As organizations strive to create value, enhance customer experiences and differentiate themselves from their competition, they place tremendous demands on their R&D departments. From accelerating the delivery of innovative products to improving compliance to understanding consumer demands and improving responsiveness to gain and keep customer trust, R&D has a lot on its plate.
Global competition, narrow margins, higher product development costs and tenuous holds on exclusivity, drive organizations to push innovation, seek cost cutting strategies and go to market as quickly as possible. Consumer demands change frequently while regulatory and compliance standards become even more stringent. Organizations must keep up, and the pressure on R&D never stops. R&D is the epicenter of an 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.
R&D thrives on information: customer information, expert information, product information, scientific information, market information, regulatory 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 easy feat in today’s world where we are inundated with data — more data, more opportunities and more challenges. As a result, many companies depend on machine learning solutions to harness insightful, high-quality information and fuel innovation within their product and solution portfolios.
Here are six examples of how R&D departments are leveraging machine learning to improve their effectiveness and create competitive differentiation for their organizations:
Machine learning algorithms objectively connect researchers and developers based on the work they do, which at a minimum results in greater efficiency and optimally streamlines the path from academic innovation to product development.
Machine learning techniques are the only effective means currently available to adapt security countermeasures based on historical hacking techniques to deal with sophisticated cybersecurity threats aimed at stealing trade secrets and intellectual property.
Machine learning algorithms are revolutionizing product and service quality by determining which factors impact quality enterprise-wide and to what extent. For example, machine learning can yield much greater manufacturing intelligence by predicting how quality and sourcing decisions contribute to greater Six Sigma performance within the Define, Measure, Analyze, Improve, and Control (DMAIC) framework.
Cognitive search and analytics solutions powered by machine learning amplify the expertise of R&D departments by surfacing insights from data across the enterprise, regardless of location and format. From a single, secure access point, these solutions enable R&D professionals to unlock relevant and timely product research from internal and external sources that helps make informed decisions.
Healthcare prediction and prevention are being revitalized and reinforced by machine learning. The pace of machine learning-powered prediction and prevention research is now faster than that of research that does not utilize the technology. From patient wellness scores to risk scores, machine learning is transforming the healthcare landscape.
Open source software libraries like Google’s TensorFlow are enabling researchers to leverage machine learning for everything from language translation to early cancer detection to preventing blindness in diabetics.
Machine learning can leverage and build on relevant customer and market information to give R&D organizations insight and the ability to react quickly to demands. Teams are utilizing this technology to eliminate data silos and deliver increasingly 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. This in turn enables forward-thinking R&D departments that thrive on continuous product improvements and introductions to amplify the collective expertise of the organization.
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.
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.
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.
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.
“OPEN” LIVE DEMOS
Bio 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
In 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… .) We 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!
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.