Sinequa Named Visionary in Gartner’s 2014 Magic Quadrant

Sinequa, has been growing rapidly over the past year, releasing an upgraded version of our software, Sinequa ES V9, and acquiring enterprise customers like AstraZeneca and Biogen. So you can imagine how excited we were when we learned that we had been positioned highest on the “Ability to Execute” scale within the “Visionaries” Quadrant of Gartner Inc.’s latest Magic Quadrant for Enterprise Search.

Gartner’s Magic Quadrant identifies technology companies in different markets and is considered a key resource for perspective on technology and service markets. According to the report, “Visionaries address the keen desire of enterprises to pursue simple access to complex data with fresh capabilities and flexibility.” Providing search features that allow for better collaboration, application development, and innovative means of finding and working with content is particularly valuable. Sinequa was one of 17 vendors evaluated by Gartner to help search managers and information architects make the right choice.

As a result of our positioning on the Magic Quadrant, Sinequa continues to gain acknowledgement by potential customers and the industry for built-in natural language processing capabilities that cover languages spoken by 95% of the world’s population, extremely scalable IT architecture and leading edge technology.

Sinequa is attending the upcoming Big Data in Pharma Conference in Boston, and we are looking forward to attracting large bio-pharma companies to our booth and to a presentation by Sebastien Lefebvre of Biogen Idec, on how he has used Sinequa, in part, to create an innovative holistic system that captures and channels insights out to the right people.

To learn more about Sinequa visit us here.

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Unstructured Data & Business Intelligence Analytics (part 2)

In our last post, we interviewed Sinequa’s Vice President of Marketing Hans-Josef Jeanrond about the Sinequa functionalities that differentiate them from other big data analytic platforms, but how do companies benefit specifically from Sinequa?


Global biopharmaceutical company AstraZeneca recently has chosen Sinequa to harness the company’s big data infrastructure to find relevant information about drugs, diseases and genes, new uses for existing compounds and help bring treatments to market more quickly. Let’s hear from Hans Josef about how this relationship works.


Q: AstraZeneca chose Sinequa for their world-wide R&D. What does a bio-pharmaceutical company do with a real-time big data search & analytics platform?

A: Most companies in that industry have R&D organizations in different countries and even continents. The question of who knows what and who works on what topic becomes non-trivial. If you want to avoid redundant research and tests, if you want to find people with complementary skills to work on a common project, you need a search & analytic platform like ours. And that platform needs to “understand” the highly technical and scientific vocabulary of the industry, it needs to be able to analyze (“index”) literally hundreds of millions of documents – from internal and external publications via projects reports and patent filings to emails – in order to come up with relevant information on a given topic, and to detect implicit networks of experts. We are talking about “implicit” networks because people may never have declared to be part of a network, they may not even each other.


Q: Why was Sinequa the best choice for AstraZeneca?

A: It is our combination of speed and functionality that made us the best choice for AstraZeneca. AstraZeneca invited a number of suppliers, big and small, and had them go through multiple tests. Instead of an abstract evaluation, they took 15 million documents and had three competitors work with them and come up with an analysis within a few days. They judged each supplier on the following categories: performance, relevance, and capability of partnering with them. Once the Sinequa platform started performing, the speed of developing applications on top of our platform impressed them.  This speed has been – and still is – unprecedented. With Sinequa, AstraZeneca could come up with “Apps” at the rate of 1 per week, a speed many of their competitors have difficulties imagining.

As for functionality, AstraZeneca knew that off the shelf solutions would provide many of the results they wanted, but even still, they would have to build more specialized functions on top of each product. That usually causes problems with every new version of the software. With Sinequa, they found a partner that would integrate the bright ideas of the AstraZeneca team as generic innovations into its platform, such that they become part of the standard solution. This removed of the common headaches from multiple layers of software that follow different revision calendars.  In the end, AstraZeneca knew what they wanted: the best available solution from not just a vendor, but a partner.

Q: What is the most important feature that Sinequa can offer AstraZeneca, or a similar company, in their vertical?

A: What sets Sinequa apart is not one single “killer function”, but the combination of functions it offers.  Each product in this space offers some of the functionalities that we offer, but it is hard to find them all in one place. That is where our difference lies, and what distinguishes Sinequa from our competition.

It was important for AstraZeneca, that we were able to easily integrate their trade dictionaries as well as company-specific taxonomies and ontologies, in order to provide information on synonyms and semantically related concepts while answering a simple query. It was important that we could identify implicit networks of experts in their world-wide R&D despite the complex scientific content of their documents.

Q: Why should a pharma/biotech company choose a small business for big data analytics?

A: Looking for a partner of the right size can be a challenge. Most pharma/biotech companies are looking for a partner that is small enough to be flexible, but big enough to handle the task at hand. At Sinequa, we like to say that we are big enough to be able to deliver and small enough to care.

Q: What would you want executives of a company looking to start using their big data to know about Sinequa? What would you want their takeaway to be?              

A: I would want to make them aware of the fact that International Data Corporation (IDC) says 90 percent of a company’s data is unstructured data which they probably don’t currently exploit to their advantage. I would tell them not to simply go for more of what they already have – bigger databases, bigger data warehouses, and more analytic tools for only structured data, but to go beyond their current limits: orient themselves in an area where they haven’t been able to do anything before, in their unstructured data. Many IT people think that data ought to be structured and if it isn’t, they haven’t completed their job. But the truth is, unstructured data grows 10 times faster within enterprises than structured data. Trying to structure it all is like chasing the Grail: You’ll never attain it. So companies need tools to analyze their unstructured data. The good news is Sinequa can help them do that. And do it many times faster than it would take them to structure it and to integrate enterprise applications and data sources.  What more can you ask for?


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Inclusion of Unstructured Data into Business Intelligence Analytics

data5Today’s large enterprises and administrations accumulate ever bigger volumes of ever more heterogeneous and volatile data. More than 90 percent of this data is unstructured, consisting of texts in many different languages. Information workers spend too much time searching for the information they need for their work and companies lose business opportunities when employees are drowning in these mounds of data.

Enter Sinequa.

Sinequa’s real-time Big Data Search & Analytics platform helps its customers meet this challenge. But what makes Sinequa different than other big data analytic platforms? See what Sinequa’s Vice President of Marketing Hans-Josef Jeanrond says about the value of Sinequa’s platform and how companies can best leverage the software.

Q: What differentiates Sinequa from other big data analysis platforms?

A: There are two basic reasons why Sinequa is different from other platforms. One being the data sources we are dealing with: We are not tackling the World Wide Web nor the complete internet of things. We focus on the enterprise world with different analytical tools.

The second reason that we stand apart from other big data platforms is that we do not choose between structured and unstructured data. We offer combined statistical and semantic analysis of big data and use the structured information to refine linguistic analysis. With Sinequa the semantic analysis of unstructured data is used to create structured data; one analytic method is used to refine the other. What sets Sinequa apart here is our capability to query an index of up-to 200 million documents in real-time with sub-second answers and our ability to deal with semantically related subjects in 19 different languages.

Q: Can you please further explain the importance of semantic analysis?

A: If you limit yourself to statistical data analysis, enterprise data often poses problems in sample size, sample error and sample bias. This is why tools from the Web don’t work well with enterprise data.

Keyword search is not a sufficient option either: The keywords you use in an information request may not occur in many of the documents that are actually relevant for your work. These keywords may not have been added as “meta data” by the people who classified and stored the documents in your document management system – they may have been looked at from a different perspective.  You need to find documents dealing with concepts that are semantically related to your request, thus needing semantic analysis to find them.


As you can see, the Sinequa platform offers functionalities that truly differentiates it from competitors. Sinequa combines deep content analytics, including Natural Language Processing with an extremely scalable IT architecture, offering users simple and secure access to the most relevant information. Stay tuned for our next post that will explain how a leading bio/pharma company implemented Sinequa to index millions of R&D documents and break down barriers between information silos, worldwide.

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Big Data: Garbage in – Garbage out?

I had the chance to present the Sinequa solution at a conference organized by one of the 5 big pharma companies a month ago. The theme of the conference was innovation for RDI and the first morning was dedicated to Big Data. Sinequa was invited because we have customers like Siemens or AstraZeneca that are using our solution for their R&D teams to help them find information in large amounts of internal and external documents (publications, project reports, test reports, patent filings, and even emails), but more importantly, to find expertise based on the analysis of these documents.

Following all the contributions on the topic of Big Data and a round table discussion, questions were invited from the audience.  One question from the audience put all the speakers on the spot:

Big Data… is it garbage in or garbage out?!

After few second of hesitation I ventured an explication of why technologies like search could help information workers to actually select what is garbage for them and what isn’t.

But the question was definitely more complex than it seems!

Big Data is very often associated with machine data and related storage issues. In large organisations, especially in R&D, Big Data is very often human generated data. By this I mean documents, email, research reports stored in many applications, containing years of research on a specific subject. Furthermore, in R&D, information does not only reside inside the firewall, but also outside, in specialized databases or in academic publications. To some people, hundreds of millions of such documents may appear as “garbage”, but they could turn out to be a goldmine if a scientist finds in that “garbage” research results related to his or her current research, or even better, if he or she can find an “unknown colleague”  who can provide answers to some specific questions.

Then came the question: How to actually filter the garbage for each end user and help find the goldmine?

The first approach people take is to define the best sources for good content. With search, if you index poor quality content, you will find poor quality content!

But very often it is almost impossible for an IT department to define what is good or bad quality content. The quality of content may even be perceived differently by different users, i.e. different subject matter experts.

This is the main challenge in dealing with human generated big data!

Search is all about “Free-Form-Analytics”, contrary to the slicing and dicing in predetermined structures of data warehouses and “classic” BI tools. To offer this flexibility, data is organized during initial indexing and then during the life of the search application.

Here are the main steps to achieve this:

Step 1: We index all the content with the corresponding security credentials, and the available metadata for every application or data source used by information workers on a daily basis

Step 2: During indexing we perform statistical analysis, like many other search engines, but we add our special sauce, Natural Language Processing and Semantic analysis to be able to tag names of people, companies, places, etc. in a full text. It seems easy, but this is the hard work that needs to be automated, at scale, for hundreds of millions of documents, if you want to get a grip on the “Garbage”.

Step 3: Once this work is done, here comes the interesting part, when we link the automated content analytics to an organization’s “DNA” (mostly contained in its business applications). Organizations have spent years trying to organize their content and will probably continue to do so forever without ever seeing the end. Why not use that available DNA (products, client information, HR data, etc.) to refine the content analytics performed in step 2? An anonymous person detected in a text then becomes a colleague, a customer, a partner, etc. A strange series of numbers and letters becomes a product ID and so on.

Structured data helps to refine the analysis of unstructured data.

Once you have gone through these steps you are able to provide end user snot only with a way to manipulate huge amounts of data (what you may have called garbage before may have become valuable Human Generated Big Data), but also to make sense of this data by asking the free-style questions that they are interested in at a given time. There is no limit to the questions you can ask – and hence no limit in making your Big Data valuable.


By Xavier Pornain
VP Sales & Alliances at Sinequa

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Mission Big Data de Boston à New York

Du 14 au 21 septembre, Alexandre Bilger et Xavier Pornain se sont envolés vers les Etats-Unis pour une mission « Big Data, simulation numérique » organisée dans le cadre du programme « Ambition PME » qui vise à accélérer le développement des PME françaises innovantes à l’international.


Au programme, coaching pour mieux appréhender les spécificités juridiques et marketing américaines, découverte de l’écosystème local, networking et pitch de la solution Sinequa à la Data Innovation Night de Boston et à la Data-Driven NYC de New-York.

« La CCIP [qui est avec Systematic, à l’initiative de ce voyage] nous a approchés à l’occasion du salon Big Data Paris pour nous proposer de faire partie de cette délégation de 7 start-up françaises voyageant de Boston à New York pour prospecter le marché américain.  En tant qu’un des leaders français dans le domaine du Big Data et ayant déjà de nombreux clients à l’international, nous n’avons pas de doute sur le fait que notre technologie est suffisamment mature pour répondre aux attentes de géants américains tous secteurs confondus. Pour nous, ce voyage a donc surtout été l’occasion d’affuter notre discours pour mieux prendre en compte les codes américains et surtout de décrocher des rendez-vous d’excellent niveau avec des personnes décisionnaires de grands comptes américains » explique Xavier.

Les réactions ont été extrêmement positives vis-à-vis de la solution Sinequa. Les entreprises rencontrées, des géants du domaine de l’assurance, de la banque, ou de l’industrie ont été très réceptifs vis-à-vis de la technologie de Search et d’Analyse du Big Data en temps réel de Sinequa. Ils ont une excellente compréhension des problématiques sur lesquelles nous travaillons et voient tout à fait comment nous pourrions leur être utile.

Cette initiative nous a donc permis en une semaine de valider le potentiel de notre solution sur ce marché et nous fait faire un pas de plus vers une implantation future aux Etats-Unis.



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