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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Congrès Big Data Paris 2013 – Interview d’Hans Josef Jeanrond

Lors du congrès Big Data 2013 qui s’est tenu les 3 et 4 avril, Hans Josef Jeanrond, Directeur Marketing de Sinequa, a présenté l’intérêt des solutions de search et  des plateformes d’Accès Unifié à l’Information pour traiter et valoriser les données non structurées contenues dans le Big Data.

Retrouvez l’interview :
Big Data 2013

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On the road to Big Data

The Documation trade fair and conference was a resounding success for Sinequa: Visitors came with more concrete projects than in previous years. Conferences were very well attended, and the roundtable discussion on Big Data with Sinequa and partners was crowded, even standing room overflowing into the aisles.

Three major players EMC²; CGI Business Consulting and Sinequa focused on ROI of Big Data projects. They form an alliance to address the challenge of value creation in the various areas and phases of real life Big Data projects.

Philippe Nieuwbourg, specialized journalist, lecturer and book author on Big Data moderated the discussion and contributed his own experience. He provoked people to breathe life into “data cemeteries” that have accumulated in many companies, and extract value from it ion realistic projects.

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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 www.sinequa.com/en/page/solutions/big-data.aspx

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