Sinequa Helps Box Customers To Be Information-Driven

Many customers that use Box for cloud content management are typically large, geographically distributed organizations. The four scenarios below describe common ways that Sinequa helps these customers leverage their enterprise information to become information-driven.

Increase the Signal, Decrease the Noise

noise

Customers who have migrated even a portion of their enterprise content to Box have made a significant step.  Workers in their organization can no doubt share and collaborate more easily than ever before; they no doubt have reduced email overhead; and they are probably working the way they want to given all of the friendly integrations with Box, including Outlook, Office365, Google Docs and the like.   However, being in the cloud does not automatically mean the valuable “signals” in your data rise above the “noise”.  Messy data migrated to the cloud is still messy data.  Sinequa helps workers quickly narrow in on the information and insights necessary to do their job effectively and with confidence.  By analyzing the content and enriching it using natural language processing and machine learning algorithms, Box users can quickly find the information and insights they need to be effective and responsive.

Connect Data

connect-data

Many Box customers run their business with other enterprise applications and information repositories, all of which contain data and content related to the information
stored in Box.  Sinequa brings advanced analytics and cognitive techniques to “connect” the data and bring context across all of the various enterprise sources, whether they be in the cloud or on premise.  By connecting the data, knowledge workers can better navigate and see how the data and connect fit together along topical lines, regardless of how many repositories make up the enterprise information landscape.

Identify Knowledge & Expertise

Screen Shot 2017-10-13 at 2.40.37 PMAs previously mentioned, many Box customers are large (or even very large) geographically distributed organizations with expertise in a wide variety of subject matter areas.  In these organizations, specific experts are difficult to identify given the size and distributed nature of the organization.  This is a modern problem that requires a modern solution.  As users store content and collaborate within Box, Sinequa’s advanced cognitive capabilities analyze that content to determine not only the areas of expertise across the organization but who the specific experts are and surfaces that information to end users.  This connects people across geographic and departmental boundaries, accelerating innovation and elevating the performance of the overall organization.

Leverage 360º Views

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Think of all the “entities” that are critical to Box customers running their business.  These business entities include customers, either specific individuals (B2C) or accounts (B2B), products, parts, drugs, diseases, financial securities, regulations, etc.  Having all of the enterprise data virtually connected by Sinequa makes it possibly to provide a unified “360º View” of these various entities to bring all of the right information to the right person at the right time.
As you can see, leveraging Sinequa to contextualize the information within Box and other enterprise repositories not only boosts productivity and keeps knowledge workers in the flow but has repeatedly proven to enhance customer service, improve regulatory compliance and increase revenue within different areas of the business.  Achieving these benefits positively impacts the bottom line and serves as validation that an organization has become truly information-driven.
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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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Relevance Feedback is Key to the Information-Driven Economy

As the data-driven age gives way to an information-driven economy where context is critical to surfacing useful insights from data, taking in relevance feedback from users, especially expert users, will play a major role in driving the benefits. This article explains the concept of a relevance feedback model and why you should care.

What is a Relevance Feedback Model?

Assume you ask a person or a system to provide you with information on a certain topic. There may be many facets to this topic, and you may get information from a whole range of different aspects. If you are working with that person or that system on a permanent basis, you may want to tell them that only certain aspects and hence certain kinds of information are relevant to you – in the hope of getting only the more relevant answers from them the next time you ask. You give the person or system “relevance feedback”.

Now let’s concentrate on a system, a cognitive information retrieval system or a “system of insight”. In that context, a relevance feedback model (RFM) is the capability of the system to take your relevance feedback and “internalize” it in order to tune the results of your future queries to what is most relevant.

The system performs and automates this task by adjusting weights attributed to certain terms and their equivalents (i.e. terms with the same or similar meanings) within the data it processes.
Imagine you asked “what do we have on MRO”, and you got information back on maintenance, repair and operations, but you told the system that you are only interested in anything pertaining to “Mars Reconnaissance Orbiter”. The next time you ask, you will get information only pertaining to the latter and possibly on related topics like Mars landing craft, automated robots for planetary exploration, etc.

RFM

For one person and one query, that seem rather simple. But now imagine, that you have tens of thousands of colleagues and thousands of topics to cover. That is when the RFM benefits from machine learning algorithms, not only to detect the preferences of each person but also of groups of people with similar interests, similarities in documents, etc. to spread the user relevance feedback to other documents, queries and people on an ongoing basis in an automated way.

Why use a Relevance Feedback Model?

A key benefit of a relevance feedback model is to enable users, in particular expert users, to affect relevance appropriate to their environment without the IT department having to implement rules for relevance according to specific user groups. It allows administrators to decide by configuration which specific users within the organization will contribute as well as the exact factor of relevancy improvement.

The relevance feedback model can also go a long way towards improving the human-machine interaction. As the relevance of certain content increases significantly due to relevance feedback, the user experience starts to feel much more “conversational” – i.e offering one to three suggestions as “answers” to a query – than a traditional search interface offering a list of documents in response to a query.

The RFM provides a way to discover from everyone’s experience the information that best answers the question. Take the real-world case of a customer service representative (CSR) seeking an answer to a customer’s product question using the product name or code. In this case, the CSR will obtain a diverse set of documents including parts catalogs, how-to information, product specifications, packaging information, marketing material, etc. All of this information is relevant but only some of it may help the CSR answer the customer’s question.

Thanks to the RFM, the CSR would immediately see information she has already viewed when she searched similar things in the past because the RFM takes into account the user’s “click actions” and applies a tiny relevance boost accordingly. Perhaps even more powerfully, the RFM will also modify the order of the results by observing (over time) what information other CSRs spend time to discover, even when they dive deeply into the results list for relevant information. Organizations striving to take full advantage of the RFM will configure it so that the experts’ interactions with the system provide bigger boosts for important content and even ban inaccurate information from appearing in results lists.

As you can see from the example above, the RFM provides a collaborative way to modify search result order. It is neither a tagging nor a classification approach, both of which can be done at indexing time (extracting metadata from source, entity extraction with Natural Language Processing) or afterwards (classification through ML algorithm like clustering, similarity computation, and so forth). The RFM arguably represents a smarter approach by directly incorporating human decisions when presenting information that will best address a user’s query.

As information-driven organizations strive for ever higher degrees of accuracy for end users seeking knowledge, the ability to leverage relevance feedback from users, especially expert users, automatically at scale becomes increasingly mission-critical for optimal business performance.

 

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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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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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