Following our first post (in French) concerning the business challenges raised by the data collection and analysis in the retail sector, we will now present a use case with its associated issues. We will see how to face them based on modern technologies that have already proven themselves in Web giants: Kafka, Spark and Cassandra.
See you on June 6 and 7, 2016
Brace yourselves, USI is back June 6 and 7, 2016 at the Carrousel du Louvre!
A unique speaker line-up
Over the past 8 years, the conference has grown to become a benchmark for major international conferences on digital transformation. 1600 attendees are expected this year.
USI’s unique speaker line-up provides a fresh outlook on new technologies. Explore the trends, expand your network and experience inspiring talks in the very heart of Paris.
USI speaks to all players involved in corporate digital transformation.
In 2014, my colleague Stephen Perin and I wrote “FinTech Is Cannibalizing Banks!” a White Paper that had a certain impact in France and even in Canada. We have been following the innovation in retail banking since 2010, and this WP was meant to create a sense of urgency.
In March, 2015, we organized a small Finovate in Paris (FinTech Day) where 20 French FinTech came to demonstrate their solutions. It was the first event of its kind in France. Sure, we are running behind when compared to London but now the French FinTech ecosystem is really expanding fast, is self organizing (Association Finance Participative). The funds raised, the acquisition, and the scaling of some actors is now underway.
Worldwide global Fintech investment jumped 201% between 2013 and 2014, breaking the $12B mark with more than 730 deals.
Investment in FinTech, Jan 2010 – June 2015, in USD (src: IC Dowson and William Garrity associates Ltd)
Insofar as FinTech domains expand faster and faster, many questions remain regarding this new eco system. Before the Fintech Montreal Event to which I was invited to participate, some of these remaining questions were sent to me. Here are the answers I gave.
The questions of 2015
Is the opportunity real or are we looking at another technology bubble like 2001?
Unlike what happened in 2001, the FinTech business model relies on the fact that FinTechs have identified customers’ pain points and, in response, provide them with outstanding user experience (customer centricity). Their transparent (no hidden fees) business models rely on data they analyze and collect. Data is definitely their new black gold. Their business models do not rely on clicks or page views. Besides, the technologies they use are rich and mature (mobile, cloud computing, Big Data, API, etc.). That was not the case back in 2001. Promises that were made in 2001 can be kept this time!
Using a CI Server is a programming practice that is well established and not opened to debate anymore. Sometimes, it’s even a topic on which the IT Department has regained control of, managing and rationalizing the servers. Yet, as is often the case, mobile is following its own way: the technologies used can be considered non-standard among the company, the ecosystem is updated way more frequently than in other computing areas, and the need of running on a specific OS for iOS can be the fatal blow that leaves mobile developers on their own. Therefore, they frequently end up installing a mac mini in the open-space, in order to run their builds on it.
Whereas the problems related to using such installations are mostly shared between Android and iOS, the solutions differ: this article focuses on Android.
Application Performance Management (APM) is a tool to monitor and analyse the performance of software applications. With APMs, end-user response time, response times of various servers and server activity (CPU, Memory) can be collected. It is also possible, mainly for Java and .Net, to detect methods that seem problematic as well as the most costly SQL queries or blocked threads. According to the Gartner, an APM covers the following functionalities:
- End User Experience Monitoring (EUM)
- Application topology discovery and visualisation
- User-defined transaction profiling
- Monitoring of components resources usage
- Analysis and visualisation of collected data
One might say that VisualVM for Java or .NET CLR profiler cover at least some of these aspects. This is true but these tools require you to always keep an eye on the screen, literally! Moreover, it’s often impossible and dangerous to use these tools in a production environment. In contrast an APM will collect, store and aggregate this information into graphs that are easy to analyse at a later time. An APM can also send email alerts when certain thresholds are detected.
This first article in the series provides an overview of APM functionalities and shows the benefits of these tools in production, their initial target. It also demonstrates the interest to expand their use to other environments. Next posts will focus on various products available on the market.
In this article, we’ll talk about Xamarin, a C# .NET tool enabling development of cross-platform mobile applications. We’ll focus on the missing part: the reuse of native libraries.
What is Xamarin?
Xamarin is not only a product but also a company. The product addresses a common issue, the unified cross-platform development.
Xamarin allows to create native applications on iOS, Android and Windows Phone platforms. Its upside lies in the reuse of code, reducing the time to market.
Xamarin also provides its own development environment, Xamarin Studio.
With Xamarin, it’s important to grasp that applications are run natively. Every iOS or Android APIs are available from C# code. It’s true for push notifications, contacts integration, Bluetooth…
Sure, but what’s going on in real life? Regarding code sharing, in particular?
During our USI Event last summer we (Stephen Perin & Sylvain Fagnent) had the chance to interveiw Chris Skinner on the future of banking. Chris also did a presentation at this event (The future of money trade and finance).
This interview has been transcribed by Natalie Schmitz (Octo Technology).
I propose in this paper a chronological review of the events and ideas that have contributed to the emergence of Big Data technologies of today and tomorrow. What we can see regarding bottlenecks is that they move according to the technical progress we make. Today is the JVM garbage collector, tomorrow will be a different problem.
Here is my side of the story:
Android applications are commonly used to process very sensitive data. It is the developer’s responsibility to make sure that the information prompted by the user cannot be intercepted easily by a malicious people. The Open Web Application Security Project (OWASP) [9,10] tries to enumerate the potential security issues of a mobile application. Some of them are the system architect’s responsibility (such as issues related to weak server sides control), some are the back end developper responsibility (issues related to authentification checks) and finally, some are purely related to the mobile application. In this article we will focus on the issues which can be tackled thanks to the Android mobile developer’s action in itself.
Therefore we will address here three potential vulnerability sources : risks when we communicate with a webservice (WS), potential leak of information when we store data on the device storage and vulnerabilities of having your application easily editable by a third party.
The life science sector faces great challenges when the time comes to meet modern digital opportunities. This sector is incredibly dynamic, both from an economic and a scientific point of view, but innovation in the lab often comes in pair with a deep evolution in the information system strategy.
In this article, we will first focus on the Swiss Lemanic area landscape and see how the main computational trends encountered there are representative of the domain at large. We will then try to extract the most prevalent technical issues, either methodological or technological.
More importantly, we will see that life science has a particular culture and faces original computational issues. But a lot of the digital aspects are strikingly similar to those of other industries, like finance, retail or social media. Therefore, a great deal is to be learned from how companies in those sectors have undertaken their own transition towards a new digital era (and vice versa). Read more