Sinequa Snags Three Key Industry Award Wins in September

Sinequa Industry Recognition - September 2018

We’re off to a busy September here at Sinequa! We’re excited and humbled to have received a few different awards for our Cognitive Search & Analytics Platform and company as a whole this month. Sinequa recognition has included the following awards from leading industry publications:

KMWorld Trend-Setting Products 2018

KMWorld’s 2018 list of Trend-Setting Products features not only emerging software directed toward human-like functionality but also more traditional offerings impressively refined. It encompasses AI, machine learning, cognitive computing and the Internet of Things, as well as enterprise content management, collaboration, text analytics, compliance and customer service. Read more.

DBTA’s Cool Companies in Cognitive Computing for 2018

DBTA and Big Data Quarterly presented the 2018 list of Cool Companies in Cognitive Computing to help increase understanding about the important area of information technology and how it is being leveraged in solutions and platforms to provide business advantages. Read more.

Datanami Readers’ Choice Award Winner

Sinequa won the Readers’ Choice – Best Big Data Product or Technology: Machine Learning category.

The Datanami Readers’ and Editors’ Choice Awards are determined through a nomination and voting process with input from the global big data community, as well as selections from the Datanami editors, to highlight key trends, shine a spotlight on technological breakthroughs and capture a critical cross-section of the state of the industry. Read more.

Looking forward to continuing the momentum for the rest of the year!

For more information on Sinequa’s cognitive search and analytics platform visit: https://www.sinequa.com/insight-platform-2/

+1Share on LinkedInShare on Twitter

Ferring Pharmaceuticals selects Sinequa and Atos to boost Global Cognitive Search Capabilities for R&D

ferring-logoAs a testament of Sinequa’s fast growing footprint among leading life sciences organizations, we are very excited to announce that Ferring Pharmaceuticals selected Sinequa and Atos to boost its global cognitive search capabilities. Sinequa’s Cognitive Search & Analytics solution was recently deployed at Ferring Pharmaceuticals with Atos as the consulting and integration partner to empower the organization’s Global Pharmaceutical R&D group to look deeply into vast scientific research data sets in order to generate new insights and accelerate innovation

Headquartered in Switzerland, Ferring Pharmaceuticals is a research-driven, specialty biopharmaceutical group active in global markets. A leader in reproductive and maternal health, Ferring has been developing treatments for mothers and babies for over 50 years. Today, over one third of the company’s research and development investment goes towards finding innovative treatments to help mothers and babies, from conception to birth. Ferring has its own operating subsidiaries in nearly 60 countries and markets its products in 110 countries.

In today’s world, especially in the life sciences industry, it is impossible for humans alone to search, process and analyze all the world’s available scientific and research data, Sinequa’s Cognitive Search & Analytics platform  makes this scientific knowledge accessible any time by any given researchers. As Sinequa continues to expand its footprint in this very competitive industry, we are very pleased to count Ferring Pharmaceuticals among our customers. Together with our partner Atos, we are committed to help Ferring improve insights and facilitate innovation.

- Stéphane Kirchacker, vice president Sales, EMEA at Sinequa

test-tubes

Atos, Sinequa’s strategic global premium partner was selected to design, implement, support and operate the platform at Ferring Pharmaceuticals to deliver the highest possible relevancies on search for the R&D teams in different locations.

Sinequa’s solution is bringing the power of AI to Enterprise Search to provide Ferring a “future proof” solution that offers a whole range of opportunities for future innovations. The dilemma of pharmaceuticals is to find the needle in the haystack – scientists need to screen tens of millions of documents from internal and external sources, from structured and unstructured data for identifying relations between genes, drugs, Mechanism of Action (MoA) and finding the right skilled subject matter experts. Other departments like Regulatory & Compliance, Legal & IP, Marketing & Sales, Clinical Trials, HR and more can benefit from customized Search-based applications on the same platform – finding relevant information instantly for fact-based decisions – no waste of time anymore.

-Alex Halbeisen, Expert Sales Big Data & Analytics at Atos

 

+1Share on LinkedInShare on Twitter

Best Practices for Intelligent Search

This article originally appeared as part of a KMWorld Best Practices White Paper on Intelligent Search

Best Practices for Intelligent Search

Sinequa provides an intelligent search platform that enables organizations to become information-driven, which means having actionable information presented in context to surface insights, inform decisions, and elevate productivity, consistently and reliably. Our platform consists of packaged technology that allows this to happen quickly and without sacrificing context or quality as typically happens with “lossy” approaches involving data migration.  (more…)

+1Share on LinkedInShare on Twitter

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.”

 

+1Share on LinkedInShare on Twitter

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/

+1Share on LinkedInShare on Twitter