Fri 24 June 2016
[Process One] Elixir Paris Meetup: July 5, 2016
Elixir Paris Meetup will happen on July 5th in downtown Paris, at Remix Coworking.
The programme includes the following talks (in French):
- Lessons learned by rewriting a SaaS application in RubyOnRails in Elixir (Thibaut Barrère)
- Sidekiq and Exq (Bryan Frimin)
- Phoenix Presence: Phoenix 1.2 realtime service (Mickaël Rémond)
You can register to attend on Paris.ex meetup page: Elixir Paris Meetup #8
Recently, I was fortunate enough to find myself in Munich, Germany during a trip to visit with family and discovered that just north of town is the city of Ingolstadt, which is home to the Audi factory. Being somewhat of a gear-head and very much an Audi fan, I decided to take the factory tour and check out the museum (I essentially got a private tour as I took the English version and it was only my wife and I on it - HIGHLY recommend it!).
The factory is awe-inspiring. The precision, engineering skill, and capability, and just the sheer magnitude of what happens there is difficult to convey in words (and they won’t allow you to take pictures or I would have). As we walked amongst the fully automated parts delivery, welding and assembly robots, and the amazing tooling and stamping lines, I was struck, as was my wife, by the sheer digital power on display. The interconnection of CPU "brains" in these devices that is required to achieve such incredible efficiency and deliver a completed car from blank steel within 34 hours is staggering. I said, as we passed between buildings, that we were witnessing a great example of the Internet of Things (IoT) in action, but I think actually that IoUT would have been a more appropriate term to use.
The IoUT vs. The IoST
The IoUT is the Internet of Useful Things - a term I have begun using to describe things that are using the collection, sharing, and analysis of data and networking to enable something really good and beneficial. The IoST is its evil twin and is occupied by things like toothbrushes, networked light bulbs, and tampons (seriously, just when I think we are at peak stupid, we outdo ourselves again…). I call it The Internet of Stupid Things and it is the prime example of the saying, "just because you can do something, doesn't mean you should". There is obviously an inherent value judgment that I am making with this statement, and in some respects, it is designed to be inciting. I want people to think about data; the way it's used, the way it's managed, and most importantly the way it gets created. As I have said before, data for the sake of data is not necessarily "good" data. The Big Data fallacy is that we have to have lots of data of every possible type in order to create real value, and the IoST is the byproduct of that fallacy.
The fact is that the IoT is here to stay, and as our capability to incorporate digital mechanisms and interfaces into the common and everyday objects of our lives increases the IoT will increase along with it. However, this is the heart of the issue with the IoT, and indeed "Big" Data, and many other things that happen; should we really network “all the things” just because we can?
I believe that the resounding answer is NO. There is no conceivable reason that my toaster needs an app. I do not want one app for my light bulbs, one for my switches, and one for my home-awareness system. Unfortunately, in the absence of standards and a common focus, that's exactly what is occurring, and it is the genesis of the IoST. The types of things that could really benefit from being digitized and connected like cars, traffic sensors and cameras, airplanes, and mass transit systems, take a backseat to toothbrushes and thermostats (although I want my fridge to count calories and order groceries for me, and my stove to alert me when something is done cooking - seriously, I do).
How the IoT Will Continue to Shape the World
The IoUT is slower to build and develop and the way it impacts us is not as "gee whiz" as the connected hair-dryer which has a marketable, shiny, and most importantly, consumer-driven "use". It impacts us in subtle ways that may not feel measurable - like changes in traffic patterns and the measure of rainfall and the reduction of flight delays. The most obvious example of IoT technology that is useful and who's time is long-since been needed to come is the connected, self-driving automobile. Audi, BMW, Tesla, the "Big 3" and many others are all working on some form of self-drive and frankly, it is about damn time. Unfortunately, we are the weak link in the chain because humans have a real problem with control and more importantly with letting go of it. Until we as a collective species get over this irrational fear of something doing things better than we can, then the types of advancement we need will continue to lag behind the kinds we definitely don't.
IoT is a critical technology that will continue to shape and drive the world around us for decades to come. I for one hope that shape is built around the IoUT and not the IouT. Now I need to find my phone so I can turn on the power strip and watch TV.
Thu 23 June 2016
[Process One] XMPP Radar Newsletter #11
Welcome to the 11th issue of our newsletter. You can subscribe to the XMPP Radar newsletter and receive it in your inbox at the end of each month. Here are the links we found interesting in May:
Push notifications are a cornerstone of every mobile app’s engagement and retention strategy, yet we know so little about them.
Apps that blast out push notifications are missing out on an opportunity to encourage engagement, with personalized notifications driving significantly higher results across the 1.5 billion messages analyzed for a new report from Leanplum.
The Economist magazine published recently an article titled “The Slack Generation”, highlighting the growing importance of messaging software in the workplace. The article pointed out three main workplace changes that have led to Slack’s rise.
Another feature that I believe can improve the user experience is the possibility of interact with your telephone system (Asterisk) directly from your IM client.
The Notifications API has been available for some browsers for a while now. Alex Castillo is bringing this powerful API to the Angular world in the form of a library, making it more accessible and reusable for developers.
Signal, which is developed by Moxie and Open Whisper Systems, is a tool for secure messaging between mobile devices. It has faced criticism since Signal is built on a centralised platform. The criticism was fueled even further by an idea that LibreSignal, an independent build of Signal, would not be able to federate and talk to the Signal servers.
[CozyCloud] Héberger un Cozy à la maison : Le matériel
Dans cet article, nous allons vous guider dans l’installation de votre serveur personnel, chez vous.
Vous avez franchi le pas de l’auto-hébergement : vous souhaitez enfin héberger vos données chez vous, sur un ordinateur que vous contrôlez. C’est parfait ! Nous allons vous aider à choisir votre matériel.
Au minimum, vous aurez besoin :
- d’un serveur personnel type Raspberry Pi 3 , 2, ou équivalent, d’un ordinateur de récup, ou bientôt d’un Pine64 2G.
- Son alimentation et souvent une carte SD ou micro SD.
- Un câble Ethernet pour le connecter à votre box (modem-routeur)
- Un accès internet
- Un boîtier de protection (optionnel)
Les ordinateurs tels que nous les connaissons sont munis d’un clavier et d’un écran, mais cela n’est absolument pas nécessaire. Aujourd’hui, vous pouvez trouver des mini-ordinateurs, tout à fait puissants, de la taille d’une carte de crédit. Ils coûtent de l’ordre de 30 à 50 euros.
Si vous êtes débutant, nous vous conseillons l’achat d’un Raspberry Pi 3. C’est le modèle le plus répandu, vous trouverez donc plus facilement de l’aide et des tutoriels, dans la langue de votre choix.
Il possède des caractéristiques techniques très correctes :
- 1 Go de ram
- 1 CPU à 4 cœurs CPU (un peu moins puissants que nos ordinateurs personnels)
- 1 port Ethernet 100Mbit
- 1 lecteur de carte microSD et plus encore !
Vous pouvez aussi nous contacter sur notre forum pour plus d’informations.
Une fois déballé, votre Raspberry Pi ou autre mini ordinateur n’est qu’un circuit imprimé avec des composants : il faut que vous ajoutiez les derniers éléments nécessaires à son démarrage.
Les mini ordinateurs ont souvent besoin d’une alimentation externe pour fonctionner. Ce sont des alimentations généralement compatibles avec les smartphones (5 Volts) avec une fiche micro-USB mâle.
Attention, néanmoins : les alimentations que nous possédons sont généralement très peu chères, mais de médiocre qualité. Nous vous conseillons de trouver une alimentation assez puissante (au minimum 1 Ampère, idéalement 2). Il est très important d’avoir une alimentation adaptée en tension (Volts) mais vous pouvez choisir une alimentation capable de délivrer plus de courant en cas de besoin. Cette remarque est d’autant plus importante si vous branchez des périphériques USB à votre mini ordinateur.
Un manque de courant peut conduire à un dysfonctionnement de votre ordinateur et est souvent difficilement détectable.
La carte mémoire :
De même que pour l’alimentation, la carte SD (rpi1) ou microSD (rpi2&3) qui va stocker le système d’exploitation doit être performante. Un manque de performance peut ralentir globalement tout l’ordinateur, la carte étant utilisée sans interruption. Les cartes sont classées par catégorie appelées Class. Il est recommandé d’utiliser au minimum une carte de Class 10 (10Mbit/s en écriture). Tous les bons revendeurs de mini ordinateur vous fourniront une carte de cette qualité. Nous vous conseillons de choisir une carte d’au moins 8Go.
Si vous souhaitez avoir plus d’informations sur les Class SD, vous pouvez consulter cette page
Si votre carte n’a pas été pré-remplie en usine avec un système d’exploitation, vous devez y copier un système d’exploitation vous-même.
Voici un guide très clair en anglais.
Le câble Ethernet :
Ici, pas de surprise : branchez un câble Ethernet classique entre votre box (modem-routeur) et votre mini-ordinateur. Cela lui permettra de se connecter à internet et à votre réseau local.
Une boîte de protection (optionnelle) :
Les mini ordinateurs sont souvent livrés sans boîte, et contiennent uniquement la carte électronique. Ajouter une boîte apporte une protection à la fois contre les chocs qui pourraient détruire des composants mais aussi contre la poussière. Au bout d’un moment, cette dernière peut en effet empêcher l’évacuation de chaleur.
De fait, nous vous conseillons d’investir dans une boîte. Elle ne vaudra bien souvent que quelques euros, et vous pouvez même choisir de la fabriquer vous-même. Vous trouverez aisément des patrons (découpe bois, plastique au cutter) ou bien des modèles d’impression 3D partout sur internet, comme celui-ci. Et vous verrez que rien ne vaut un Raspberry dans votre boîte faite maison !
Vous pouvez maintenant brancher l’alimentation : les LED vont commencer à clignoter. Félicitations, et merci de contribuer à la décentralisation d’internet !
Il s’agit maintenant s’y connecter et le configurer. Nous aborderons ces sujets lors d’un futur article sur ce blog !
*Images : Cozy Cloud, Creative Commons and Wikimedia.org, Nico Kaiser, Creative Commons Attribution 2.0 Generic
[CozyCloud] Self-hosting your Cozy at home: your equipment
This article aims at guiding you through the installation of your personal server, at home.
You decided to self-host your data: you want to run your own server at home, on a computer you control. That’s great! Let’s chose your equipment.
You will need at least:
- A personal server, like Raspberry Pi 3, 2, or equivalent, an old computer, or even a 2GB Pine64 (coming soon!)
- A power supply and a SD/microSD card
- An Ethernet cable to connect your server to your DSL modem
- Internet access
- An optionl case for the Raspberry
We always picture computers with a keyboard and a screen, but they actually don’t need all this stuff to run properly. Today, you can find tiny credit-card-sized computers, but powerful enough for only €30-50 ($35-55).
If you’ve just discovered self-hosting, we advise you to buy a Raspberry Pi 3. This is the most popular model, and if you have issues, you will easily find help and tutorials in your language.
Raspberry Pi 3 has pretty good specs:
- 1 Go RAM
- 4 CPUs (a bit less powerful than a PC)
- 100Mbit Ethernet port
- 1 microSD card reader (and much more!)
If you are willing to get one, we’ve bought a couple from Farnell and we had a good experience.
You can also ask your questions on our forum if you need more information.
When you receive your Raspberry Pi (or any other tiny computer), you will only find a circuit board with its components: you have to plug your power supply, memory card, Ethernet cable before turning your device on.
The power supply:
Mini computers often need an external power supply to work. These power supplies are often smartphone compatibles (5 Volts) with a micro USB plugin.
Be careful nonetheless: power supplies we have in our home are often cheap, but with a mediocre quality. We recommend you to find a power supply powerful enough (at least 1 Amp, 2 would be even better). It is really important to have a power supply with the right voltage (Volts) but you can choose a power supply with more electric current if needed. This is especially true if you plug USB devices to your mini computer.
A lack of electric current can cause a dysfunction of you computer and it is difficult to detect it.
The memory card:
As for the power supply, the SD card (rpi1) or microSD (rpi2&3) that will store the operating system must be efficient. A lack of performance can slow the entire computer, the card being used without interruption. Cards are sorted by categories called Classes. It is recommended to use a Class 10 card at the bare minimum (Writing at 10Mbits/s). Any reliable mini-computers retailer will provide you with a card of this quality. We recommend to choose at least a 8Go card.
The Ethernet cable:
Here, no surprises: plug any Ethernet cable between you box (routeur-modem) and your minicomputer. This will allow your Raspberry Pi to connect to the Internet and to your local network.
Here is an optional protection box:
Minicomputers are often shipped without any box, and only include the circuit board. Adding a box gives an extra protection against shocks that could destroy components, but also against dust. Dust can lead to an excessive heat after some time. For all these reasons, we advise you to buy a box. It will only cost a few euros and you can even build it yourself! You will easily find templates (for wood or plastic) or even 3D printing files. And you will see, nothing is worth a Raspberry in a box you made yourself!
You can now plug the power: LEDs will start to blink. Congratulations, and thank you to contribute to a more decentralized Internet! Now is the time to log in your Raspberry and configure it. We will talk about this in a future article in this blog!
*Pictures : Cozy Cloud, Creative Commons and Wikimedia.org, Nico Kaiser, Creative Commons Attribution 2.0 Generic
[GANDI] European Football Cup Promo
Football fans (or not), see our promo that follows the progress of your favourite national teams.
OpenDataSoft is thrilled to kick the Summer off with an exciting announcement: we are proud to have been chosen as a winner in the 2016 Amazon Web Services City on a Cloud Innovation Challenge, solidifying our status as a global Open Data innovator!
The Global City on a Cloud Innovation Challenge is a program that recognizes developers or local and regional governments that are innovating for the benefit of citizens, using the AWS Cloud. Awards are given across three categories: Partners in Innovation, Dream Big, and Best Practices. OpenDataSoft was chosen for the Partners in Innovation Award for providing an application that solves local government challenges.
A panel of worldwide experts selected 15 winners from a pool of 43 finalists. The awards are based on the solution’s impact, the long-term likelihood of success, potential to help other local governments solve similar challenges, and the implementation of AWS services. We would like to offer our congratulations to our fellow City on a Cloud Challenge winners!
We’ve been busy these last few months…
This new global recognition wraps up an action-packed spring for OpenDataSoft. Recent news includes:
- Recognition as an IDC Innovator in the Smart City Open Data Platforms Market 2016
- Team growth to 30 members in both Europe and the United States
- Participation in White House Round Table events and other global conferences
“Six short months ago in December, we had the pleasure of celebrating OpenDataSoft’s fourth birthday alongside many of our customers in France. In this short time, we’re proud we can look at a map of the world and locate users in 10 countries in all corners of the globe; they’re as far away as Australia, the U.S., Canada and Saudi Arabia, and closer to home in countries such as Sweden and Portugal. Through our close collaboration with our users, our team has brought the solution to where it is today, helping to propel this phenomenal growth and positioning,” said Jean-Marc Lazard, CEO and Co-Founder of OpenDataSoft.
Leading up to the AWS Public Sector Summit in Washington D.C. on June 21, when the announcement was made, OpenDataSoft was featured on the stage at the Amazon Web Services Summit in Paris. During a keynote speech delivered by Awa Ndiaye, Open Innovation Project Manager for the City of Paris, the City of Paris revealed its strong relationship with both OpenDataSoft and Amazon. Ndiaye noted that the City currently stores over 48 million records on its Open Data platform, powered by OpenDataSoft, and will be pursuing its ambitious Smart City program with both OpenDataSoft and Amazon Web Services.
— Fanny Goldschmidt (@foldschmidt) 31 mai 2016
The Frenchies are Coming!
In only four short years, OpenDataSoft is able to confirm its position as a global leader as a Smart City and Open Data innovator thanks to these recent and numerous accolades. Over 70 cities, government administrations, transportation providers, energy companies, and many more actors across the globe are now using the platform to spur open, transparent, and innovative communities and companies. With this momentum, we are ready to announce plans to even further solidify our position as an international player.
One of our co-founders, Franck Carassus, will be moving to the United States and setting up a United States Headquarters for OpenDataSoft in Boston, Massachusetts. With two team members currently based in North Carolina, OpenDataSoft will begin growing our team and expanding our presence outside of Europe.
This will be a great new adventure for OpenDataSoft, and we will have plenty of more details to come! If you would like to stay up to date on our news, please go ahead and sign up for our newsletter!
The state of us Open Data Legislation
The United States Congress is currently working on new Open Data legislation at the Federal level. Click below to learn more about our upcoming webinar, featuring Jason Hare, Daniel Castro from the Center for Data Innovation, and Mark Headd, talking all about the subject.
[Adacore] New check_all mode for GNATprove
[Adacore] Improved exception propagation on cert
[GTLL] [ACTU MEMBRE] - OpenDataSoft est lauréat du Amazon Web Services City on a Cloud Innovation Challenge 2016
Un grand bravo à notre membre OpenDataSoft, éditeur de la première plate-forme de valorisation de données structurées et d’APIs, qui remporte le concours international Global City on a Cloud Innovation Challenge 2016 organisé par Amazon Web Services (AWS) !
L'événement met à l’honneur les développeurs et les collectivités qui œuvrent à améliorer la vie des citoyens grâce aux innovations technologiques via le Cloud AWS.
Un panel d'experts venus du monde entier a décerné le prix Partners in Innovation Award, à OpenDataSoft, qui récompense les solutions les plus innovantes qui aident les collectivités au quotidien.
OpenDataSoft s'est distingué sur les critères d'évaluation suivants :
- l'impact de la solution,
- la probabilité de succès sur le long terme,
- l'adaptabilité à des défis similaires auprès d'autres collectivités,
- et la mise en œuvre des services AWS.
"C'est avec une grande fierté, que nous avons reçu cette distinction qui récompense la qualité et le fort potentiel de notre technologie auprès, notamment, des collectivités partout dans le monde. Aujourd'hui, à l'approche de notre cinquième anniversaire, nous avons le regard tourné vers les États-Unis" commente Jean-Marc Lazard, CEO and Co-Fondateur d’OpenDataSoft.
Rejoignez OpenDataSoft sur Twitter @opendatasoft
Wed 22 June 2016
Most of the media and e-commerce Chief Data Officers I talk to on a daily basis dream of an active collaboration between their Marketing and Data Science teams. To realize this goal, we suggest setting a use case that promotes cooperation between colleagues with different skill-sets. One of our favorite cross-team approaches is to practice a use case involving Churn Analytics.
According to Wikipedia, the definition of churn is:
"Churn rate (sometimes called attrition rate), in its broadest sense, is a measure of the number of individuals or items moving out of a collective group over a specific period of time."
Keep Your Fishes Home
Churn Detection Depends on the Subscription Model
The first step in a churn-based data science project is to define the model used by an organization; typically, there are two varieties: subscription and non-subscription. Some examples of subscription types can be found in our customers:
- Subscription Model
- Non-subscription Model
Determining whether or a not a customer will become a churner (i.e., no longer remain a customer) is fairly straightforward in subscription models, but a bit more challenging in non-subscription models. In subscription models, a customer churns when they request cancellation of their subscription. In non-subscription models, however, you need to analyze your customer’s behavioral tendencies in order to identify potential churn (e.g., the amount of time since he last used the company’s services/products). The goal is to then determine the specific point when your customer will no longer use your services or products.
Dealing with Churn over Multiple Time Spans
Churn projects are typically launched when the customer acquisition rate diminishes. For most companies, the customer acquisition cost (cost of acquiring a new customer) is higher than the cost of retaining an existing customer… sometimes by as much as 15 times more expensive (Winning New Business in Construction by Terry Gillen, 2005). Therefore, the challenge of implementing a successful churn project is to increase customer loyalty and, consequently, increase company revenue.
How Can Different Modelling Approaches Solve Churn Issues?
There are two complementary modeling approaches used to predict churn:
- Machine Learning Model (short term action): Develop a Machine Learning model to analyze performances that will enable short-term actions. Based on the outcome of this analysis, our clients are able to undertake one-shot actions to reduce churn;
- Analytical Model (long term study): Develop a model to understand the reasons causing the churn. This deeper knowledge enables our clients to attack the root of the problem and to understand how to reduce churn.
In both cases, it is crucial to connect your models to marketing-driven actions in order to attain churn reduction. Some examples of short and long-term actions include:
- Short-term actions:
- Special offers (e.g., calls, e-mails, push notifications, free in-game money, discount coupons, etc.);
- Feedback loop to control efficiency;
- Model the probability of churn due to the offer.
- Long-term actions:
- Purchase funnel optimization;
- Analyze whether or not the offer is correctly adapted to the customer base.
Only a combined approach of mixing short-term actions (in order to retain potential churners) with longer-term approaches will have an effective and sustainable impact on churn reduction.
Using Data Science to Model Churn
Churn analytics projects can be addressed by Data Science and Marketing teams thanks to Machine Learning modeling (classification) with a defined target. The target is known in subscription business models while it needs to be defined in non-subscription scenarios.
Step 1: Create a Churner Profile and Identify Churner Behavior
Segmentation: Segment your customers based on their behavior and address the question, “Which customers do we care about?” Only the best? The most valuable? Regardless of the answer, a churn reduction campaign should be targeted toward a well-defined customer segment;
Compare to Control Population: By understanding the extremes of churners, new customer classes can be created and refined. On one extreme there are customers who interacted with the product at least once, but no longer visited afterwards. The other extreme includes customers who make frequent uses or purchases and are heavily engaged with the product. In this context, the definition of a “new customer” can be formulated along with an understanding of customer groupings;
What Makes your Churner Different?: Data collected from the above analysis, when subjected to Machine Learning modelling, enables your company to discover differential patterns among churners and identify what makes your churners different from others.
Step 2: Implement a Churn Scoring Mechanism
Implementing a churn scoring mechanism relies on a pair of processes:
- Find Relevant Features: Customer features, such as social information and behavior-based actions, are used to paint a picture of who your customers are. Start the churn scoring process by finding the customer features that are the most relevant to your churn calculations;
- Compute a Churn Score: Churn score computation combines all relevant customer features to determine exactly how likely specific customers are to abandon your product/service. At this point, machine learning technology takes over — predictive algorithms are fed into Data Science Studio and the best one is selected. It is then deployed to calculate a churn score.
Yes, the way you do marketing is about to change.
Implementing the Churn Scoring Mechanism
The process of churner identification and behavioral analysis involves expertise from both Data Scientists and Marketing Specialists: one party understands the customers whilst the other can measure & analyze behavior. At the end of this process, it is time to apply the information learned to the company’s loyalty program. The output of a churn project is a dataset (Excel or CSV file, or a table stored in your customer database) that contains the customer ID and an associated churn score. This churn score indicates the probability of the customer abandoning your product or service. With this score, the Data Science and Marketing teams can build business rules that define customer segments.
Churn Scoring Applied
Churn scoring is used to assign a score to customers that conveys the potential loyalty of the customer. Churn scores enable Data Science and Marketing to build business rules together in order to define customer segments. For example, a churn scoring mechanism would enable your company to creatively segment customers — sample types and potential actions include:
- Churners: Sending them a special offer via e-mail;
- Loyal Customers: Take no action;
- Potential Churners (we want to keep): Sending them a special offer via e-mail;
- Customer Ambivalence (unsure whether to keep or not): Sending them a Greetings e-mail without an offer.
In order to achieve optimal results, the actions need to be customized based on your business requirements and your knowledge of customer behavior & expectations. Actual deployment of your churn scoring methods can be done by feeding e-mail marketing automation tools and push engines for in-app notifications. Want to fight against customer churn, but don’t know how to begin your project? Have a look at our Whitepaper “The Modern Marketer’s guide” to find out how to deploy your marketing project.