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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Finance & Banking: Collecting High Value from Cognitive Search and Content Analytics

In today’s rapidly changing technology climate, financial services customers expect their banks, insurance companies and asset management providers to know them. It’s expected that providers know about recent transactions, account details, and even anticipate future needs. But this can be challenging with the numerous silos of content in which customer data resides. With a 360° view of the customer through cognitive search and analytics, financial services organizations can deliver the customer experience that provides more value, drives increased sales and meets rapidly evolving customer expectations.

As an example, Crédit Agricole, one of the largest banks in the world, has launched an ambitious project to deliver a new digital workplace, offering a 360° view of customers to its representatives as well as to the customers themselves. The bank’s more than 60,000 internal users will be able to know the exact situation of the customer in front of them, to find the most relevant offerings for the customer and the corresponding procedures. The customers connecting to the bank’s online service also find themselves in a similar “work place” that allows them to know the current status of all their business with the bank including accounts, contracts, records of transactions, share portfolio and share prices, banking charges, additional services, and more.

This comprehensive “work place” is created through inclusive enterprise search and analytics of all of the bank’s data sources. From CRM and account transaction applications to external sources such as stock exchange data, corporate websites, financial and trading news-feeds, the bank can provide a complete customer picture from which to deliver robust service, new offerings and build increased customer satisfaction.

Cognitive Search and Analytics platforms index all the structured and unstructured data sources and create a semantically enriched index, optimized for performance in dealing with user search queries.  In fact, some search and analytics solutions even offer as many as 150 smart connectors, ‘out of the box,’ that can seamlessly connect multiple sources of data.  These companies integrate your company’s and industry specific dictionaries allowing the information to be integrated and indexed, putting your specific knowledge ‘under the hood’ of one platform – making it an intelligent partner for workers looking for business insight at their digital workplace.

See here how Sinequa’s Cognitive & Analytics platform brings business value to Finance and Banking organizations.

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5 Ways Finance & Insurance Organizations Take Advantage of Cognitive Search and Analytics

Sinequa-Cognitive-Search-Analytics-Finance-Insurance

Leading banks, financial institutions, and insurance companies are known to be data-intensive organizations and depend more than ever on data to make well founded decisions. They also rely on data to extract insights on customers that can result in increasing revenue streams. In order to address these challenges they need to be agile, innovative and responsive to evolving customer needs. Find here five ways Financial organizations leverage their big data using cognitive search and powerful analytics:

  1. Get Actionable insight from the most diverse data sources: the objective is to analyze, structure and categorize all available data to get intuitive and unified information access across all internal and external data sources, including customer contracts, insurance claims, payment history, email communications, CRM data, company policies and processes and more. Employees must be able to access relevant information without having to know where data is stored, in which format or how to access it.
  2. Obtain instant 360° views of customers, portfolios, investment targets, contracts, financial performance, and any other subject linked to the business of an organization. People can do so across all business units – from banking to insurance, leasing, property management, asset management, and beyond. Only an efficient “insight engine” – as some leading analysts call cognitive search and analytics platforms – can provide rapid 360° views to users without the need to change existing applications.
  3. Detect fraudulent activities & prevent money laundering: banks and insurance companies face the daunting task to accurately and rapidly identify fraud by analyzing Big Data volumes. To face this challenge, a cognitive insight platform enables the detection of “unusual” data patterns by predictive machine learning algorithms and the mapping of relationships between people, bank accounts, credit card numbers, financial transactions, and many other data types. To uncover patterns in behavior, analysts use a combination of interactive charts, timeline analyses, tables and relationship maps.
  4. Reduce customer churn: the combination of cognitive search and powerful analytics help organizations improve customer retention. Here, Natural Language Processing with text mining agents plays a major role in detecting relevant information in customers’ data and behavior, for example by analyzing information requests and navigation patterns on the company’s website. Predictive Analysis also plays a role in reducing churn rates. For example, machine learning algorithms help detecting patterns and trends in customers’ transactions which can identify them as “high-risk” potential defectors.  Companies can propose tempting offers to potential churners that prove usually quite effective in retaining them. This also reflects in staggering yearly ROI figures, up to tens of millions of dollars.
  5. Recommend up-sell and cross-sell offers: Once customer data is collected and analyzed across all available channels, additional functionalities can be added with marginal effort. Machine learning algorithms, such as “collective filtering and recommendation”, can then be used to optimize marketing campaigns, improve up-selling and cross-selling. Indeed, on top of the 360° view of customers, we can use machine learning algorithms to recommend products and/or services that are relevant to customers, based on deep analytics of contents and customers’ behavior data.

In the fast-evolving world of Finance & Insurance, it becomes increasingly important for these organizations to capture, process and analyze massive amounts of structured and unstructured to make better business decisions while better serving their customers. A Cognitive Search & Analytics platform that delivers superior agility, flexibility and scalability and turns data into business insight can bring significant value.

Interested to learn more about this platform for your organization? We’d love to help.

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