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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3 Key Drivers for a Performant Enterprise Big Data Search and Analytics Platform

If you are aiming at deploying a performant Enterprise Search platform you would do well to consider these 3 key criteria:

Strong Content Analytics

In order to be extremely effective and efficient, an Enterprise Big Data Search and Analytics platform should offer strong Content Analytics that combines indexing of both structured and unstructured data. Indeed, it’s the combination of both types of analysis that delivers more relevant results and insights to users.

In addition, a performant Enterprise Big Data Search and Analytics platform should also “put powerful NLP to work in surprising scenarios” according to Forrester Research. The semantic analysis (named entities extraction, text mining agents, etc.) coupled with the statistical analysis and machine learning algorithms enables data-driven businesses getting more relevant and contextual information from search results.


Big Data Search and Analytics  PlatformHigh Connectivity

A Big Data Search & Analytics platform only deserves its name if it connects easily to virtually all data sources of an organization. If you need access to a new data source, you want to have it now, not in 3 months.

Multiple connectors to structured and unstructured data sources (internal and external to an enterprise) will help you cope with “data variety” and ensure that projects can start delivering value to users in a matter of weeks rather than months.

Extreme Scalability

Your platform architecture should offer the necessary scalability to deal with your large and diverse amounts of Big Data. It should be scalable enough to combine statistical analysis of structured and unstructured data with linguistic and semantic analysis of texts in several major languages (NLP – Natural Language processing). Moreover, an out-of-the box Grid Architecture that allows you to flexibly adapt resources will help you gain agility and get faster response times.

So, is your Enterprise Big Data Search & Analytics platform as performant as you thought?

If not, request a demo here and see how you can get value from your big data easily and rapidly!

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Have You Heard About SBA “AppStores”?

You are overwhelmed with business data coming from diverse and multiple data sources and with demands from various user populations to make use of this data? In that case you should look at how to construct your own App Store!

You may be wondering “How on earth am I going to that?”

But our customers achieved it! They built their own SBAs on the top of our advanced search & analytics platform. And now, they have created an “SBA factory” serving all kinds of business needs.

Sinequa ES is designed for high performance content analytics across functions and industries, it offers a powerful platform to create search-based applications (SBA) with hitherto unimaginable speed. This allows them to create Apps for any operational, individual and data need.

architecture-en

Our unique content analytics, including Natural Language Processing, produce a “rich” index, with information added on top of the original sources: meta-information, concepts and relationships between contents, etc. Only such a rich index can serve as a platform for an abounding and ever-growing set of Search Based Applications (SBA): Its richness ensures that even the SBAs you haven’t thought of as yet will find the information they need to serve their users in the index. If you need to delve into the original data sources, Apps become too difficult to construct.

Our customer AstraZeneca’s vision is to have Search nourish their next generation of business intelligence software and help create new applications. They have created a particularly innovative “App Store”.

Curious now to know more about these Apps and see how they have been deployed?

Click here to request further information

 

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Enterprise Search & Big Data: Building a 360° View of the Customer

The complete picture: Building a 360˚ view of the customer

Graham Woolley from Sinequa explains to EM360° how enterprises will gain a business advantage from having a complete 360-degree view of their customer. He looks into three particular benefits: knowledge, insight and most importantly, establishing a customer relationship.

Listen to the podcast on the Enterprise Management 360° websiteCustomer Service

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The Heat in The Trend Point: June 24 to June 28

We come across so many articles in the media that point to next generation search solutions and innovative business intelligence systems, but there are many companies still using the technology of bygone days. This idea occupied  The Trend Point over the past week.

Despite recent innovations with semantic search capabilities and interface design that tends towards an intuitive user experience, legacy systems still remain in the enterprise. “Enterprise User Experience Matters” summarizes the state of the matter:

The computational legacy of the 1960s is still with us today, despite a surplus of aluminum and gorilla glass. And despite the aspirations being fulfilled on the consumer level, a comprehensive simplicity is lacking at the core of most enterprise software. Obscurity and inconsistency reign (think of BlackBerry’s descent of late) where transparency and interoperability ought to go hand in hand. The egregious result is that the everyday tools, the interfaces that we must interact with daily in our jobs—from banker to lawyer, from journalist to physician—are almost incapable of leveraging the considerable network of information that many of us need to wade through at work.

When the problem has been recognized as an information management issue stemming from the software “solution,” many companies know they must take action. However, there is no one correct path to take. We saw the following summary in “Data Management Tips” offer advice:

Keep in mind that overhauling an existing system or syncing all of the databases in an organization can be an enormous, costly, and difficult project that can take months or years to implement – this may make it impractical, particularly if other projects will deliver a bigger business benefit. However, you can take other steps to improve data management for your team, and for your organization.

What should be done with existing data when replacing a legacy storage system? “Combining Big Data with Existing Data” calls for the integration of data previously collected and stored with the huge chunks of unstructured data represented by varying file types. The following information was relayed in this post:

Big data opens an entirely new data universe to consider and use to improve decision making. But how does a business/systems analyst turn it into actual usable data so that it can be used for operational improvements that result in real business value? Success depends on how fast and seamlessly you can combine your big data with your enterprise data and present that collective information to your decision makers.

While we definitely recommend storing and parsing old data in addition to new data, merging legacy enterprise data warehousing systems with new solutions is not always a cut and dry answer. When there are many search solutions that provide efficient information access in real-time, who needs to hold on to any remaining parts of a legacy search system? Companies like Siemens, for example, are choosing to replace their out-dated search technology with Unified Information Access.

Jane Smith, July 03, 2013

Sponsored by ArnoldIT.com, developer of Beyond Search

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