Cognitive Search & Analytics Transforms The Enterprise From Data Driven to Information Driven

**This article was originally published on AI Business magazine**Sinequa-AI-for-Business

The quest for actionable insights and answers from within vast troves of data is neverending within the modern enterprise. There’s good reason for that – it is the end goal of all information work – but the process is anything but optimized. Global analytics firm Forrester revealed as much in a 2017 report, which found that more than 54% of global information workers are interrupted from their work a few times or more per month by time wasted trying to gain access to information, insights, and answers.

It’s a problem that goes far beyond the limitations of conventional enterprise search technology – it’s a Sisyphean challenge, thanks to the sheer volume of data being created every single second.

“As organizations in data-intensive industries strive to create value, enhance customer experiences, and differentiate themselves from their competition, they are placing demands on their knowledge workers in unprecedented ways,” explains Laurent Fanichet, VP of Marketing for Sinequa. “Frequently, the data and knowledge they are looking for is isolated, segmented, and fractured. It’s difficult to surface the right information at the right time to see the patterns in the data.”

Fanichet has a clear grasp on the key problem Sinequa, an independent software vendor specialising in cognitive search & analytics, is trying to address. In its recent report, Forrester Wave: Cognitive Search and Knowledge Discovery Solutions, (Q2 2017), Forrester defines cognitive search 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’ – and, in the same report, go on to highlight Sinequa for the applications of their NLP technology in enterprise search.

The kind of cognitive search and analytics platform Sinequa offers, Fanichet explains, refers to an information system that is capable of automatically extracting relevant insights from diverse enterprise datasets for users within a specific work context. “Cognitive search brings the power of AI to enterprise search,” he says. “It helps organizations in data-intensive industries to become information driven.”

A recent IBM Watson report highlights the applications of cognitive search in the aerospace sector. One company uses these enhanced search capabilities “to improve supply chain visibility and reduce cycle time, saving millions of dollars on critical parts deliveries.” Furthermore, the system enables aircraft technicians to search through “reams” of maintenance records and technical documentation. “Now, if a worker needs to know what’s causing high hydraulic oil temperatures, the [cognitive solution] identifies historical cases with similar circumstances, finding patterns that point to the root cause of the overheating.” The report goes on to note that the solution in question saves the airline manufacturer up to $36 million per year.

Cognitive search and analytics likewise has its applications in the health and pharma sector. AI Business recently spoke to Karenann Terrell, GlaxoSmithKline’s first ever Chief Data and Analytics Officer, and former CIO of Walmart. She explained that a big component of what it takes to develop medicine can benefit from next-generation computing and machine learning. “Approximately 1/3 of the total cost of developing a medicine (>$2.5bn) is spent during the time it takes from identifying your target (the process in the body that you want to affect) to testing your molecule in humans for the first time,” she explained. “This process can take around five years. [GSK’s] goal with artificial intelligence is to reduce this time to just one year in future.”

“These are just a few of the many business areas where surfacing the information from within their data can drive better decisions,” Fanichet argues. He explains that cognitive search and analytics also have a range of powerful potential applications within customer service, enabling organizations to:

  • Provide personalized and highly relevant communication to their customers
  • Nurture customer relationships and prevent customer churn
  • Improve productivity, reduce operating expenses, and gain operational efficiencies
  • Minimize customer service representative turnover and knowledge loss

The Challenges Ahead for Cognitive Search

The potential use cases speak for themselves, but that doesn’t mean there aren’t challenges ahead for enterprises looking to incorporate cognitive search technology into their work. While working with clients, Fanichet explains, Sinequa helps them to understand that there are a set of common machine learning challenges along the path ahead. Expertise is often the first hurdle, but he maintains that there are many different types of AI implementation challenge. “Assuming that enterprises are able to resolve a dearth of expertise, there are still other challenges – most of which are specific to the type of AI being pursued.”

Take supervised machine learning, where the system learns to recognize patterns by observing ‘correct’ patterns provided by humans. “The greatest challenge is around providing sufficiently labelled training datasets from which the system can learn,” Fanichet explains. This is something Matt Buskell highlights in his ‘10 keys to AI implementation‘, recommending that following the initial loading of data and knowledge base, the system needs to go through a phase of refinement once the software has launched. “During this phase, things like gain and variance for Machine Learning, or intent training for NLP and maybe model refinement to cognitive reasoning need to be improved. During this phase, it is essential to carefully release the software and measure how well it’s performing over a 6-12 week period, at the least.”

Fanichet likewise highlights the obstacles unique to unsupervised machine learning, in which the system identifies existing patterns and a human determines their usefulness. “The greatest challenge is balancing the system’s need for sufficient data with the proper human guidance and interpretation needed to train the system,” Fanichet argues. This is as much an issue of skills and process culture as it is technical expertise, and is reflected in a recent Genpact survey of over 300 senior executives, which argues “AI cannot be implemented piecemeal. It must be part of the organisation’s overall business plan, along with aligned resources, structures, and processes.” Collaboration is therefore key.

Finally, there’s a need to formulate clear goals and outcomes, Fanichet says. “When pursuing reinforcement learning, where the system makes many attempts and learns from the outcome to take better actions, the greatest challenge is providing the system with a defined goal and sufficient practice in a dynamic environment so that the system can effectively learn from trial and error.”

With Sinequa, researchers, designers and engineers have immediate access to all the information needed to work productively.

With Sinequa, researchers, designers and engineers have immediate access to all the information needed to work productively.

Sinequa Brings the Power of AI to Enterprise Search

Fanichet believes Sinequa offer a range of unique intelligent capabilities within the analytics space:

  • Robust Indexing Engine: “If cognitive search was all about matching a keyword, a single index would suffice. The best results are obtained when multiple indexes are combined, each providing a different perspective or emphasis, providing a comprehensive overview of the information available. This provides the best possible understanding of the meaning it carries.”
  • Enterprise Grade: “Sinequa was designed from the start to support the complexities and multiple security layers of today’s enterprises. It was also designed to be immersed in diverse enterprise environments and can operate within the context of a specific industry and the language of the specific organization.”
  • Topically Aware: “Connecting information along topical lines across all repositories surfaces the collective expertise of the organization and makes it transparent. This is especially valuable in large organizations that are geographically distributed. By connecting people with expertise, the overall responsiveness of the organization increases.”
  • Natural Language Processing: “Sinequa’s world-class NLP offers automated language detection; lexical and syntactical analysis; and automatic extraction of dozens of entity types, including concepts and named entities like people, places, companies, etc. It also supports text mining agenda that is integrated into the indexing engine. This enables the extraction of virtually any function, relationship, or complex concept from the content.”
  • Machine Learning: “Sinequa leverages ML to enhance and improve search results and relevancy. This is done during ingestion but also constantly in the background as humans interact with Sinequa. It has become an essential part of the platform since it can handle complexity beyond what’s possible with rules.”
  • Well Designed User Experience: “Sinequa’s front-end serves as an intelligent agent that employees can consult for institutional knowledge that can be readily applied to the task or situation at hand. The experience is well designed in the sense that it is aesthetically pleasing, it is understandable in that it makes use of the user’s intuition, it is unobtrusive, and perhaps most importantly, it is contextual to the user’s goals.”
  • Ubiquitous Connectivity: “Sinequa’s product comes with over 160 ready-to-use connectors, all of which were developed in-house, thus ensuring consistency, quality control, and high performance.”

 

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IDC Highlights Sinequa Strengths and Leadership in Cognitive Applications Vendor Spotlight

IDC-Vendor-Spotlight-Cognitive-Applications-Sinequa-2017 (2)A new IDC report is recognizing Sinequa for our Cognitive Search & Analytics platform around critical technologies, including machine learning and advanced natural language processing. This Vendor Spotlight looks at how Sinequa leverages artificial intelligence and cognitive computing-based analytics to meet the needs of companies that are looking to address complex problems with easy-to-use, powerful solutions featuring simplified interfaces.

According to the report’s author David Schubmehl, Research Director for IDC’s Cognitive/Artificial Intelligent Systems and Content Analytics research, “The capabilities being offered by cognitive knowledge discovery systems, such as Sinequa, provide many opportunities for enterprises to innovate and advance their organization using approaches that were either not possible or not easily implemented several years ago. Within many enterprises, these opportunities are limited only by the imagination and creativity of those seeking to improve their business and information handling processes.”

The report states that Sinequa’s software provides organizations with real-time, relevant results from unstructured and structured internal data, and that the we are developing our Cognitive Search & Analytics platform on an extensive foundation of unstructured information access technologies that include advanced natural language processing capabilities in 21 different languages.

Schubmehl adds: “While Sinequa has offered a flexible information collection, access, and analysis architecture for many years, it has now built capabilities around cognitive technologies, such as machine learning, advanced natural language processing, improved relevance, and better decision support while offering strong user and data interaction capabilities.”

The advancement of natural language processing and increased maturity of machine learning are creating substantial demand for cognitive search and analytics solutions. At the same time, the growth of unstructured data and pressure to improve worker productivity makes it even more critical to find the right information at the right time. This report highlights the fact that Sinequa’s platform meets this demand and by combining our solution with human ingenuity, we can produce the best possible search and analytics results.

The full report is available here: https://www.sinequa.com/idc-vendor-spotlight-2017/

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Sinequa’s Big Splash at Bio IT World 2017

PHARMA CONNECTION
Sinequa has taken part for the 4th consecutive year in Bio IT World Conference & Expo on May 23-25 in Boston. We’ve been delighted to meet with our Biopharma and Life Science customers and partners at the show and share innovative use cases of our solution for the Pharma industry via live demos.
Bio IT Demo

“OPEN” LIVE DEMOS

Bio ITBio IT World conference is always for us a great venue to showcase our platform and present how leading biopharma organizations leverage our Cognitive Search & Analytics platform. This year, the attendees were very interested to see how Sinequa combines advanced Search, NLP and Machine Learning capabilities to extract relevant insight from vast structured and unstructured data silos.

 ALEXION’S CONTENT ANALYSIS PROJECT: MINING CONTENT FOR ACTIONABLE INSIGHT WITH SINEQUA

Alexion-Martin-Leach-Bio-IT-2017-SinequaIn our joint talk, our customer Alexion shared a testimonial on the implementation of Sinequa for their content analysis project. The presentation highlighted the technology and approaches they used with advanced data visualizations that help explain information sources. ICYMI – please feel free to get your copy here.

UNLIMITED THEATER PRESENTATIONS

Once again, we were very pleased to see the strong interest of many biopharma professionals toward Sinequa insight platform. Our team gave more than a hundred presentations and live demos in the Sinequa Theater Area where they explained a large panel of use cases including R&D Enterprise Search, Clinical Trial Data Discovery & Exploration, Key Opinion Leaders & Subject Matter Experts… .) BioIT17-Demo-TheaterWe hope you enjoyed the conference as much as we did and you could understand how our Cognitive Search & Analytics platform enable leading pharmaceutical organizations drive innovation, accelerate research and shorten drug Time-to-Market. We are already getting excited for next year’s edition! See you all in spring 2018!

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5 Ways Machine Learning Makes Your Search Cognitive

5 Ways Machine Learning Makes Your Search Cognitive

Artificial intelligence, machine learning, deep learning, cognitive computing…no doubt there is a lot of buzz out there but quite a bit of confusion too in terms of expectations and pre-requisites. We often hear customers and prospects say: “I want an AI assistant that tells me what to expect and what to do next,” or “It takes a lot of time to train an AI assistant to make him an expert in my field, doesn’t it? I want something that fits in my budget and I want it now.” Also, users often have many questions regarding the potential of machine learning for end users in their work environment. In this blog post, I’m sharing our initial thoughts about machine learning algorithms and how they empower cognitive search and analytics platforms to deliver better insights in relevant work context.

Machine learning algorithms often operate in two phases: the learning phase and the model application phase. In the learning phase, the data is analyzed iteratively to extract a model from manually classified data. While in the model application phase, the extracted model is applied to further inputs to predict a result.

Machine learning algorithms depend strongly on the quality of data, which is correlated to the quality of results. Cognitive search and analytics platforms can use natural language processing (NLP) and other analytics to enrich structured and unstructured data from different sources (entity extraction, detection of relationships within the data, etc.). This “data pre-processing” stage allows machine learning algorithms to start from enriched data and deliver relevant results much faster. These results continuously enrich the index/logical data warehouse and thus make it easier to answer users’ queries in real-time.

A performant cognitive search and analytics platform must integrate machine learning algorithms with its NLP and other analytics capabilities to deliver the most intelligent and relevant search results to users. Below are five ways machine learning makes search cognitive:

  • Classification by example – a supervised learning algorithm used to extract rules (create a model) to predict labels for new data given a training set composed of pre-labeled data. For example, in bioinformatics, we can classify proteins according to their structures and/or sequences. In medicine, classification can be used to predict the type of a tumor to determine if it’s harmful or not. Marketers can also use classification by example algorithms to help them predict if customers will respond to a promotional campaign by analyzing how they reacted to similar campaigns in the past.
  • Clustering – an unsupervised learning algorithm whereby we aim to group subsets of documents by similarity. Sinequa uses clustering when we don’t necessarily want to run a search query on the whole index. The idea is to limit our search to a specific group of documents in each cluster. Unlike classification, the groups are not known beforehand, making this an unsupervised task. Clustering is often used for exploratory analysis. For example, marketing professionals can use clustering to discover different groups in their customer/prospect database and use these insights to develop targeted marketing campaigns. In the case of pharmaceutical research, we can cluster R&D project reports based on similar drugs, diseases, molecules and/or side effects cited in these reports.
  • Regression – a supervised algorithm that predicts continuous numeric values from data by learning the relationship between input and output variables. For example, in the financial world, regression is used to predict stock prices according to the influence of factors like economic growth, trends or demographics. Regression can also be used to create applications that predict traffic-flow conditions depending on the weather.
  • Similarity – not a machine learning algorithm but simply a heavy computing process that helps build a matrix synthesizing the interaction of each sample of data with another one. This process often serves as a basis for the algorithms cited above, and can be used to identify similarities between people in a given group. For example, pharmaceutical R&D can rely on similarity applications to constitute worldwide teams of experts for a research project based on their skills and their footprints in previous research reports and/or scientific publications.
  • Recommendation –  one of the various use cases consists of merging several basic algorithms to create a recommendation engine proposing contents that might be of interest to users. This is called “content-based recommendation,” which offers personalized recommendations to users by matching their interest with the description and attributes of documents.

All the algorithms above need to be executed in a fast and scalable computing environment to deliver the most precise results. Currently, the Spark distributed computing platform offers the most powerful capabilities to execute machine learning algorithms efficiently. It is indeed designed to scale up from single servers to thousands of machines and it runs much faster than simple Hadoop frameworks.

Our recent contribution in the KM World Whitepaper “Best Practices in Cognitive Computing” highlights concrete use cases, describing how cognitive information systems are capable of extracting relevant information from big and diverse data sets for users in their work context. Get your copy here.

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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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