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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Cognitive R&D – Leveraging Cognitive Search and Analytics to Amplify Research and Development Expertise

Forces of global competition, narrow margins, higher product development costs, and tenuous exclusivity holds drive organizations to push innovation, seek cost cutting strategies, and go-to-market as quickly as possible. Demands change frequently while regulatory and compliance standards become even more stringent. Organizations must keep up, and the pressure on research and development (R&D) never stops. R&D is the critical driver within the organization, whether within a large aircraft manufacturer or a leading automobile company looking to develop cutting edge products and services or a pharmaceutical company accelerating time-to-market for new drugs or a CPG company reinventing waning products. R&D thrives on information: customer information, expert information, product information, scientific information, market information, and competitive information.

To be at the forefront of innovation, R&D departments need complete visibility into both new and historical information across the entire enterprise as well as access to research from external public and premium information services. This is no simple task in today’s world where we are inundated with data — more data, more opportunities and more challenges. As a result, many companies depend on Cognitive Search and Analytics (CS&A) solutions to harness insightful, high-quality information and fuel innovation within their product and solution portfolios.

THE PRESSURE ON R&D

As organizations strive to create value, enhance customer experiences, and differentiate themselves from their competition, they have placed demands on their R&D departments to:

  • Accelerate delivery of innovative products to market
  • Optimize and manage available resources and knowledge while leveraging intellectual property
  • Devise methods to reduce product development costs and eliminate re-work
  • Improve product compliance both internally and externally and deliver safer, compliant products faster
  • Understand consumer and market demands and improve responsiveness
  • Resolve product issues quickly and efficiently to gain and keep customer trust

To meet these demands, R&D depends on complex scientific and engineering content that contains implicit conceptual relationships that can and should be semantically linked to simplify access to the knowledge embedded in that content.

HOW COGNITIVE SEARCH AND ANALYTICS HELPS

Cognitive Search and Analytics solutions amplify the expertise of R&D departments by surfacing insights from data across the enterprise, irrespective of location and format. From a single, secure access point, these solutions enable R&D professionals to unlock relevant and timely product research that helps make informed decisions. In addition, these capabilities are not limited to internal information; users can quickly access information from external Web sites and other applications, deriving relevant information and seamlessly integrating with internal enterprise information.

Cognitive Search and Analytics solutions enable enterprises to maximize the value of their intellectual property. Powerful search relevance and navigation capabilities enable researchers to find valuable pieces of past research and even parallel work going on without each group knowing about the other — eliminating duplicate work, reducing time spent in trials and shortening development cycles. These solutions allow employees to tag, bookmark and comment on documents, enabling collaboration and making teams more innovative, efficient and productive. Surfacing this existing knowledge enables workers to leverage the past work of distant or former researchers to benefit future research. Dynamically delivering relevant information, surfacing knowledge and enabling collaboration can decrease R&D costs significantly. Because R&D departments need to comply with a myriad of complex regulations, they need to be aware of relevant regulations without having to sift through the myriads themselves. This visibility enables R&D to stay abreast of regulatory mandates and efficiently manage compliance. Organizations can also leverage these solutions to send alerts to employees when there are new policy and compliance changes so that relevant R&D stakeholders are immediately notified.

Managing and maintaining product specifications is a critical function within R&D. Cognitive Search and Analytics solutions can access virtually any data source and expose changes when information is deleted or becomes outdated. These solutions can alert workers when any new information is created that impacts their specific process in the development cycle. These solutions also track and respect the access permissions accorded by each target application; only those with the correct privileges can access restricted information. Cognitive Search & Analytics solutions give researchers clear insight into product requirements and enable them to collaboratively develop safer, higher quality products that meet regulatory requirements.

RAPID RETRIEVAL OF RELEVANT INFORMATION MAKES THE DIFFERENCE

Extracting relevant information from vast and complex data volumes is a challenge that requires a sophisticated and scalable solution. The Sinequa Cognitive Search and Analytics platform handles all structured and unstructured data sources and uses Natural Language Processing (NLP), statistical analysis and Machine Learning (ML) to create an enriched “Logical Data Warehouse” (LDW). You can think of it as a repository of information about data and about relationships between data, people, concepts, etc. This LDW is optimized for performance in delivering rapid responses to users’ information needs. Users can ask questions in their native language or ask that relevant information be “pushed” to them in a timely fashion when it emerges. More than 150 connectors ready for use “out of the box” make the process of connecting multiple data sources fast and seamless. Company and industry-specific dictionaries and ontologies can be easily integrated, putting domain-specific knowledge “under the hood” of the Sinequa platform, making it an intelligent partner for anyone in search of relevant information.

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.

The advanced semantic capabilities within Sinequa’s platform provide strong relevance in 21 different languages to assist organizations with even the most geographically and linguistically diverse workforce.

REAL-WORLD EXAMPLE: AMPLIFYING BIOPHARMA EXPERTISE

Consider one of Sinequa’s biopharma customers, a research-intensive organization dealing with a vast number of highly technical documents, produced both in-house and externally. The information in these documents varies according to the field of its origin – e.g. medical, pharmaceutical, biological, chemical, biochemical, genetic, etc. – and may deal with diseases, genes, drugs/active agents, and mechanisms of action. A lot of the information is textual, but there is also structured information, like molecular structures, formulae, curves, diagrams, etc. The volume of this information is on the order of magnitude of about 500 million documents and billions of database records.

Now consider the more than 10,000 R&D experts within the organization trying to leverage this information daily. They need to be able to ask topical questions, find relevant people and documents, and explore the vast information landscape to discover knowledge. The Sinequa platform supports this by plowing through the hundreds of millions of documents and equally large amounts of structured data, analyzing the data, analyzing the natural language user queries, and classifying results by category in real time. With the data tamed and enriched, it is presented to the user via a simple, intuitive interface with faceted navigation aids that allow the user to filter results further based on structural attributes that are either explicit or were intelligently derived by the system. The interfaces, also referred to as search-based applications (SBAs) are configured to expose functionality that is very specific to an R&D expert, aligning the solution with the goals of the user.

The Sinequa solution has proven to be very valuable to the customer in question, putting both internal and external research–related information that scientists need for research, development, and decision making into a single virtual repository with advanced navigation and retrieval capabilities. It has also proved to be very beneficial to teams of research and development contributors by allowing experts around the world to collaborate more easily through a single research application. Features such as navigation by topic across multiple repositories, de-duplication of similar documents, and improved research capabilities have all made knowledge workers more efficient and innovative.

CONCLUSION

Sinequa’s Cognitive Search & Analytics platform leverages relevant customer and market information to give R&D organizations insight and the ability to react quickly to demands. Teams utilize this platform to collaborate and share information. Sinequa effectively eliminates data silos and delivers relevant information from data to users in their business context, such that they can make better decisions, drive innovation, reduce risk, and be more efficient, which in turn enables forward-thinking R&D departments that thrive on continuous product improvements and introductions to amplify the collective expertise of the organization.

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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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Gartner Named Sinequa a Leader in Its Magic Quadrant for Insight Engines

As the CEO of Sinequa, I am proud that Sinequa was recognized as a leader in the recently released Magic Quadrant for Insight Engines 2017. Being a Gartner Leader, once again, underlines our continued progress that has led to this renewed leadership position in a Gartner Magic Quadrant. (We have previously been positioned as a leader in the Magic Quadrant for Enterprise Search.)  Gartner selects leaders for their “Completeness of Vision” and their “Ability to Execute” Good to see that others find our vision convincing and believe in our ability to realize it!

More reassuring still is the testimonial of our customers that led Gartner to state that “reference customers regarded Sinequa’s roadmap and future vision for its software to be particularly attractive. All indicated that those were significant reasons for choosing the software.”

As an established Cognitive Search platform, we’re continuing to evolve our vision and invest in enabling the largest organizations such as Airbus, AstraZeneca, Bristol Myers Squibb, Credit Agricole, and Siemens around the globe to get more value from their ever growing and diverse Enterprise data, as well as broadening the impact of search and analytics within the digital workplace of their employees.

According to Gartner:

“Insight engines apply relevancy methods to describe, discover, organize and analyze data. This allows existing or synthesized information to be delivered proactively or interactively, and in the context of digital workers, customers or constituents at timely business moments.”

Gartner-Magic-Quadrant-for-Insight-Engines-2017-Sinequa

Get your copy of the full report here and see why Sinequa is among the 3 leaders over the 13 vendors who participated in this Magic Quadrant.

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Machine Learning Becomes Legit, but Not Mainstream in 2017

ML-Sinequa-Predictions-2017

There has been a lot of hype around machine learning lately. Over the past decades, we’ve heard about various concepts around machine intelligence that in most cases didn’t get anywhere. But more and more frequently, organizations are learning how to bring together all the ingredients needed to leverage machine learning, and there is a simple reason for that: according to Moore’s law, the performance of microprocessors has increased since 1980 be a factor of more than 16 million! A program that ran on a 1980 computer for more than half a year today delivers its results in one second!

That is why I think Machine Learning will be the story for 2017. We’ll see it move from a mystical, over-hyped holy grail, to more real-world, successful applications. Those who dismiss it as hocus-pocus will finally understand it’s real; those who distrust it will come to see its potential; and companies that apply ML to appropriate use cases will achieve real business benefit without the high cost of entry that was common in years past. In 2017 it will be clear that it has a credible place in the business toolkit.

The four necessary enablers for machine learning – huge parallel processing resources, cheap storage, large and appropriate data sets, and accessible machine learning algorithms – are all now mainstream. Most large organizations have readily-available access to all these components (appropriate data sets are potentially the only open question, as they are always business- and use-case-specific), which makes machine learning a real possibility to reduce risk, increase customer satisfaction and loyalty, create new business models, identify patterns, and optimize complex systems.

One area where machine learning is growing rapidly and already showing success is for cognitive search and analytics applications. It won’t take over core algorithms anytime soon, but ML is already supplementing and enhancing search results based on user actions and smart analysis of content.

I don’t foresee machine learning achieving “mainstream” status in 2017, but it will within the next few years because the technology is advancing exponentially, quickly enabling its use in broader contexts.

For more on my complete prediction on machine learning, check out this article in Virtual Strategy Magazine.

 

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