Cognitive Search Brings the Power of AI to Enterprise Search

Forrester, one of the leading analyst firms, defines Cognitive Search in a recent report¹ as: The new generation of enterprise search that employs AI technologies such as natural language processing and machine learning to ingest, understand, organize, and query digital content from multiple data sources. Here is a shorter version, easy to memorize: Cognitive Search = Search + NLP + AI/ML
Of course, “search” in this equation is not the old keyword search but high-performance search integrating different kinds of analytics. Natural Language Processing (NLP) is not just statistical treatment of languages but comprises deep linguistic and semantic analysis. And AI is not just “sprinkled” on an old search framework but part of an integrated, scalable, end-to-end architecture.

AI Needs Data, Lots of Data
For AI and ML algorithms to work well, they need to be fed with as much data you can get at. A cognitive search platform must access the vast majority of data sources of an enterprise: internal and external data of all types, data on premises and in the cloud. Hence the system must be highly scalable.

Continuous Enrichment
Cognitive Search uses NLP and machine learning to accumulate knowledge about structured and unstructured data and about user preferences and behavior. That is how users get ever more relevant information in their work context. To accumulate knowledge, a cognitive search platform needs a repository for this knowledge. We call that a “Logical Data Warehouse” (LDW).

The Strength of Combination
To produce the best possible results, the different analytical methods must be combined, not just executed in isolation of each other. For example, machine learning algorithms deliver much better results much faster if they work on textual data for which linguistic and semantic analyses have already extracted concepts and relationships between concepts.

Whitepaper-kmworld-07-2017Get your copy of the full paper here and learn more about current use cases of cognitive search and AI at large information-driven companies.

(1) Forrester Wave: Cognitive Search & Knowledge Discovery Solutions, Q2 2017
Read the full report on https://www.sinequa.com/forrester-wave-2017/

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Cognitive Search & Analytics Capabilities Out of the Box for Box Customers

In today’s digital age, leading organizations are looking for better ways to get more out of their data. They are choosing platforms that make every employee more connected, productive, and mobile-without compromising security. As companies adopt Box, providing intuitive information access and advanced search capabilities become increasingly important to end users. Using advanced Natural Language Processing (NLP) and Machine Learning algorithms, Sinequa’s Cognitive Search & Analytics platform enables users to search, analyze and gain valuable insights extracted from Box content repositoriesalong with on-premises enterprise applications, big data and cloud environments.

To build a sophisticated search and analytics engine is one thing, but to build such an engine that can preserve all the native security and permissions settings of connected repositories is another matter altogether. With Sinequa and Box connected, workers can search the Box environment (and all other data sources) while maintaining the native control settings of the respective platforms in which the data resides. This ensures that the granular security and permissions within Box are maintained in the Sinequa search interface, allowing individual users to seamlessly search and leverage only the content they are entitled to access.

The result is an environment unhindered by unnecessary, cumbersome processes for permission requests, or worse, unintentional viewing of unauthorized content. This allows users to quickly search and pinpoint the data, content, subject-matter experts, and topics they need in a fully secure and managed environment, where only the relevant data appears to each individual.
To learn more about the partnership between Box.com and Sinequa and the benefits of Cognitive Search & Analytics, you can download the complimentary research note “Sinequa partnership with Box amplifies cross-platform enterprise search and analytics” – April 2017 – from Paige Bartley, Senior Analyst at Ovum.

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Artificial Intelligence in 2017: Expands Capabilities, but Impacts the Workforce

Artificial-Intelligence-SinequaThe beginning of the new year is a good time to reflect on the events of 2016 and on their forebodings for the coming year and beyond.There has no doubt been a great deal of buzz around artificial intelligence (AI) this year. However, it’s difficult to sort through what’s hype and what’s not to determine where these technologies will actually take us in 2017. While we know the trend will continue in some form, what will be new or different next year? Here are some of my predictions: 

Artificial Intelligence is taking the industry by storm, and not just in “Westworld.” We’re entering a new phase of AI thanks to advances in computing power and volume of data. This has opened the door to solve computational problems on a scale that no human mind could approach – even in a lifetime. The result is that computers are now able to provide responses that aren’t dictated by a collection of “if A, then B” rules, offering results that can only be explained by saying that the computer “understands.” The benefit is that complex and time-consuming cognitive processes can now be automated, and we can do things at scale that were previously impossible because unlike humans, computers are not overwhelmed by volume.

We’re definitely headed in the direction of workforce displacement and I believe it’s going to happen quickly, as there are huge economic incentives to increase efficiency and to automate manual tasks. This will happen faster than we expect because we think linearly, while technology is advancing exponentially. We struggle with that perspective because it quickly outpaces what we can readily grasp, whether that be in size or speed, or both. This will bring additional challenges because the disruption will occur across the occupational spectrum (unlike the industrial revolution, which primarily impacted “low-skill” jobs). I don’t see any particular sector being hit by this tidal wave in 2017, but AI is a disruptor like we’ve never seen before and it will be here soon whether we are ready for it or not.

However, with this transformation, tasks that have been impractical because of the time/labor involved now become feasible, which means we’ll be able to do things we haven’t been able to do before. It will also free us from many mundane and repetitive tasks, enabling people to focus on new or more valuable activities. This will increase efficiency in the workplace as well as consistency, which will improve quality and safety. So while the workforce will look very different from how it looks today – certainly in 10 years and probably in five, AI and ML are going to greatly extend and expand our capabilities in ways that, for now, we can only imagine.

What are your predictions for 2017 and beyond? For a full list of my predictions on AI other topics such as machine learning and big data, check out my post in VMblog.

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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.

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Sinequa’s Cognitive Search & Analytics Platform Receives an Award from BigData Insider

Last night, Sinequa participated in the Readers’ Choice BigData Insider Award Gala in Augsburg, Germany. From April 19 to August 31, 2016, the readers nominated their IT Vendor of the Year across six portals: BigData insider, cloud computing Insider, Datacenter Insider, IP Insider, Security insiders and Storage insiders. In total, more than 34,000 readers voted for their favorite solutions.

As a result of the vote, Sinequa’s Cognitive Search & Analytics platform won the Silver Award in the “Big Data Management & System Tools” category. In the same category, Talend and SAS received respectively the Platinum Award and the Gold Award.

Big Data Insider Award 2016

“We are honored to receive this distinction resulting from the vote of the readers of BigData Insider comprised of customers and partners. This is a great recognition for Sinequa’s growing momentum in the DACH region,” said Laurent Fanichet, Vice President, Marketing at Sinequa.

Sinequa @ BigData-Insider-Awards-2016

Bild: Dominik Sauer / VIT
From left to right: Matthias Hintenaus, Sinequa, Andreas Gödde, SAS and Harald Weimer Talend.

 

 

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