What is Cognitive Search? How a New Generation of Platform is Transforming Enterprise Insights?

Despite the effort from technology vendors to deliver relevant, contextual, and actionable insights with their applications, most organizations have been slow if not reluctant to embrace these advances in search-driven experiences. In fact, a lot of companies have been burned by their past enterprise search experiences.

The good news is that something is shaking the world of Enterprise Search – some would say ‘finally.’ New industry investments and R&D effort are changing the search experience to provide more relevant results and deeper insights to users in their work context.

As we enter the era of “cognitive computing,” new search solutions combine powerful indexing technology with advanced Natural Language Processing (NLP) capabilities and Machine Learning algorithms in order to build an increasingly deep corpus of knowledge from which to feed relevant information and 360° views to users in real-time. This is what leading analyst firms call “Cognitive Search” or “Insight Engines.”These cognitively-enabled platforms interact with users in a more natural fashion, learn/progress as they gain more experience with data and user behavior, and proactively establish links between related data from various sources, both internal and external.

In a recent brief, Forrester defines cognitive search as:

“Indexing, natural language processing, and machine-learning technologies combined to create an increasingly relevant corpus of knowledge from all sources of unstructured and structured data that use naturalistic or concealed query interfaces to deliver knowledge to people via text, speech, visualizations, and/or sensory feedback.”

How does cognitive search work to deliver relevant knowledge?

  • It extracts valuable information from large volumes of complex and diverse data sources. It is crucial to tap into all available enterprise data whether internal or external, both structured and unstructured, to provide deeper insights to users in order for them to make better business decisions. Cognitive search provides this connection to provide comprehensive insights.
  • It provides contextually and relevant information. Finding relevant knowledge across all available enterprise data requires cognitive systems using Natural Language Processing (NLP) capable of “understanding” what unstructured data from texts (documents, emails, social media blogs, engineering reports, market research…), and rich-media content (videos, call center recordings..), is about. Machine Learning algorithms help refine the insight gained from data. Trade and company dictionaries and ontologies help with synonyms and with relationships between different terms and concepts. That means a lot of intelligence and horse power “under the hood” of a system providing “relevant knowledge” or insight.
  • It leverages Machine Learning Capabilities to continuously improve the results relevancy. Machine Learning algorithms (amongst the most popular ones: Collaborative Filtering and Recommendations, Classification by Example, Clusterization, Similarity calculations for unstructured contents, and Predictive Analysis) provide added value by continuously refining and enhancing the search results in an effort to provide the best relevancy to users.

Thanks to new technology advancements, cognitive search brings to data-driven organizations a new generation of search enabling them to go far beyond the traditional search box, empowering its users to get immediate and relevant knowledge at the right time on the right device.

+1Share on LinkedInShare on Twitter

Join Sinequa at Bio-IT World Conference & Expo 2016 (Booth #421)

Sinequa will present and exhibit at Bio IT World Conference & Expo that will take place on April 5-7 at the Seaport World Trade Center in Boston, USA.

Sinequa For Life Sciences

We invite you to stop by the Sinequa booth #421 to discuss innovative use cases of our solution for the Pharma industry – Sinequa For Life Sciences - and see how our customers raised their competitiveness by implementing our Big Data Search and Analytics solution across the most diverse data silos.

  

Also, make sure to book your agenda and attend our presentation in the Bioinformatics Track #5:

Wednesday, April 6, at 2:55-3:10 PM

“Increasing the Competitiveness of Pharma Companies:
Real Time Search and Analytics Across Structured & Unstructured Data”

Speaker: Xavier Pornain, Vice President of WW Sales & Alliances

Book your agenda

+1Share on LinkedInShare on Twitter

Digital Workplace: Digitized Chaos or Information at your Fingertips?

Digital Workplace

You have a digital Workplace, of course. Does it fulfil all the expectations you had when you went “all digital”? Or is getting at the right information still too complex, too cumbersome and time-consuming? Companies often need specialists to extract information for each specific work context. That is not agileand it’s in total contradiction with the modern digital workplace principles promising “information self-services”.

In decent Digital Workplaces, you find information, not data! And this information must be comprehensive and relevant, and delivered instantly, since in the era of digital business models, there is no time to sift through tons of data when you need information. At best, information is delivered proactively, in order to gain time, increase productivity and improve decision making.

Now, many of you may be wondering: “how to create value from data in increasingly digitalized businesses?”; “how to extract relevant information from big and diverse data and then, deliver precise and relevant information to each and every person at the right time?” This might seem like an elusive goal as we create more data than ever in digitalized workplaces, potentially increasing chaos every day.

To overcome these challenges, we need to simplify the digital workplace for users. This requires high performance systems of data retrieval, analytics and information delivery.

In the past, organizations have installed data warehouses and search engines to help people find relevant data. Many of these never delivered on the expectations – and the needs – of users and organizations. They were lacking in analytical power and in performance when faced with large and growing amounts of heterogeneous data and with the need to combine analysis of structured and unstructured data, including most prominently natural language processing (NLP) for a while range of languages.

The new generation of enterprise search platforms have evolved into whatGartner calls “Insight Engines”.

According to this leading analyst firm, 25% of large organizations will have an explicit strategy to make their corporate computing environment similar to a consumer computing experience by 2018; 46% have a digital workplace initiative underway and 4% have appointed a Digital Workplace leader.

As usual, the bright new digital future cannot be “bought” with a new piece of technology. It requires a change of mind-set and a change in corporate culture.  Nevertheless, be aware that the digital workplace technology you select can either facilitate or impede adoption and change of culture.

Gartner specifies these Digital Workplace Principles : Contribution/ Enthusiasm; Digital Dexterity; Autonomy

#1 Contribution/ Enthusiasm: By promoting employee engagement, digital workplaces create a workforce that makes discretionary contributions to business effectiveness

#2 Digital Dexterity: Creating a “consumer-like computing experience” to enable teams to be more effective

#3 Autonomy:  Exploiting emerging smart technologies and people-centric design to support dynamic non-routine work

To step into the era of the reimagined Digital workplace you need the “Insight Engine” to increase your employees’ effectiveness and productivity, to help them better serve their customers while enjoying their work environment.

Sinequa has been mentioned next to Apple, IBM and the likes in the latest Gartner’s Hype Cycle Content Management/Digital Workplace 2015 Reports – for proactive search capabilities that are mandatory for a transition to Digital Workplaces.

Take a look at our presentation in the Gartner Digital Workplace Summit last September in London:

 “The Re-Imagined Digital Workplace: Where is the Beef?

+1Share on LinkedInShare on Twitter

4 Ways Big Data Analytics Transform Intelligence Data into Actionable Insights

Intelligence and law enforcement agencies experience an enormous pressure to identify threats across multiple data sources. These Defense and Security organizations require real-time information at their fingertips for quick analysis and decision making.

Big Data Search and Analytics for Defense and Security

Big Data Search and Analytics for Defense and Security

Here are 4 ways Big Data Analytics can transform intelligence data into actionable insights:

  • Monitoring of Social Media interactions

Intelligence agencies must anticipate any kind of cybercrime and attacks. Social media monitoring enables them to collect and analyze relevant and targeted information relating to counter-terrorism and criminal networks. Reacting at the right time is a major challenge for these organizations that use OSINT (Open-Source Intelligence) to find, select and acquire information from various sources online (social networks, forums, blogs, websites, videos etc.) in order to get real-time insight on potential threats, generate reports and prevent any kind of attacks. In response to this challenge, intelligence agencies must invest in a cutting-edge technology that brings together data search and collection across multiple online sources and a deep content analytics of unstructured textual data that are flooding the web.

  • Detection of money laundering, fraud & terrorist financing

Money laundering is a key component of most organized crime. Terrorist networks continue to be funded through money laundering schemes that need to be identified. A powerful Big Data Search and Analytics platform enables agents to pinpoint suspect money transfers, accounts and networks of individuals involved in sophisticated money laundering schemes through a highly dynamic approach to relationship mapping.

  • Identify and correlate threats & cyberattacks

Investigators face the daunting task to accurately identify fraud and cyberattacks across big data volumes within shrinking windows of time. To prevent threats and cyberattacks before they happen, intelligence agencies must be able to deliver dynamic relationship mapping to connect people, bank accounts, credit card numbers, financial transactions, and many other data types. They need a scalable platform based on advanced Search and Natural Language Processing capabilities. Analysts uncover patterns in behavior using a combination of interactive charts, timeline analyses, tables and relationship maps.

  • Solve crime cases with powerful search capabilities

Law enforcement professionals need effective crime analysis tools to easily reveal networks of criminal activity. The sophistication of criminal behavior has increased across virtually all areas, including cybercrime, identity theft, gang activity, fraud and narcotics. These tools must provide the ability to search and analyze a wide range of sources of both structured and unstructured data to gain meaningful insights using connections between people, phone calls, license plates, addresses, properties or other forms of data.

To learn more – please download the brochure “Sinequa for Defense and Security”.

+1Share on LinkedInShare on Twitter

What is Natural Language Processing (NLP)? Why Should You Care About It?!

Linguistic and deep semantic analysis cope with the biggest challenge of Big Data analytics: textual data or human generated data. Semantic analysis helps in understanding your questions as well as the meaning of texts beyond simple keyword matches. It also helps to cluster information via business-relevant filters to dramatically improve discovery of relevant information.

What would you use NLP for?

To get relevant information, all relevant information, nothing but relevant information… if you want to leverage all your enterprise data, you will definitely need it!

I can hear you say: “But isn’t NLP just text extraction, word separation, language identification? … many search engines do that, why would I need Sinequa?”

Because we are the experts when it comes to NLP! And here are the main four reasons that make Sinequa’s Linguistic Analytics stand out:

  • Automatic concept and entities extraction from linguistic patterns and an out-of-the-box facets navigation
  • Strong text mining capability with “Part of Speech tagging”
  • “Semantic correlations”, for example co-occurrence of concepts within a single phrase.
  • Easy integration of “enterprise knowledge”: dictionaries, ontologies, etc.

All of these features help you extract value from textual data, such as projects reports, clinical trial reports, publications, patent filings, and emails. In many companies this data contains a wealth of information that has not been “codified” in sheer figures. In fact, it often contains the reasons why the figures are what they are.

And you want this extraction to be fast and easy, such that your employees find relevant information in their work environment without having to know where it comes from and in what format it is in.

If you are in a pharma company looking for information on a particular drug, you want to use your brand name but get results on all synonyms, the active molecule in that drug, on other drugs with the same active molecule, on diseases treated with these drugs, on experts working on these drugs and molecules – all without having to specify any synonyms or relations in your request for information.

In a globalized world, you will want the system to “speak your language”. I.e. you need a multilingual system. Sinequa offers NLP in 20 different languages, including “difficult” ones like Chinese, Japanese, Korean or Arabic.

+1Share on LinkedInShare on Twitter