What is the strategy to face the diversity of effectiveness measurements?

Effectiveness measurements are growing everyday. Not a day passes without having a new KPI emerge within companies, where each one of them is considered complementary or even more relevant than the other ones. So what kind of KPIs are relevant to measure the effectiveness of actions taken? Because KPIs are diverse, they must be considered as a whole, as the global vision of the company - its relationship and business story. In real time, KPIs should meet and if possible exceed expectations for simplicity. But choosing the right KPIs is essential as well as the correlation of those linked by the same goal. These KPIs together are critical for decision-making, we call them “super KPIs” as they carry the power to change companies. Those super KPIs are able to evaluate the relevancy of actions taken for new organizational modes in companies. They are, by definition, scalable and must be mastered in their analysis and representation.

Beyond the careful choice of KPIs and their assembling, their representation into dashboards is a stake by itself. In order to stay loyal to their power and diversity, dashboards have to be dynamic tools, able to adapt to all changes, evolutions or twists, it’s a must today. The simplicity and agility of the dashboards are key for efficient decision-making, they help organizations to rapidly grow in a constantly changing environment, by making it readable and understandable for everyone.

Good KPIs are those making you ask yourself the right questions, they are often easy to understand for the organization at all levels and allow you to realize what is necessary to positively impact results. For example, the NPS (Net Promoter Score) is often used to measure customer’s experience. Even if it’s a simple management KPI, it can help associates, who are directly in contact with customers, in changing their behaviors to improve the customer’s experience.

Another example would be to consolidate some KPIs to obtain an aggregate used as a baseline, a super KPI, like the time consumers spend online with the brand by adding the time spent on digital assets such as websites, videos, social media, or the advocacy (by collecting all positive content posted on social media platforms).

A good KPI is the one that is aligned with individual and collective performances and goals. By reaching goals, individuals and teams will feel valued and will be rewarded.

Captain Dash’s approach is based on this simplicity and agility. Our mission is to make the change of organizations a lever for performance. Contact us to know how we can help you!

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Written by: Bruno Walther, CEO & Co-founder of Captain Dash

A customer vision yes, with the right dashboard, it’s better!

A customer relationship requires to not only follow your client, but to also interact in a smarter way with him, this is undeniable. Once that promise is met, a clear and useful vision needs to be made in order to understand the customer’s journey and its behavior with the brand in the long run. Then, the customer dashboard needs to simplify the customer experience by increasing the content and channels relevancy. That’s the only way to make this reinvented customer relationship a business tool.

A simple dashboard, not a simplistic one Simplicity is duty, it makes complex things easy to read and clear. It’s the same with a dashboard, its simplicity must serve customer data to bring the dashboard back to one of its essential functions: to inform.

An elegant dashboard, not a flashy one Elegance is respect, it helps make a difficult thing to manage more accessible. It’s the same idea with a dashboard, its elegance has to motivate the user to use it and optimize one of its purposes : to analyze.

A useful dashboard, not a static one Usefulness is action, it pushes to appreciate things and use them on the long run. A dashboard must be useful not only because it is its main function, but to also generate movement and decision making regarding actions to take related to customers. Its ultimate goal: to bring to action.

At Captain Dash, we are motivated by action, the one that anticipates and helps organizations to leverage their customer relationships into real business opportunities coming from satisfaction and loyalty. Our dashboards are made for that, they help to inform, analyze and bring to action. In one word, they help organizations to succeed!

Stay updated and learn more on Captain Dash, follow us on Twitter or subscribe to our blog.

Written by: Bertrand Verret, Chief Revenue Officer at Captain Dash

Une vision client oui, avec le dashboard qui va bien, c’est mieux !

La relation client nécessite, c’est irrévocable, de savoir suivre son client mais également d’interagir intelligemment avec lui. Au delà du déclaratif plein de promesses, c’est une vision claire et utile du client qui se dessine, pour appréhender sur la durée son parcours et ses comportements avec la marque. Le dashboard client doit alors aider à simplifier l’expérience client en permettant d’augmenter la pertinence des messages, contenus et canaux. C’est le seul moyen de faire de cette relation client revisitée un moyen de développement business.

Un dashboard simple mais pas simpliste La simplicité c’est la rigueur, elle permet de rendre lisible et compréhensible des choses complexes ou qui semblent l’être. C’est le même postulat pour le dashboard, sa simplicité doit servir la donnée client pour lui rendre une de ses fonctions essentielles : informer.

Un dashboard élégant mais pas “tape à l’oeil” L’élégance c’est le respect, elle aide à rendre une tâche ou un objet, probablement pas évidents, plus accessibles. Il en va de même pour le dashboard, son élégance doit motiver l’accès aux informations client donc l’usage et ainsi optimiser une de ses raisons d’être : analyser.

Un dashboard utile mais pas figé L’utilité c’est l’action, elle pousse à apprécier les choses et à les utiliser sur la durée. Un dashboard se doit d’être utile non seulement parce qu’il doit être fait pour cela mais aussi parce qu’il doit générer le mouvement et la prise de décision sur les actions client à mener. C’est son objectif ultime : agir.

Chez Captain Dash, nous sommes motivés par l’action, celle qui anticipe et qui permet à l’entreprise de faire de sa relation client un vrai levier business issu de la satisfaction et de la fidélisation. Nos dashboards sont faits pour cela, ils permettent d’informer, d’analyser et d’agir, bref de réussir !

En savoir plus sur Captain Dash, suivez-nous sur Twitter ou abonnez-vous à notre blog.

De : Bertrand Verret, Leader des ventes et du revenu au sein de Captain Dash

Data dashboards and microservices: same battle!

With microservices, complex applications are made of tiny independent programs where each one of them has a specific function and communicate with each other in a very modular and agile way. Just as with dashboards, all business approaches should have simple data where each data would have a specific role and be organized in a very agile way to help the stakes of the company.

Why is this comparison?

Because today, simplicity, modularity and precision are major stakes. Microservices are able to deliver on that and a new vision of dashboards must emerge.

A dashboard should be able to integrate any data. It should offer a simple and efficient view over the business, in real time and in a dynamic way. Its mission should be precise and its use should be easy to help decisions based on what the dashboard is showing. In other words, a dashboard should have the same philosophy microservices have!

At Captain Dash, we apply this philosophy to the dashboards we build for our clients. Our dashboards are simple, ergonomic and dedicated to offer an efficient vision of our clients’ business. Powerful, our dashboards transform the way our clients manage their business in a more efficient and dynamic way.

And of course, we use microservices to build our dashboards…

Stay updated and learn more on Captain Dash, follow us on Twitter or subscribe to our blog.

Written by: Bruno Walther, CEO & Co-founder of Captain Dash

Data dashboards et microservices : même combat !

Avec les microservices, des applications complexes sont composées de petits programmes indépendants ayant chacun une fonction unique, et communiquant entre eux dans une approche hautement modulaire et agile. De même, avec les dashboards, toutes les approches business devraient être présentées par des données simples, mono tâche et pouvant être organisées de manière agile et modulaire pour servir les enjeux d’une même organisation.

Alors pourquoi ce parallèle ?

Tout simplement parce qu’aujourd’hui la simplicité, la modularité et la précision sont des enjeux majeurs, les microservices y répondent dans leur domaine et une nouvelle vision des dashboards se doit d’exister dans le sien.

Un dashboard doit pouvoir intégrer des données quelles que soient leur nature. Il doit permettre une vision simplifiée et efficace du business, en temps réel et de manière dynamique. Sa mission doit être précise et son usage clair pour aider à la décision. Bref, il doit avoir la même philosophie que les microservices !

Chez Captain Dash, nous appliquons cette philosophie aux dashboards que nous créons pour nos clients. Ils sont simples, ergonomiques et dédiés à une vision performante de leur business. Puissants, ils savent rendre le pilotage d’activité efficace et dynamique.

Et nous utilisons les microservices pour bâtir ces dashboards …

En savoir plus sur Captain Dash, suivez-nous sur Twitter ou abonnez-vous à notre blog.

De : Bruno Walther, Directeur Général et Co-fondateur de Captain Dash

Le dashboard, véritable outil de pilotage financier

Face à un changement radical des modèles business, le pilotage financier d’une organisation devient de plus en plus compliqué, alors devons-nous pour autant complexifier les outils dédiés à ce pilotage ? Bizarrement non !

L’évidence c’est que finalement les indicateurs à mettre en oeuvre se doivent d’être simples et pertinents donc plus efficaces. Il faut être capable de donner une vision dynamique de la santé de son business, en temps réel et le plus près du terrain, car seule cette approche permet les prises de décisions rapides nécessaires aujourd’hui dans nos métiers.

Alors comment procéder ?

Tout d’abord, définir des KPIs qui soient basiques et parlants, ne pas chercher à charger la barque mais miser sur l’utilité et l’usage pour mieux évaluer et valoriser les actions. Bâtir ensuite des dashboards souples, simples et modulables permettant d’avoir une vision financière précise, parlante et facile à analyser, il en va de l’efficacité des décisions.

Vue de l’esprit ?

Non ! Chez Captain Dash nous sommes guidés par une philosophie affirmée, faire compliqué ne signifie pas être efficace alors faisons simple, beau et utile pour mieux réussir car seules l’excellence opérationnelle et l’agilité doivent nous “driver”.

En savoir plus sur Captain Dash, suivez-nous sur Twitter ou abonnez-vous à notre blog.

De : Bertrand Verret, Leader des ventes et du revenu au sein de Captain Dash

The dashboard, a real tool for finance

As we are facing drastic changes in business models, driving the finances of an organization can become more and more complex. But does it means that we have to make complex tools to manage it? Surprisingly… Not!

The key is to choose simple and relevant indicators to be more efficient. This is critical in order to give a dynamic vision of your business, in real time and in all transparency. Only this approach will enable quick and efficient decisions.

So how to do that?

First, we need to define basic and meaningful KPIs rather than trying to fill up your dashboards with tons of KPIs. We must think of the usefulness and the usage of each KPI to better evaluate and increase the value from your actions. Then, we have to build flexible and simple dashboards for a precise financial vision that is easy to understand and analyze. We can then talk about efficient decision-making.

Wishfull thinking?

No! At Captain Dash, we are guided by a strong philosophy: simplicity is a key to be efficient. We believe in simplicity, beauty and usefulness to better succeed. Only operational excellence, efficient and quick decision-making should drive us all.

Stay updated and learn more on Captain Dash, follow us on Twitter or subscribe to our blog.

Written by: Bertrand Verret, Chief Revenue Officer at Captain Dash

Data is the new paradigm of marketing

The homo numericus produces in two days what the homo sapiens produced from his birth to the 50’s. This is a complete change of world. 90% of the data produced by humans and machines is not exploited!

50 years ago, we invented the concept of branding. The ability for brands to create a universe where the consumer does no longer buy the functional value of the product but an emotional value, a unique experience to get him through his daily life. This is the way to create an imaginary value that overruns the functional value of a product and this is the true definition of marketing.

Today, data allows us to go further, which is the everyday life of brands such as Tesla, Apple or Runkeeper - to create a unique experience of consumption with data. Each contact made with the brand creates data that is the key for marketers to offer a new experience and engage their customers. Collecting and visualizing the good data generated will help companies to develop and maintain new relationships with individuals, based on a mutual exchange.

Data est definitely the new paradigm of marketing.

Stay updated and learn more on Captain Dash, follow us on Twitter or subscribe to our blog.

Written by: Bruno Walther, Co-founder & CEO of Captain Dash

Sexy Data = Smart Data + Beautiful Data

At Captain Dash we believe that data is sexy. Data is smart enough to tell a story and beautiful enough to delight your senses. Or at least it can be all those things. Smart Data

The world is already overwhelmed with data and the number of devices producing data outnumbers humans. This number is all set to explode by 2020.

What this means is that data is not going away and it IS the future.

As data has increased everyone has jumped on to the bandwagon of creating metrics. Everyone wants to know every possible thing that data can tell him or her.

As a generation we have become pros at adding performance metrics to as many things as possible – our health, our businesses, dating sites, even our social interactions!

But these metrics are just numbers again without the right context and the right vision. This is where story telling comes in. All the data that you or your business generate can tell a fascinating story when the right KPIs are applied to this data along with supporting facts, understandable charts and finally correlation.

Beautiful Data

Humans are visual beings. Not only do we judge people and things based on how they look but we digest information in matters of seconds when represented visually as opposed to the minutes or hours it would take to read it or listen to it. On top of that it is easier for us to retain a picture over a book.

So, of course visualizing data is key. But visualizations needn’t be boring pie charts. Visualizations should be harmonious, sleek and impactful!

They should make the viewer stop and spend time on them.

Sexy data

When you combine these two elements, the correct data with beautiful visual representations of it’s story, you have on your hands what we call sexy data.

This is the kind of data that is intuitive, hits the mark and is appealing to look at.

Going forward, the success of data depends entirely on turning the vastness and complexity of data into something simple and intuitive enough to attract its viewers.

Hence, how sexy your data is depends on how smart and beautiful you make it!

Written By: Meghna Verma

Fa(s)t Data

Chez Captain Dash, nous rencontrons différents types de projets autour de l'information. Certains projets sont clairement orientés Big Data. Cependant la vaste majorité des projets ne concerne pas la Big Data, mais plutôt… la Fast Data.

Les clients ne veulent pas plus d'informations. Ils la veulent plus rapidement, plus simplement et à n'importe quel moment. Le décideur, la personne en charge de l'opérationnel, a besoin de sa tablette, car il est amené à prendre plusieurs décisions chaque jour. Je suis dans un taxi en route pour un rendez-vous client et je dois vérifier une intuition. Il me suffit de consulter les chiffres de ventes croisés avec l'investissement en publicité. Il est 21h, demain aura lieu la réunion de pilotage de l'équipe marketing. Est-ce que je vérifier que l'audience du site web et le référencement sont corrects ?

L'entreprise devient accro à l'information, et c'est une bonne chose. Les décideurs souhaitent d'abord de la remontée d'informations, ensuite pas trop de nouveaux soucis, et enfin, très peu veulent voir les problématiques des projets Big Data.

Nous aimons la Big Data, mais nous aimons encore plus l'idée de la Fast Data. Simple, orientée métier, utile et surtout… intelligente.

C'est là l'un de nos secrets : tout le monde n'est pas un Data Scientist.

L'expertise vient de ce que l'on maîtrise le mieux, à force de le faire chaque jour. Or chez Captain Dash, nous avons observé que nos clients sont meilleurs que les algorithmes en général. Et c'est tant mieux. L'exercice et le métier de Captain Dash n'est pas de prendre une décision pour vous. C'est plutôt d'initialiser rapidement une réflexion, en vous montrant des indicateurs visuels.

Fast Data Captain Dash

Cette philosophie a un impact sur nos développements. Nous étions à nos débuts sur le chemin d'Apache Hadoop, mais nous sommes revenus à des solutions plus simples. D'abord le choix des tablettes (iOS et Android) car nos utilisateurs sont nomades. Ensuite le choix de développer une librairie d'affichage très puissante, qui écrase les concurrents. Enfin par un choix d'architecture autour de l'idée de micro-services avec Scala, Akka et Play2. Nous avons tué la complexité pour aller vers des systèmes simples, robustes et faciles à mettre en oeuvre.

Captain Dash a fait le choix d'une solution de type SaaS et reste aujourd'hui sur cette approche. Les niveaux de sécurité d'Amazon Web Services vont au delà de la plus-part des niveaux actuels des DSI. Lorsque la confidentialité est abordée, nous préférons nous appuyer sur le leader mondial, qui présente un ensemble de certifications reconnues, plutôt que sur un système propriétaire. Quant à l'approche économique, elle convient à nos clients. Ceci permet d'offrir une solution compétitive, focalisée sur la restitution et le chargement des données, sans avoir l'approche d'un éditeur classique.

"Rien ne se perd, rien ne se crée, tout se transforme" est une citation d'Antoine Lavoisier qui s'applique plutôt bien à l'idée de faire évoluer un logiciel existant dans votre système d'information. Lorsque nous travaillons avec nos clients, nous sommes à l'écoute. Ceci passe par un travail et un accompagnement de la part de nos chefs de projets. Notre conduite de projet permet de prendre l'existant, et de l'intégrer vers nos services, sans remettre en question les choix technologiques de nos clients.

Notre vision technique repose sur un mot : simplicité. Vous n'avez pas à "apprendre à utiliser un logiciel" ou à demander à un consultant de vous créer un tableau de bord. Votre donnée vous appartient, il existe des solutions simples, et nous sommes là pour y répondre.

Big Data c'est bien, Fast Data c'est mieux.


Written By: Nicolas Martignole Nicolas Martignole is the Lead Developer at Captaindash. He was previously the Lead Architect at Zaptravel. He’s also the creator and co-organizer of Devoxx France, one of the biggest conferences for Java and web developers in Paris.  You can reach him on Twitter on @nmartignole.

Fa(s)t Data (English)

At Captain Dash we encounter different types of projects around information. Some projects are clearly Big Data oriented. However, the vast majority of projects are not about Big Data, but ... Fast Data.

Clients do not want more information. They want information quickly, simply and available any time. The decision-maker has need of it on his tablet, as the data is required to make many decisions every day. I'm en route for an appointment and have to check a hunch. I need only refer to the sales figures crossed with investment in advertising. It is 21h; tomorrow I will be the steering meeting for the marketing team. Can I check if the traffic of the website and the SEO are correctly aligned?

The company becomes addicted to information, and that's a good thing. Policymakers want first, the information feedback, then, some want to see the new problems, and finally, very few want to see the problems of Big Data projects.

We like Big Data, but we love still more the idea of Fast Data. Simple, business oriented, helpful and above all ... smart.

And here is the secret: not everybody is necessarily a Data Scientist.

People are experts in the spheres they work in on a daily basis. At Captain dash, we have observed that our clients are smarter than any algorithm in general. And that's good. The job of Captain Dash is not to take decisions for you. Rather, we work by using visual indicators to aid you in taking fast, informed decisions.

Fast Data Captain Dash

This philosophy has had a profound impact on our developments. When we started, like everyone else, our intention was to walk down the Apache Hadoop path. But, we returned to simpler solutions. Our first weapon of choice was tablets (iOS and Android) because our clients are nomads. The second, was to create a very powerful display library, which crushes the competition. Finally, a choice of architecture around the idea of micro-services with Scala, Akka and Play2. We killed the complexity to go to simple systems, robust and easy to implement.

CaptainDash has chosen a SaaS solution and remains loyal to this approach. Security levels of Amazon Web Services go beyond the most shares of current levels of DSI. Where confidentiality is concerned, we prefer to rely on a global leader, with a set of recognized certifications, to a proprietary system. As for the economic angle, it is customizable based on our customers’ needs. This approach offers a competitive solution, focused on the restitution and uploading of data, over that of a traditional publisher.

"Nothing is lost, nothing is created, everything is transformed" is a quote from Antoine Lavoisier that applies pretty well to the idea of upgrading existing software in your information system. When we work with our customers, we listen. This requires an involvement of and guidance from our project managers. Our project management methodology allows us to take the existing, and integrate it into our services, without questioning the technological choices of our customers.

Our technical vision can be expressed in one word: simplicity. You do not have to "learn to use software" or ask a consultant to create a dashboard. Your data belongs to you, there are simple solutions, and we are here to answer them.

Big Data is good, Fast Data is better.


Written By: Nicolas Martignole Nicolas Martignole is the Lead Developer at Captaindash. He was previously the Lead Architect at Zaptravel. He’s also the creator and co-organizer of Devoxx France, one of the biggest conferences for Java and web developers in Paris.  You can reach him on Twitter on @nmartignole.

Data and Objectivism

Note: This article is written by Bruno Walther and all the viewpoints expressed below are his personal thoughts. They do not represent Captain Dash.

Could Ayn Rand be the mother of data revolution?

Many think that the emergence of data is a technical phenomenon.

I do not think so!

Data is not politically neutral. It is the result of a vision arising from the metaphysical, the epistemological and an ethical world.

The Epistemological Vision: The Reason

Think of the prism of the data, its base is its reasoning on objective and reasoned facts. This suggests that there is an objective reality. This says that consciousness is the ability to perceive what exists and that knowledge is gained by releasing anti-concepts that are myths, beliefs or emotional values only. This denies the existence of knowledge "a priori" and does not believe there is "truths by virtue of meaning."

Metaphysical Vision : Metaphysical Reality

Think in terms of what is given to know that reality exists independently of its observation or its conscience. It is there for whoever discovers it. Things are what they are, they have a specific character, their own identity. Awareness is a relational concept. Its purpose is not to create new objects, but to discover them and to establish links and hierarchies between them. The mind is not just created by reality but is a way to experience said reality.

The Ethical Vision: The Laws of Logic

The ethics of data consist of making the objective reason its only guide to action. The source of all the other virtues. This is the base of our beliefs, our goals, our values, our actions on a specific and rigorous rational process based on the laws of logic, not doctrines, aestheticising visions or social conventions. It is thought that irrationality is human engagement in self-destruction.

In short, data has a philosophical substrate. And it is no coincidence that almost all the data entrepreneurs, from Peter Thiel to Tay Kurzweil, share a common vision of the world.

This vision has a name: Objectivism formulated in the sense of Ayn Rand. In this sense data is the natural child of Ayn Rand or to be more precise grammar. It inherently carries its values, rules.

We are only at the beginning of this wave. But Uber to Airbnb to Netflix, data centric startups sweep the conventions. They design a society based on reality and reason as a principle of analysis, self-interest, and self-esteem with ethics and capitalism playing the role of the transformation tools.

Data is politically oriented. It is the catalyst for a radical transformation of the world towards a more rational society, made freer when capitalism becomes the beneficial system where the innovations brought by human creativity benefit all without causing losses to others.

And this is a formidably good news.


Written By: Bruno Walther Bruno Walther is the CEO & Co-Founder at Captain Dash.  You can reach him on Twitter @brunowalther .


Data et Objectivisme

Note: Cet article est écrit par Bruno Walther et tous les points de vue exprimés ci-dessous sont ses pensées personnelles. Ils ne représentent pas Captain Dash.

Ayn Rand est-elle la mère de la data revolution ?!

Beaucoup pensent que l'émergence de la data est un phénomène technique.

Je ne le pense pas.

La data n'est pas politiquement neutre. Elle est le fruit d'une vision métaphysique, épistémologique et éthique du monde.

La vision epistémologique : la raison

Penser au prisme de la data, c'est fonder son raisonnement sur des faits objectifs et raisonnés. C'est penser qu'il existe une réalité objective. C'est penser que la conscience est la faculté de percevoir ce qui existe et que l'on acquiert des connaissances en se libérant des anti-concepts que sont les mythes, les croyances ou les valeurs uniquement émotionnelles. C'est nier l'existence de la connaissance “à priori” et ne pas croire qu'il existe "des vérités par vertu de signification".

La vision métaphysique : la réalité métaphysique

Penser en matière de donnée c'est avoir la certitude que la réalité existe indépendamment de son observation ou de sa conscience. Elle est là pour celui qui la découvre. Les choses sont ce qu'elles sont, elle possèdent un caractère spécifique, une identité propre. Dès lors la conscience est un concept relationnel. Son objet n'est pas de créer des nouveaux objets mais de les découvrir et d'établir des liens et des hiérarchies entre eux. L'esprit ne créé par la réalité mais est un moyen de découvrir la réalité.

La vision éthique : les lois de la logique

L'éthique de la data consiste à faire de la raison objective son seul guide d'action. La source de toute les autres vertus. C'est baser nos convictions, nos buts, nos valeurs, nos actions sur un processus rationnel précis et rigoureux fondé sur les lois de la logique et non sur des doctrines, des visions esthétisantes ou des conventions sociales. C'est penser que l'irrationalité est pour l'Homme un engagement dans l'autodestruction.

Pour faire court, la data a un substrat philosophique. Et ce n'est pas un hasard si la quasi totalité des data entrepreneurs, de Peter Thiel à Tay Kurzweil, partagent une vision commune du monde.

Et cette vision porte un nom : l'Objectvisme au sens où Ayn Rand l’a formulé. Dans ce sens la data est l'enfant naturel d'Ayn Rand ou pour être plus précis sa grammaire. Elle porte intrinsèquement ses valeurs, ses règles.

Nous ne sommes qu'au début de cette vague. Mais de Uber à Airbnb en passant par Netflix, les startups data centric balayent les conventions. Elles dessinent une société basée sur la réalité et la raison comme principe d'analyse, l'intérêt individuel et l'estime de soi comme éthique et le capitalisme comme outil de transformation.

La data est politiquement orientée. C'est le catalyseur d'une transformation radicale du monde vers une société plus rationnelle, plus libre où le capitalisme redevient ce système bénéfique où les innovations portées par la créativité humaine profitent à tous sans causer de pertes aux autres.

Et c'est une formidable bonne nouvelle.


Par : Bruno Walther  Bruno Walther, CEO & Co-Fondateur chez Captain Dash.  Vous pouvez le trouver sur Twitter @brunowalther .


Data Lakes - The Way Forward


Data lakes have been in big data news lately. From being marked down as a terrible idea to being THE big data trend to watch out for in 2015, everyone has something to say about them. Enterprises, especially large ones, are wondering whether or not to make the switch to this de-structured, easily accessible form of data storage. Consultants stand divided citing pros and cons while the fence sitters wait with baited breath to see which side the coin will eventually fall on.

So, what are data lakes exactly? Imagine a repository where all the silos have been broken down to create a free flow, easily accessible environment for data to exist in and which is scalable to meet the company’s needs in the future as and when said data is needed. This is called a data lake – an opposite of a data warehouse which collects and sorts data before storing it in the relevant silo and ultimately discards the older data to be able to add newer data.

Now, as much as one may question the risks and advantages of making this big shift there is no doubt that this shift in inevitable. There is already a huge production and storage of data, which is only growing. As this data grows the demand for storage and mining solutions grows with it. That data lakes offer a solution at a low cost makes them very desirable. The other factor that makes them attractive to various industries is the agility and options they offer for insight into that data. But, the factor that is the most controversial is the silo breakdown. The availability of all data across all levels leading to true data democratization!

So what happens when data silos are broken down and why is it so terrible according to most people? With data stored in no particular structure and with no definitions it is accessible by all departments at all levels to be played with.  While this is obviously a security risk, the bigger argument is that not everyone is qualified to make sense of this data thus affecting data lineage and quality. Other than that there is also the concern that since data lakes by themselves are not equipped to provide any clarity, different departments and people will come out with different results thus creating more chaos than solutions.

While, these arguments are sound they tend to cloud over the enormous world of possibilities opened up to us by data lakes. Massaging and making data answer our questions is the way we operate at this point in time. But, how do we know which questions to ask? How do we know whether the question we are asking is the correct one? The answer to that is that we don’t. Thus, when we choose to break down silos and play with data across functions and departments we open ourselves to questions and realizations we never imagined. Up until now everything has been built around simple questions that we defined over the years. Imagine 5 years from today, with the help of data lakes, we will have access to the all our data old and new, thus being able to ask and answer newer, more refined questions. The openness combined with the agility alone makes data lakes one of the better solutions going forward.

 So, is it time to roll up our sleeves and turn to data lakes? Are they the real future? We have a long way to go and a lot to learn in the world of free flow data but at the same time we cannot ignore that more and more non technical departments are turning to data for answers and these answers can come our way only with a clear open structure. We can compare this change to the coming of Amazon onto the Internet. At that time many businesses wondered if they really needed a website for their business. That question has very clearly been answered 20 years later today. The companies that survived were those who made the switch. The real question is not if this is the future, but, are we truly prepared to make the transition. Making this transition obviously means sharing data but it also means letting go of your data, letting go of the command of your silo. It is the companies, which are prepared to let go of the established rules of data ownership, and power that will be able to survive this switch. These are the companies that will gain competitive advantage in time to come.


The 3 Big Vs of Big Data


With each passing day, a company creates and captures figures and data. Every purchase, every transaction by your business teams produce information that must be managed. Tech giants like Amazon, eBay and Google have taught us that data can be a source of value creation.


We have had mainframes for a while, which, provided us with sufficient methods to calculate daily business indicators. We then used these BI tools to manage and make the right decisions at the right time.

But, this was earlier, earlier than the coming of the great innovation called the InterWeb. Before you see the power and possibilities of Big Data, what we call the 3 Vs: Large Volume, Value and Great Velocity, let us consider these points: what do you do with this data? Does your information system behave like an open faucet or like an oil well?


 The first characteristic of Big Data is of course the volume of your data. What is the cost of storing this information? What do you do with it? What is the data growth factor? Do you know how much information is lost every day in your information system? Let us assume that your technical teams already use dashboards to monitor your machines: CPU, memory, input / output network, technical logs, etc. But, that is of no interest to the Marketing department. Your sales now wish to view real-time traffic to your site. More than technical data, the key is to exploit the volume of business data. This will allow you to predict and better understand your customers. Do you already have these tools? Will they be able to handle three times more data in 6 months?

Quantifying and identifying is what finally helps to define the usability of your data. Whether on the storage format, the tools used, or even your security policy, this volume can be your first source of income in a few months.


We live in a time where it is possible to record everything and store everything, yet we fail to exploit this ability to its fullest. Developers now have access to an ecosystem of technical solutions in order to create the data. Nothing is easier than adding a collector in a Web application and forward purchase or the product of choice through a dedicated system.

Earlier web developers only saw the data warehouse, often a relational database. Today, web developers must also consider storing user behavior of an application to a new system. Yes, these are also Big Data projects. In this vision the developer should be required to create value, prepare and store business information. Technical logs are good. Logs trades are even better. Are your teams adaptable to this evolution? Are simple solutions based on open industry standards being considered?

Pushing a little further, we can imagine two worlds. The first, where the CIO cannot explain to investors why nothing was stored for many years. The second, where the CIO is no longer simply an asset manager. He sits on a volume of important marketing data thus creating a magnificent Headquarters of Defence for the company. When are you insuring your data assets?


The developer has an ace to play here. Using an analogy to the world of finance, where high frequency trading has come into its own, we need to realize the potential of data the size of big data and more. For example, when will a solution be capable of running an arbitration and book an airline ticket for us at the correct time? There are possibilities but when will they be realized? As developers, we now have access to phenomenal power. A search on Redis takes only 10ms, 10 to 15 times less time than a blink of an eye. We can save time by destroying silos and intelligently storing data in a de-structured format thus creating even higher technical format. Why? Because the same memory that cost a pretty penny yesterday is practically free today with the costs having come down drastically. Another thing that makes a big difference is simplicity. And no, Hadoop is not the answer to all Big Data projects. We are still in our infancy and have yet to learn solutions from the world of intelligence, and perhaps a bit of pragmatism.

In conclusion

We will fight tomorrow to find technical analysts. The Hadoop expert will be courted as was his father, the expert BO before him, and his grandfather, the expert Mainframe. Tomorrow, new professions appear in the form of expert analyst, data surgeon, digital actuary data insurer or Conservative digital mortgages. But everything, absolutely everything, first has to pass through the hands of a developer.

Software vendors also have the promise of a new El Dorado. Calculations Solutions, representation, analysis, prediction, and machine learning ... We are certainly at the dawn of a new technical revolution.

Written By: Nicolas Martignole Nicolas Martignole is the Lead Developer at Captaindash. He was previously the Lead Architect at Zaptravel. He’s also the creator and co-organizer of Devoxx France, one of the biggest conferences for Java and web developers in Paris.  You can reach him on Twitter on @nmartignole.

Data-Driven Marketing Best Practices

information_overloadIt's impossible to escape from the truth that, following the wake of the digital transformation, the marketing environment has irrevocably changed. The digital ecosystem is not what it used to be and so activities must be adjusted and refined in order for companies to succeed in the new world. First we must identify the changes that have taken place in order to define the and anticipate the resulting challenges. New customer experience The digital transformation has affected communication and thus the consumer experience as well. Companies who have leveraged big data to their advantage are micro-targeting their market and delivering hyper-personalized communication and offering. With the standard for competition set at a high bar, all companies will have to strive to deliver a customer experience that wows, marketing that effectively generates leads, and a streamlined and efficient system of customer relationship management.

Every solution claims to be a "wonder fix" The market is heavily imbued with different technologies that claim to be a wonder all on their own but don't have integration capabilities. A true wonder fix doesn't exist - with the competitively of the technological environment, new technologies are constantly emerging that outperform those of the past. A truly intelligent investment would be in a flexible data solution that has the capacity to integrate multiple systems and is capable of adapting to future developments.

So what best practices can act as solution to combat these challenges?

Develop skill sets When hiring it's important to pay attention to key quantitative skill sets that relate to the core business function. However, the world is evolving and as even months after a new hire, you may be faced with a technological challenge that requires new competencies of your teams. In these cases, training and continuing education are essential…that means investing in the new software and the tutorials to teach your team how to use it.

Define goals and constraints Ignoring challenges doesn't make them go away. With change comes difficulty and uncomfortable adjustment periods, so rather than ignoring a potential problem or setback that may result, pay attention to, define, and address your constraints.

Integrate technology Cross channel marketing is now essential and we communicate with our customers through a variety of feeds. It's important to consolidate your marketing efforts and simplify your life through adopting integrated analytics and management systems.

Complete company alignment Determining priorities and creating company cohesiveness must be promoted through complete company alignment. Teams need to share the results of a campaign or important pieces of information on a fluid, instant and company-wide level. The only way to manage the complexity of the digital world is through aggregation, consolidation and simplification.





Big Data and Causation: The Curly Fries Case Study

Curly-Fry-Cutter-review Big data can be an extremely accurate predictive tool and often reveal patterns and outliers, but often the relationships between big data and the information it attempts to represent are convoluted and misleading. To delve through the data  we must ask "why" to figure out the reasons why a particular big data finding exists and connect the numbers to their real-life representations. The fact that "big data" is often open for interpretation leaves room for human error, meaning that we must be especially aware and make a concerted effort to apply intelligence to the data in order to glean as much accurate value from it as possible.

An interesting case came to light in the recent TEDX MidAtlantic talk when scientist Jennifer Golbeck revealed a phenomenon dubbed the “Curly Fries” Case Study. The concept is simple but quite bizarre. When scientists were looking into the possibility of selling people’s personal social media activity data to future employers and marketers, they found that there was an astronomically high percentage of “smart-people” that had liked curly fries on Facebook. The question is why, and the potential thought processes used to answer that question can reveal a common analyzing error typically made when formulating explanations to support big data findings.

A mechanic, robotic approach to analysis would use the straight-line method to connect one and two and conclude that smart people like curly fries. The hypotheses that could follow: If my child develops a penchant for curly fries at a young age, can this predict his future intelligence? If I feed my child curly fries, will he become smarter?

In reality, we have to take the big data findings at face value and not form irrational conclusions. The study has told us that a lot of smart people like curly fries on FACEBOOK. So what intelligent and realistic explanations could support this finding? If you think about the mechanics of facebook and the trickle-down approach through friend networks on social media sites, you'll realize likely that a person with a large friend network of equally smart people and a high influence factor “liked” curly fries then the rest followed.

It’s important to distinguish between correlation and causation. It’s very easy to assume that an outlier in a dataset indicates a causal relationship. In reality, smart people are not more likely to like curly fries and it can be due to a unique coincidence independent of the data.

Lesson learned, and enjoy your Tuesday!

Data-Fully Yours,

Captain Dash


Data Analytics, the Internet of Things and Dynamic Visualizations The Most Disruptive Sectors

Picture_38 The word disruptive typically calls to mind scenarios such as an impulsive child acting out in a classroom or a roadblock forcing a detour, yet in the world of technology the word has a wholly positive connotation. In the fast-paced and competitive tech environment each company is in a constant battle with one another attempting to out-new and out-cool the competition. So at what point can we apply the word disruptive? Disruption doesn’t necessarily signify a new and crazy invention. A disruptive person would be a thought leader whose innovative thinking or revelations renders past schools of thought or debates obsolete. A disruptive company jumps a step ahead of the rest, displacing past technologies or processes and forcing other companies to adapt to the change. The point is that for something to be disruptive, it must touch everyone in some way and necessitate change that evolves an industry, even in a small way.

A recent survey revealed that people predict that data analytics, the Internet of Things, and dynamic visualizations will be the most disruptive sectors in the next three years. (http://www.techradar.com/news/world-of-tech/big-data-analytics-and-internet-of-things-voted-most-disruptive-future-tech-1249855).  The same survey reported that respondents currently consider mobile technology, social media and cloud computing to be the most disruptive sectors today.

So what does this mean for the near future? Clearly these three sectors have recently attracted a spotlight, and with their importance acknowledged it’s sure that more financial strength and brainpower will be dedicated to them and breakthroughs will certainly result.

Data analytics "Big data” and analytics have garnered a lot of attention lately as the new bread-and-butter for companies who have a digital presence. What the public doesn’t realize is that big data analytics will inevitably be implemented in companies even without a digital presence. Those in the manufacturing industry still produce data regardless of the fact that their main activities are rooted in the physical. For this they’ll need to leverage big data analytics in order to keep up on an increasingly leveled playing field.

The Internet of Things We can do a little word breakdown to understand this concept better. Inter-net means the net between, namely the net that connects smart objects together in an increasingly connected world. New objects and new ways to connect already existing “smart” objects create disruptive waves in our everyday lives. Smart homes, smart watches, and smart glasses used to be a thing of science fiction. But now that Google Glass is on its way to becoming mainstream in tandem with the rise of the quantified self movement (collecting personal lifestyle data) it’s clear that smart objects will become more central to our everyday lives.

Dynamic Visualizations  It’s been known for quite a while that there are massive cognitive benefits to viewing information in the form of a visualization. The mind can grasp visual information quickly and more thoroughly when it’s presented in the form of an image versus text or numbers.

Access to advanced visualizations to represent company data unequivocally gives companies an advantage over those who simply use spreadsheet or textual reports. Visualizations can exhibit information and highlight patterns in a data set in a way that intuitively makes sense for users; numbers and test simply cannot.

As companies are realizing the vast benefits of visualization-driven strategizing, there has become a greater market for companies who specialize in providing these sorts of solutions. As these companies become more competitive with one another, they’ll be competing to create more and more advanced visualizations. Dynamic visualizations with manipulable features will soon become the norm.


In conclusion, we must keep our eyes on the horizon. The only constant is change as they say, and with so many recent breakthroughs and advancements evolving exponentially, it will certainly be interesting to see what developments these top 3 sectors will churn out in the coming years.


Data-Fully Yours,

The Captain

Big Data & Education Collide: Standardized Testing and Child Self Esteem

(Disclaimer…this is a rant) Big data in education…what a mighty subject to attempt to cover. There's no denying that big data is touching every industry, and of course education is a field teeming with profitable opportunities. The formation of the future generation is not a responsibility that any country takes lightly, and so many companies (especially those in big data) are realizing the potential to improve and perfect the learning process through analytics and trend spotting in order to optimize students' education. 

It would really take all day to weigh the merits and flaws of the big data - education collision which is why I will discuss in specifics one relevant issue: standardized testing.

In the United States, the entire national school system uses standardized testing to measure the performance of students individually and in comparison to one another, rank teacher performance, and rank schools (which then affects their funding). What not many people know is that standardized testing is also used on a trial/error experimental basis to measure the effectiveness of curriculums, teaching methods, and textbooks.

These usages may seem innocent at worst, yet there still exist purveying issues behind big data application, particularly when relating to a subject as provoking and controversial as children's education.

There are many issues with this that span from privacy to the unfairness of funding allocation to the fact that teachers and administrators are given financial incentives for having higher-scoring students. Yes, those are all massive problems but today I'm not going to talk about any of that. Rather, I'm going to address the elephant in the room which is that...

The testing is being used to prove something it doesn't measure. A standardized test may be effected at 10 different schools in 10 different cities. The school who performs the best is viewed as the "best" school with the most effective teachers and the best curriculum. It's assumed that the teachers and the lessons are better at this school.

Equally, 5 teachers could teach the same lesson. Whichever classroom scores the highest is thought to have the best teacher.

5 different lessons could be taught. The highest scoring classroom is thought to have the best lesson for learning the concept.

So what is the problem with all of these assumptions? The problem is that a standardized test is black and white. When it's used to measure varying performances and pick out best practices, it's assumed that all variables have been held constant and that only ONE variable is changed (ex: teachers thought to be best at one school because they have consistently higher test scores). In reality, there are many factors that influence a child's score on a test that span beyond the simple effectiveness of the teacher or lesson or textbook or how intelligent the child is.

There exists a very well-known theory called Maslov's hierarchy of needs. Basically it says that all human needs can be visualized in a pyramid shape, where the most basic needs are on the bottom rung, and only once the basic needs have been filled can a person move up to the higher rung of needs.


This theory can also be roughly applied to children. If self-actualization (the top rung) can be defined more in terms of a child's fulfillment of their own potential and effective learning, then that means that all of the needs on the lower rungs of the pyramid must be fulfilled in order for them to achieve their academic potential. There can be many reasons why a child or a group of children (in an entire school) are blocked from the lower rungs of the pyramid. They could live in a low-income or at-risk neighborhood and the problem could lie with their parents or their home life. Or the school environment itself could be harmful - there may be a problem with bullying (safety) or with students feeling a sense that they don't belong. These factors can't and aren't factored into the results of a standardized test…which makes a standardized test inaccurately representative of what it's meant to measure.

I really believe that this hierarchy of needs theory plays a huge role in influencing how effectively children learn. Low self esteem in particular is a major issue that is actually propagated by standardized testing. Basically, a child's self esteem is influenced by how their parents see them, how their school sees them, and how they see themselves. If at a young age a child receives low test scores, they know that they're performing lower than "average" compared to their peers and their parents are aware of it as well. At first their parents will think that the child is having a "hard time" but if this issue persists, they will inevitably come to the realization that their child has academic difficulties indicative of lower intelligence or just a lack of "book smarts." The child will consider him/herself as less intelligent or worse in school, and this will affect their identity and self esteem and perpetuate the cycle of low performance.

Now, the problem with this is twofold. These test scores damage a child's self esteem because they and their parents are made aware of these test scores. Now, it's not realistic to think that there's a way to administer these tests yet keep the scores from the parents (the argument would be that they have "a right to know"), so how would the education system go about minimizing the impact that these scores have on the child's perception of him or herself? The answer to that would be that the school shouldn't put such a heavy weight on these test scores. Subjective measures of performance should be highlighted just as much as standardized, objective measures. These measures could be essays, creativity, the child's personal qualities (a good leader, etc). It would certainly be an idea worth considering to require (on a national level) that teachers pay attention to students' personal qualities and report those back to students and their parents during parent-teacher conferences. Schools with more funding (typically in high-income areas) usually do take this approach to student development, but it would be interesting if national policies reflected a shift in attempting to improve children's self esteem.

The OTHER problem that hinders children's self esteem is the fact that they really only have one job…to be a student. They aren't old enough to have a job or have discovered and nurtured natural talents yet. So if a student is not successful at the one thing they do all day (go to school) then their sense of identity and self esteem will be damaged.

Now, most schools require that students participate in athletics, but what often occurs is that children who are younger than the rest or less physically developed tend to be overshadowed by their older, stronger classmates in sports and in the classroom. So what is an alternative method to help a student build a sense of identity independent of their "book smarts" that can't be damaged by test scores? Creative pursuits of course! Many schools who lack funding have cut creative and art programs in favor of allocating their funding toward more "practical" subjects that they feel will have a more direct effect on student's academic performance. However, what they don't realize is that a student's self esteem is essential in their ability to learn and the quality of their academic performance, and that sense of self worth can in turn be nurtured by creativity.

Earlier I also touched on the fact that schools (particularly in at-risk areas) may have a bullying problem or the students feel like they don't belong. On a national level, policies require that bullying be met with strict discipline and that resources like school counseling must be available to students who are dealing with social problems. Sounds like a good idea, but when you were 6-14 years old did you ever think to seek out help from a school counselor? Realistically, children aren't going to look for help from an adult if they're having difficulty…in fact, a student might not even realize that there's a problem in the first place. The solution to this is a focus on workshops, seminars, and possibly even required subjects that emphasize empathy and teach students the tools to set personal boundaries, deal with social issues, and strive for social harmony.

I apologize for the long rant, but I really feel like it's time to highlight the issue of big data not from a frenetic parent's perspective ("My child is not a data point!") or the administration's perspective ("Big data helps leverage and standardize the national schooling system") or from the perspective of a big data education company looking to make a profit. Students are the purpose of education, so we must try to gain a deeper and more holistic understanding of how children learn and how their environment affects them in order to figure out exactly how much value certain big data initiatives are actually adding.


Signing off for now,

The Captain



Big Data Leveraging Across All Industries Levels the Playing Field

Today I saw an interesting headline informing the public that "leveraging big data is the new price of entry for the manufacturing industry." Indeed, in an evolving world where the digital is touching virtually every industry that statement holds true. Big data implementation used to be more of a leg-up and gave companies a competitive edge; now in this leveled playing field companies must leverage big data simply to keep pace with their competitors. Although the article in question specifies the manufacturing industry, is it possible that different industries must implement big data analytics differently to achieve the same value? How?

E-commerce- You don't have surveillance cameras to track your customers' journey through your store. You don't have the ability to see or hear your consumers travel through your physical space, making comments or stating complaints. You need some way to track their path online while connecting this to behaviors and reactions, like purchases, conversions, and bounces.

A stupid company doesn't continuously evolve, improve, and address mishaps. Data analytics is necessary for any company who wants to figure out what they're doing right, what they're doing wrong, and what they have potential to do differently. A webpage has a high bounce rate? Boom. Change it. Your conversion rates are low? Boom. Make your products look more attractive with better images or more specific content.

Brick and mortar retail- These companies profit from the benefits of existing in the sensory physical world but also are burdened with several challenges that accompany a more physical experience. For one, they have a clear, eye-to-eye view in and around their store and a clear ability to connect physical actions and behaviors to purchase. However, they also have more moving parts at play that influence their success. They have to ensure that their location is appropriate and determine if people in the surrounding area will want to buy their products (a.k.a. if their target demographic is accessible), their store layout, setup, physical branding, etc.

E-commerce sites exist in competition on more of an equal playing field in the sense that they all exist in cyberspace and are accessible to all. Their location is where they show up on google search, and their store "interior" is their digital space that they can craft to reflect their branding. In fact, this actually heightens the level of competition because the benefits of physical location (like being a neighborhood store) will not influence buyers to purchase from them over others. Basically a consumer's only deciding factor when choosing to purchase or pursue a relationship with an e-commerce retailer is what's in their site and their online reviews.  Data analytics can help them gain a more focused sense of their digital surroundings - who is searching for them, what consumers say about them, and how people respond to and interact with their online presence.

What do these two have in common? 

1. Brick-and-mortar retailers increasingly need an online presence to survive- They need analytics to manage their online presence for the same reason that e-commerce sites do...in addition to aiding them in forming a cohesive unit between it and their physical presence.

2. With e-commerce, the "company" may be online, but the product is still physical. E-commerce and brick-and-mortar retail still have physical products and supply chains, physical people behind the computer screens, and need data to make sense of their physical operations. The physical world is a large jumbled space - relationships and correlations are often difficult, if not impossible, to identify and tease out in that world. We need data analytics to record information and put pieces on paper, then side-by-side to see what we find. Valuable activities and indicators to watch are departmental spending, revenue per employee, trends in purchases, etc.

And manufacturing?

3. Manufacturing has no website to worry about or social media presence to manage. They can use data analytics on a strictly internal basis. Manufacturing is a complicated process, dealing with many automated systems that all have unique relationships with each other, from each step of the supply chain to the production processes. Big data analytics can be used to connect operations by continent, country, city, etc. and figure out lag times, isolate problems, and identify patterns, in addition to measuring productivity in terms of value added per employee or equipment.


These industries may be different and implement big data for varying purposes, but the message is still the same: big data deals with numbers, and every company across every industry needs to sit down and look at their numbers if they want a hope of increasing a special little number called revenue.