Sinequa Snags Three Key Industry Award Wins in September

Sinequa Industry Recognition - September 2018

We’re off to a busy September here at Sinequa! We’re excited and humbled to have received a few different awards for our Cognitive Search & Analytics Platform and company as a whole this month. Sinequa recognition has included the following awards from leading industry publications:

KMWorld Trend-Setting Products 2018

KMWorld’s 2018 list of Trend-Setting Products features not only emerging software directed toward human-like functionality but also more traditional offerings impressively refined. It encompasses AI, machine learning, cognitive computing and the Internet of Things, as well as enterprise content management, collaboration, text analytics, compliance and customer service. Read more.

DBTA’s Cool Companies in Cognitive Computing for 2018

DBTA and Big Data Quarterly presented the 2018 list of Cool Companies in Cognitive Computing to help increase understanding about the important area of information technology and how it is being leveraged in solutions and platforms to provide business advantages. Read more.

Datanami Readers’ Choice Award Winner

Sinequa won the Readers’ Choice – Best Big Data Product or Technology: Machine Learning category.

The Datanami Readers’ and Editors’ Choice Awards are determined through a nomination and voting process with input from the global big data community, as well as selections from the Datanami editors, to highlight key trends, shine a spotlight on technological breakthroughs and capture a critical cross-section of the state of the industry. Read more.

Looking forward to continuing the momentum for the rest of the year!

For more information on Sinequa’s cognitive search and analytics platform visit:

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Inspiration and Innovation: Learning From Artists

This article appeared in Wired Innovation Insights.

The crowd at an IT event in Paris was intrigued to see an art historian as the keynote speaker opening the conference. They had come to hear their peers talk about trendy topics in Big Data, Search, Content Analytics, Natural Language processing, etc. But the first thing they were asked to do was contemplate a painting by Veronese . Then, art historian Stéphane Coviaux showed them a drawing that looked like a sketch the artist made before embarking on the big painting. Yet the “sketch” had been created by a different artist some 50 years earlier! Veronese a plagiary?!

When asked to compare the two works, the IT audience was not shy and came up with many major and minor differences. It became clear to everyone that Veronese had been inspired by the work of his predecessor. Through his changes in the conception of the image, in his use of space and color and through his own symbolic, Veronese had produced a major work of art from a comparatively minor source of inspiration.

The message to the audience: do not expect cooking recipes or “best practices”! Transpose what you see from others – the innovations they have implemented – into your own environment. And, don’t be overawed by impressive projects that you may see, view them as sketches for your own projects and “go create!”

In an emerging market or one that is radically changing, there simply are no “best practices” and no “recipes.” Take inspiration from others but use your imagination to create innovations that advance your business. A specific message for the modern times IT-audience was added: Aim at “co-creation”; find partners you trust to help you along in the creation process and accompany you in uncharted (or not completely charted) territory. In uncharted territory, your procurement services cannot take a standard contract out of a drawer and hope it fits.

The “distance” between the artistic and the business environments worked well to get the message across. The presentation of even an exemplary business project coupled with the injunction “be inspired! Do not copy, but transpose,” would certainly have provoked reactions like “fine but not applicable in my environment.” In the distant world of art, everyone could easily agree on the necessity of inspiration to create innovation, took this lesson home to apply to their enterprises.

Xavier Pornain – VP of Sales & Alliances for Sinequa.


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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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La coopération homme-machine distance les « supercalculateurs »

Vous connaissez peut-être la vielle blague sur le supercalculateur à qui on a posé la question « quel est le sens de la vie », et qui sortait (après un long temps de calcul) la réponse « 27 ». Cette blague a été racontée pour illustrer bien des aspects différents  sur des ordinateurs, sur l’interaction homme machine, la philosophie et la vie en général. Ici, je voudrais tirer votre attention sur les questions floues et réponses précises, ou vice-versa, et à l’amélioration de l’interaction entre les hommes et les machines.

Shyam Sankar a donné une conférence intéressante à ce sujet à  TED, sous le titre de « l’ascension de la coopération homme-ordinateur ». Dans son discours il explique pourquoi « résoudre de grands défis (comme arrêter des terroristes ou identifier des tendances émergentes) n’est pas tant une question de trouver le bon algorithme mais plutôt de la bonne relation symbiotique entre calcul d’ordinateurs et créativité humaine ».

Son premier exemple est bien connu mais il mérite d’être raconté encore une fois. C’est l’histoire de deux championnats d’échec de niveau mondial : En 1997, le champion du monde Gary Kasparov perd contre l’ordinateur « Deep Blue » d’IBM. En 2005, dans un championnat d’échec ouvert à tous, dans lequel des hommes peuvent jouer avec des machines comme partenaires, un supercalculateur a été battu par un grand-maître avec un ordinateur portable assez médiocre. Mais à la surprise de tous, le tournoi a été remporté non pas par un grand maître associé à un supercalculateur, mais par deux amateurs avec trois ordinateurs portables assez faibles. Sankar  pense que c’est la façon d’interagir avec leurs machines qui a fait gagner des hommes moyens avec des ordinateurs moyens contre les meilleurs hommes avec les meilleures machines.

Très bien, mais quelle relation avec le Search ou l’Accès unifié à l’information (Unified Information Access UIA) ?

Peut-être la relation est-elle tenue, et peut-être je ne l’exprime pas bien, mais je vous sollicite de m’aider avec votre « puissance symbiotique cerveau-calculateur » pour affiner  mon argument.

Dans l’informatique “classique”, où l’on travaille avec des bases de données, des entrepôts de données (data warehouses), des systèmes de BI, etc. , des questions précises sont posées au (super) calculateur par des gens qui connaissent la structure de leurs données et maitrisent la façon de poser ces questions, et l’ordinateur sort des réponses précises du genre « 27 » ou des tableaux de bord sympathiques qui illustrent des chiffres et même des trends. Mais si vous voulez poser des questions qui vous amènent en dehors des structures de vos données ou de la logique prédéfinie de vos « systèmes décisionnels », vous n’aurez pas de chance.

Le Search, par contre, vous permet de poser des questions floues en langage naturel et il ne vous retournera pas une réponse du type « 27 », mais un ensemble de réponses – des documents ou des entrées d’une base de données – classées dans des catégories (des « facettes ») dans lesquelles vous pouvez naviguer. (Des informations de sources multiples, y inclus des applications métier,  peuvent être agrégés dans une catégorie) Vous pouvez zoomer sur des sous-catégories que votre intelligence humaine reconnait instantanément comme les plus prometteuses. Vous pouvez aussi raffiner votre question suite aux idées que le premier lot de réponses vous aura données. En effet, vous pouvez poser n’importe quelle question que vous voulez sans aucune nécessité de (re) programmer quoi que ce soit. Et dans un ping-pong de trois échanges avec votre solution de Search vous avez de fortes chances de découvrir une réponse que votre supercalculateur avec ses logiciels élaborés n’aurait pas trouvée. Ou peut-être il l’aurait trouvée, mais après quelques milliers de jours-hommes de développement et de mise au point, et des millions d’Euros dépensés pour un matériel de pointe – tout comme Watson a gagné le jeu Jeopardy.

Chez Sinequa, nous aimons penser que nos logiciels sont meilleurs que la moyenne, mais même si vous présumez qu’ils soient tout justes dans la moyenne, l’interaction des utilisateurs avec notre plateforme de Search et d’accès unifié à l’information (Unified Information Access, UIA) se rapproche assez de celle des deux amateurs avec leurs ordinateurs portables qui ont battu le champion de l’échec avec son supercalculateur.

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Big Data: Marketing Nirvana or the Next Big Bubble to Burst?

Everyone surfs on the Big Data wave, redefining it such that their roles in this new “hot” market are maximized. Some journalists have already started to blacklist press releases on the subject, since they receive too much fanciful nonsense.

That is a pity for the companies that have really something to offer in this market. If you don’t agree that Big Data really defines a market, let us take a simple approach: We talk about enterprises and administrations that have to deal with vast amounts of data that come from very varied sources and in wildly different formats, and flow into their storage space at great speed. This market is addressed by products and services that help these large organizations not just cope with the deluge of data but extract the useful information contained in it.

Some of the players in the Big Data market must feel reminded of Molière’s Monsieur Jourdain who learnt that he had been “speaking in prose” before knowing what prose was: They had been serving the Big Data market before they knew it would be called that.

At Sinequa, we have been dealing with Big Data (in the above sense) for quite some time: Our Unified Information Access solution has been used by large enterprises and administrations to plough through billions of data base records, business transactions, and unstructured data of all sorts, like documents, emails, and social network data. Our semantic analyses and Natural Language Processing have served to make sense of this magma of data, and to create structure where there was none. All this in order to find sense in chaos. The challenge for us was to combine deep analysis with high performance in dealing with big volumes. We have invested a lot of energy – and dare I say, brain power – in our solution to satisfy big customers like Siemens, Crédit Agricole, Mercer or Atos in their quest to extract useful information from their big data volumes, relevant for their employees and customers.

The Grail of the Structured Universe

For many years, IT professionals have been chasing the grail of the “all-structured” enterprise data. This is how engineers were educated: you must structure the world to get a grip on it. If you need to search, you haven’t done your homework. For many of them, it is thus painful to give up on this goal – and on years of work and huge investments – in order to turn to search technology that can cope with the unstructured world much more easily and demanding an order of magnitude less time and money. Thus, search technology has evolved and is now used at the core of Unified Information Access platforms.

It’s not all or nothing

Now let’s not fall into the trap of claiming that Big Data is all about search and our kind of content analytics, just because we have been in Big Data up to our ears long before the people who invented the name. There are many approaches to Big Data and many useful tools and solutions to deal with it. But Unified Information Access platforms and semantic technologies are certainly part of any complete solution set. And our customers benefit from the fact that we have been in Big Data quite some time before the concept entered the hype cycle: Our solutions have matured over time.

Is Big Data a bubble that will burst?

If you link it inseparably to its name, “Big Data”, then it might well disappear. But the very real problems of Big Data sketched above will not go away. Heterogeneous and continuously changing big data volumes will increase rather than diminish.

See also

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