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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Cognitive M&A – Leveraging Cognitive Search & Analytics for Successful Mergers and Acquisitions

Mergers and acquisitions provide one avenue for organizations to grow via synergistic gains, strategic positioning and diversification. Even with an abundance of M&A activity, mergers tend to fail at the business process and information integration levels. The success of a merger can be greatly enhanced when business processes are integrated and information is seamlessly unified by gathering it from both organizations, analyzing it, establishing clusters of semantically similar information, and finding common patterns. Cognitive search & analytics platforms provide the necessary capabilities to accomplish all of this, thereby helping facilitate merger and acquisition initiatives and significantly increasing the odds of success.

ANATOMY OF A SUCCESSFUL MERGER

Let’s envision the details of how this impacts the relevant stakeholders. At the outset, a cognitive search & analytics platform provides the organization with unified access to information from both organizations and beyond. Users can leverage out of the box machine learning algorithms to explore and navigate this information. For example, the Clustering algorithm groups documents into topically-related clusters by analyzing the content and metadata. This is very useful for topical navigation and helps stakeholders identify similar documents based on named entities within the content. Automated classification is another useful technique for unifying information and improving navigability. In certain circumstances such as when classification rules do not exist but a properly classified sample set of content does, a Classification by Example algorithm can automatically create a model from the sample set, which can subsequently be applied across the combined set of content from both organizations to further enhance findability for stakeholders.

Sinequa CollaborationMachine learning algorithms can also help match experts with other experts as well as relevant documents across the consolidating organizations. This is done dynamically by analyzing what people write and collaborate around to compute user profiles, which are subsequently analyzed to compute “peer groups” that connect stakeholders with similar interests and expertise across the consolidating enterprise. With these peer groups established, experts can be more effectively presented with relevant content using a collaborative filtering technique that compares preferred content across the peer group and surfaces valuable content to members of the peer group who have not previously been exposed to it. As you can see, a cognitive search & analytics solution facilitates smart information sharing across the consolidating enterprise. Usually a lack of sophisticated security controls impedes greater openness between consolidating entities. A search-based application, however, respects existing security profiles—making it easier to merge infrastructures securely.

A cognitive search & analytics solution also helps to identify areas of risk and to solve outstanding issues before financial consequences occur. For example, risks could include content containing Personally Identifiable Information (PII) or content with no security associated. This is done by employing text-mining agents (TMAs), which provide out-of-the-box rules-based capabilities to extract elements from unstructured text. TMAs can be configured to incorporate terms and phrases specific to any part of the business. A cognitive search & analytics solution enables a quick, seamless and successful consolidation of organizations. Typically, in a large enterprise this is done as a series of search-based applications (SBAs) that each pull from a Logical Data Warehouse (LDW), which is essentially central cache of unified information.  In the next sections, we will look at specific areas of the business that typically benefit the most from this approach.

SALES AND MARKETING 

Once consolidation is underway, the organization must move quickly to combine sales and marketing activities, sales methodologies, pipelines and channels to drive revenue in the field and promote up-selling and cross-selling into new and existing market segments. The organization wants to minimize any potential lapse in the sales cycle for the newly merged company.

A cognitive search & analytics solution immediately equips sales teams with a single global access point to relevant, real-time and insightful information on products and customers—sales and customer notes, sales processes, product information and sales training are all immediately accessible. As previously mentioned, this is typically done using a dedicated search-based application (SBA).

An SBA for Sales and Marketing could provide unified access to the separate CRM systems and allow for the addition of shared content. As a result, no sales note, customer quirk or prospect is lost during consolidation. An SBA also offers the ability to push alerts and notifications out to users.  As sales representatives learn about new products,

The SBA provides unified access to new marketing materials, sales process documentation, research documents and news articles to assist in training and ensures that they are effectively representing the newly expanded product line. Often when a merger occurs, customers know more about the products from the other company than the sales reps. With a cognitive search & analytics solution in place, an empowered and unified sales team can competently sell the acquired products and services.

FINANCE, ACCOUNTING, AND HUMAN RESOURCES

Finance, accounting and human resources are other key departments that need to be unified. A dedicated SBA provides a complete and consolidated access to information from the disparate ERP systems.

A cognitive search & analytics solution provides administrators and content curators with the visibility across the enterprise necessary to manage all documentation from both parties related to the merger effectively and securely.

Multiple IT systems and finance systems always increase the complexity of a merger.  Organizations acquiring a large IT infrastructure need to identify the systems acquired and the value of the data in these systems.

A cognitive search & analytics solution enables the required visibility and helps to expose any redundant or unused systems that might be eliminated. For example, a cognitive search & analytics solution can be used to monitor and analyze usage of underlying repositories and applications to show what sources are being used less frequently. Some organizations have even been able to standardize and eliminate the need for multiple search applications. In other cases, a cognitive search & analytics solution works as a stop gap providing access to legacy systems that should be migrated, thereby reducing the cost of forcing an immediate migration.

Organizations can leverage Sinequa’s scalable platform as the cognitive search & analytics solution to connect information across the consolidating enterprise by leveraging previously mentioned machine learning algorithms like Clustering, Classification by Example, etc. as well as more traditional rules-based enrichment techniques. This helps the organization deliver unified access to stakeholders, ensure accuracy, reduce risk and gain insight into the complex systems across all affected departments. Enterprise Application Integration is usually a multi-year project. While cognitive search & analytics solutions do not replace such an integration, they provide comprehensive visibility in a very short time.

RESEARCH AND DEVELOPMENT

The cost of R&D increases when multiple teams unknowingly work on solutions to the same problems or fail to recognize and utilize the work done in past research. Cognitive search & analytics solutions positively impact R&D by accurately combining research data from the consolidating organizations, giving users real-time access and reducing the duplication of content, efforts, and sometimes entire projects. They do this by connecting experts working in the same subject area.

Sometimes a key driver in a merger or acquisition is gaining access to intellectual property, which often includes the expertise of the other organization’s knowledge workers. Sinequa’s Find the Expert capability gives employees from each organization the ability to discover the most knowledgeable people on a variety of topics, to view their profiles and to find associated information. This accelerates R&D discovery by enabling users to navigate information in clusters—leveraging the machine learning algorithm—and by content refinements.

These tools enable users to find past research and hidden relationships – including relationships with external experts with whom both companies have collaborated in the past – that would have otherwise been missed, thereby increasing speed-to-market.

The organization is also able to gain greater market share by leveraging and optimizing the information acquired instead of simply discarding projects in progress. One of the key drivers in a merger is the ability to retain and share as much knowledge as possible. Sinequa’s collaborative features spawn greater innovation in the newly expanded R&D department in the form of capabilities such as tagging, bookmarking and automatic feedback loops. For example, a scientist can comment on an old publication and explain how it relates to new research, which subsequently increases go-to-market speed by enabling broader collaboration.

HOW IT WORKS

Sinequa has developed an innovative and simple-to-use Cognitive Search and Analytics platform that offers Unified Information Access (i.e. information from any source, in any format, whether structured or unstructured, internal or external, through a single platform and user interface) to respond to even the most difficult information access challenges of large companies and organizations. The solution is composed of three main components:

  • A powerful (natural language) analytics platform that gives structure to the most unstructured content. The analysis and indexing of data solve typical enterprise challenges involving multiple sources, unstructured and structured content, multiple languages, high data volume and high update frequencies.
  • Simple and intuitive user interface that brings simplicity to complex search and analytics. Sinequa’s out-of-the-box user interface leverages the familiarity of common search tools enhanced with faceted search tools (i.e. filters) and analytics, giving the user an immediate picture of the information available from all enterprise sources. The UI is easily customizable to reflect specific appearance and navigation goals as well as corporate standards/branding, and is extensible, allowing third-party or custom UIs to be easily integrated via a provided search API (Application Programming Interface).
  • A powerful, open and scalable technical architecture (GRID). Sinequa’s highly-scalable architecture was designed from the ground up to support multiple search needs starting on a single server cluster, providing a cost-effective solution that allows companies to respond to multiple business needs now and in the future with minimal hardware investment (whether the application is deployed on premise or in a private cloud). The architecture is distributed and modular to support virtually any data source addition and growth, often without any additional infrastructure investment.

Leveraging these combined components from within the Sinequa platform has proven to accelerate the integration of business processes and to amplify the expertise of the whole by seamlessly and securely unifying disparate information from each organization involved in a merger or acquisition.

CONCLUSION

A cognitive search & analytics platform facilitates information transparency and communication across the entire enterprise, minimizing disruptions while integrating teams and departments during mergers and acquisitions. Applying this technology as a solution effectively operationalizes the information access, speed and control needed to ensure long-term success in the newly formed organization.

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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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Big Data: Garbage in – Garbage out?

I had the chance to present the Sinequa solution at a conference organized by one of the 5 big pharma companies a month ago. The theme of the conference was innovation for RDI and the first morning was dedicated to Big Data. Sinequa was invited because we have customers like Siemens or AstraZeneca that are using our solution for their R&D teams to help them find information in large amounts of internal and external documents (publications, project reports, test reports, patent filings, and even emails), but more importantly, to find expertise based on the analysis of these documents.

Following all the contributions on the topic of Big Data and a round table discussion, questions were invited from the audience.  One question from the audience put all the speakers on the spot:

Big Data… is it garbage in or garbage out?!

After few second of hesitation I ventured an explication of why technologies like search could help information workers to actually select what is garbage for them and what isn’t.

But the question was definitely more complex than it seems!

Big Data is very often associated with machine data and related storage issues. In large organisations, especially in R&D, Big Data is very often human generated data. By this I mean documents, email, research reports stored in many applications, containing years of research on a specific subject. Furthermore, in R&D, information does not only reside inside the firewall, but also outside, in specialized databases or in academic publications. To some people, hundreds of millions of such documents may appear as “garbage”, but they could turn out to be a goldmine if a scientist finds in that “garbage” research results related to his or her current research, or even better, if he or she can find an “unknown colleague”  who can provide answers to some specific questions.

Then came the question: How to actually filter the garbage for each end user and help find the goldmine?

The first approach people take is to define the best sources for good content. With search, if you index poor quality content, you will find poor quality content!

But very often it is almost impossible for an IT department to define what is good or bad quality content. The quality of content may even be perceived differently by different users, i.e. different subject matter experts.

This is the main challenge in dealing with human generated big data!

Search is all about “Free-Form-Analytics”, contrary to the slicing and dicing in predetermined structures of data warehouses and “classic” BI tools. To offer this flexibility, data is organized during initial indexing and then during the life of the search application.

Here are the main steps to achieve this:

Step 1: We index all the content with the corresponding security credentials, and the available metadata for every application or data source used by information workers on a daily basis

Step 2: During indexing we perform statistical analysis, like many other search engines, but we add our special sauce, Natural Language Processing and Semantic analysis to be able to tag names of people, companies, places, etc. in a full text. It seems easy, but this is the hard work that needs to be automated, at scale, for hundreds of millions of documents, if you want to get a grip on the “Garbage”.

Step 3: Once this work is done, here comes the interesting part, when we link the automated content analytics to an organization’s “DNA” (mostly contained in its business applications). Organizations have spent years trying to organize their content and will probably continue to do so forever without ever seeing the end. Why not use that available DNA (products, client information, HR data, etc.) to refine the content analytics performed in step 2? An anonymous person detected in a text then becomes a colleague, a customer, a partner, etc. A strange series of numbers and letters becomes a product ID and so on.

Structured data helps to refine the analysis of unstructured data.

Once you have gone through these steps you are able to provide end user snot only with a way to manipulate huge amounts of data (what you may have called garbage before may have become valuable Human Generated Big Data), but also to make sense of this data by asking the free-style questions that they are interested in at a given time. There is no limit to the questions you can ask – and hence no limit in making your Big Data valuable.

 

By Xavier Pornain
VP Sales & Alliances at Sinequa

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The Heat in The Trend Point: April 22 to April 26

Enterprise search does not always get the attention it deserves but recently we have seen a crop of articles on this vital technology in The Trend Point.

An efficient and agile user experience is an incredibly sought after characteristic in enterprise search today. The article cited in “Usability of Enterprise Search Valued” shares the following opinion:

There are too many IT consoles, too many vendors — one for network management, one for help desk, one for application performance,” said Raj Sabhlok, president of ManageEngine’s parent company Zoho. Pity the poor admins who have to piece all that information together to figure out what’s going on, or worse, what went wrong. The search function promises these woebegone admins a “Google-like interface” that lets them search on a device name, for example, and get back every instance in which that name crops up.

This has in fact more to do with the search function than with a search interface: Enterprise Search is good at pulling together all relevant information on a given topic, providing the notorious 360° view. In the long run, systems administrators will not want to have a “Google-like” interface to see the 360° view of the problem domain they are working on. They will probably want a mix of dashboards, facets and lists ordered by relevance. Such interfaces will be part of Search Based Applications on top of a Unified Information Access platform (aka as Enterprise Search platform).

A strategic disconnect between IT and business leaders can often drive IT professionals to have to build the case for innovative enterprise search software. In, “Podcast Offers Tips on Building Business Case for Enterprise Search” the following recommendations were given:

*The first steps to take to show business leaders the real value that enterprise search has to offer and convince them it’s time to implement a search program;

*Key questions that project managers and business stakeholders within an organization should ask of themselves when developing a formal enterprise search technology business strategy;

*The change management aspect of putting an enterprise search program in place;

*Liewehr’s take on how to build an enterprise search team and who should be in charge of shepherding the project;

*How enterprise search technology can be used to support; and

*Best practices on how to develop an enterprise search technology review process to ensure adoption and implementation success.

Enterprise organizations of all shapes have a need for enterprise search and while none of the articles referenced here pointed to the innovative aspects of current search technologies that does not mean there are no companies enjoying an advantage because of them. The fact that there are still many mentions purely in regards to enterprise search shows that the core technology is absolutely essential. Of course, semantic capabilities and the spread across structured and unstructured data that Unified Information Access offers are the type of search technologies that will be brining home stronger ROI and the implication of business stakeholders.

Jane Smith, May 1, 2013

Sponsored by ArnoldIT.com, developer of Beyond Search

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