reizvollen Anwendungen auch die entsprechenden Technologien zur Verfügung stehen, kündigen derzeit Beratungsgesellschaften wie IBM aber auch der Chiphersteller Intel Produktoffensiven an, die im Falle von Intel gar einer Neudefinition des gesamten Unternehmens gleichkommen.

Human API – Quantified Self für jedermann

Self Quantifizier wissen es schon lange: nicht nur das Sammeln und Analysieren der eigenen Daten, sondern gerade das Teilen der Daten mit anderen bringt hohe Erkenntnisgewinne. Was zunächst für weniger technologisch-affine Menschen undenkbar ist – das Teilen privater Verhaltensdaten mit anderen – bringt neben dem Wissen über die eigene Person eine Einordnung in das jeweilige soziale Umfeld mit sich, die dem Nutzer wiederum extrem hilft, sich in der Gesellschaft zu verorten.

Andrei Pop geht mit seinem Startup Human API den nächsten Schritt:: er möchte dem Normalbürger, der kein ausgewiesener Quantified Self Fan ist, möglichst einfach zu den oben beschriebenen Erkenntnisgewinnen verhelfen. Human API aggregiert Schnittstellen einer Reihe von Apps und Geräten aus dem Gesundheits- und Wellnessbereich. Zusätzlich macht das Startup externen Entwicklern die Normalisierung und Standardisierung der Daten einfacher. Über inhaltlich zusammenpassende APIs  werden kategorisierte Datenströme geliefert: so ist es für Dritte wesentlich leichter, auf thematischen Datenströmen aufzusetzen.  Dass diese Daten begehrt sind, zeigt der Andrang: über 600 Entwickler meldeten sich innerhalb der ersten 48 Stunden nach Einladung.