What is enterprise search and how does it help?



Popular search engines like Google and Bing are so enmeshed in our everyday lives that have become synonymous with search in most of our minds. However, though web search and enterprise are broadly comparable, they work in quite different ways and serve distinct purposes. Enterprise search tools are for use by employees. They retrieve information from all types of data that an organization stores, including both structured data, which is found in databases, and unstructured data that takes the form of documents like PDFs and media. The term “enterprise search” describes the software used to search for information inside a corporate organization. The technology identifies and enables the indexing, searching and display of specific content to authorized users across the enterprise.

IT industry analysts have shared that enterprise search is growing into something new.  In 2017, for instance, Gartner created a new enterprise search category called “Insight Engines.” These solutions help business synthesize information interactively, or even proactively, by ingesting, organizing and analyzing data. Forrester, another prominent analyst firm, defines this new category as “Cognitive Search.”


How does enterprise search work?

   Content is the raw material for enterprise search

      More and more data to analyze, structure and classify

Data becomes more pervasive within a business as the organization grows. There can be a huge proliferation of product information, process information, marketing content and so forth. Individual teams create content, which then inevitably spreads across the enterprise.

      Diversity of data

The information found inside large organizations tends to be highly diverse and fragmented. It’s invariably hosted on a broad range of repositories and enterprise applications. These include Content Management Systems (CMS’s), Enterprise Resource Planning solutions (ERP), Customer Relationship Management (CRM), Relational Database Management Systems (RDBMS’s), file systems, archives, data lakes, email systems, websites, intranets and social networks as well as both private and public cloud platforms.

The data comes from a variety of sources. Structured and unstructured data are kept in different “containers.”

   How enterprise search indexes, classes and ranks data 

        Search engine process

The enterprise search process occurs in three main phases:

  • Exploration—Here, the enterprise search engine software crawls all data sources, gathering information from across the organization and its internal and external data sources.
  • Indexing—After the data has been recovered, the enterprise search platform performs analysis and enrichment of the data by tracking relationships inside the data—and then storing the results so as to facilitate accurate, quick information retrieval.
  • Search—On the front end, employees request for information in their native languages. The enterprise search platform then offers answers—taking the form of content and pieces of content—that appear to be the most relevant to the query. The query response also factors in the employee’s work context. Different people may get different answers that relate to their work and search histories.

Techniques like Natural Language Processing (NLP) and Machine Learning are often involved in determining relevant answers to queries.

      Natural Language Processing (NLP)

NLP, a branch of Artificial Intelligence (AI), involves interactions between humans and computers that take the form of natural, human-like language. Its ultimate objective is to make sense of human languages in a way that is valuable to the process at hand, with the computer reading, deciphering, understanding the human language.

      Machine Learning

Machine learning applies AI to give systems the ability to learn and improve from experience, automatically, without the need to be programmed explicitly. It focuses on creating computer software that can access data and then make use of it for learning purposes.

      User Experience Design

User experience (UX) design is about creating products that deliver relevant, meaningful experiences to end users. It comprises the design of the entire process required to acquire and integrate the product. This includes branding, design, function and usability.


Why is enterprise search strategic in big companies?

   Content without access is worthless

Enterprise search helps people in an organization find the information they need to perform their jobs. It gives them access to data extracted from inside the business, along with external data sources like document management systems, databases, paper and so forth.

   Time is money: how Enterprise Search increases productivity 

Studies reveal the cost of employees spending time finding knowledge:

  • “The knowledge worker spends about 2.5 hours per day, or roughly 30% of the workday, searching for information.” – IDC
  • “The research found that on average, workers in both the U.K. and U.S. spent up to 25 minutes looking for a single document in over a third of searches conducted.” – SearchYourCloud
  • “The average digital worker spends an estimated 28 percent of the workweek searching e-mail and nearly 20 percent looking for internal information or tracking down colleagues who can help with specific tasks.” – McKinsey & Company


Enterprise search software reduces the time employees require to find the necessary information. As a result, it opens up work schedules for more high-value tasks. This improvement is particularly important given the current emphasis on getting optimal performance out of teams in lean, digital, agile organizations.

   Enterprise Search, Insight Engine or Cognitive Search

Cognitive search, the new generation of technology for information gathering, uses AI capabilities like NLP and machine learning to ingest, analyze and query digital data content from multiple sources. Users receive results that are more relevant to their intentions. Cognitive search solutions are essential to delivering the most valuable customers and employee experiences.

   Apply Enterprise Search to many Use Cases

Enterprise search engines can put to work across many different use cases in order that are intended to improve productivity:

  • Digital workplace—Enabling teams to be more productive and collaborate effectively using enterprise search as part of an overall digital workplace experience.
  • Customer service—Giving customer service representatives the ability to quickly and easily find the information they need to deliver excellent customer service.
  • Knowledge management—Applying enterprise search to facilitate the corporate knowledge management process.
  • Contact experts—Letting employees search for experts and filter results according to expertise and knowledge.
  • Talent search—Matching candidates with job descriptions from a database of potential candidates.
  • Intranet search—helping intranet users locate information they need from shared drives and databases.
  • Insight engines—Leveraging AI to detect relationships between people, content and data as well as connections between user interests and current and past search queries.


What are the main criteria to select an enterprise search software?


How many data connectors will an enterprise search engine need for the data sources it has to index? The best practice is to include the sources that are likely to be indexed in the future in addition to what is planned for current indexing. If a company plans to decommission a data source in a year or so, however, it may want to exclude it from the connection and indexing processes. This is particularly true if the data is going to migrated to a new source.

   Privacy & Security

Data security and privacy is of paramount importance in the enterprise search process. The enterprise search platform must be configured to comply with corporate security policies, SOC2 and regulations like GDPR. Efforts must be made to ensure the integrity and confidentiality of data. Critical business assets must be protected.

The following enterprise search platform characteristics and features help make sure that information and documents are only accessible to users with the right permissions:

  • Adhering to international and industry-specific compliance standards
  • Protecting content from malicious actors using built-in encryption in the indexing pipeline
  • Customizing the process regarding IP restrictions and encryption
  • Synchronizing with single sign-on (SSO) providers
  • Controlling access on a per-user basis and using security filters for indexed content
  • Using multilayer security across the cloud, on-premises data centers, intranets and operations

   Intelligent search or Predictive AI

Predictive AI is seen as the future of enterprise search engines. With self-learning algorithms embedded in enterprise search tools, it is possible to innovate by learning from users and improving results based on their usage patterns. Furthermore, by using custom APIs that are designed to make search tools work optimally for a given audience, it is possible to deliver fine-tuned results that improve over time.


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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


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


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Sinequa Wins Best NLP Platform in AI Breakthrough Awards


It’s been a busy few months at Sinequa, and we are ending June on a high note having been selected as the Best Natural Language Processing (NLP) Platform in the AI Breakthrough Awards!

The AI Breakthrough Awards recognize today’s top companies, technologies and products in the artificial intelligence industry. Over 2,500 nominees have participated from all over the world. (more…)

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Cognitive Search & Analytics Capabilities Out of the Box for Box Customers

In today’s digital age, leading organizations are looking for better ways to get more out of their data. They are choosing platforms that make every employee more connected, productive, and mobile-without compromising security. As companies adopt Box, providing intuitive information access and advanced search capabilities become increasingly important to end users. Using advanced Natural Language Processing (NLP) and Machine Learning algorithms, Sinequa’s Cognitive Search & Analytics platform enables users to search, analyze and gain valuable insights extracted from Box content repositoriesalong with on-premises enterprise applications, big data and cloud environments.

To build a sophisticated search and analytics engine is one thing, but to build such an engine that can preserve all the native security and permissions settings of connected repositories is another matter altogether. With Sinequa and Box connected, workers can search the Box environment (and all other data sources) while maintaining the native control settings of the respective platforms in which the data resides. This ensures that the granular security and permissions within Box are maintained in the Sinequa search interface, allowing individual users to seamlessly search and leverage only the content they are entitled to access.

The result is an environment unhindered by unnecessary, cumbersome processes for permission requests, or worse, unintentional viewing of unauthorized content. This allows users to quickly search and pinpoint the data, content, subject-matter experts, and topics they need in a fully secure and managed environment, where only the relevant data appears to each individual.
To learn more about the partnership between Box.com and Sinequa and the benefits of Cognitive Search & Analytics, you can download the complimentary research note “Sinequa partnership with Box amplifies cross-platform enterprise search and analytics” – April 2017 – from Paige Bartley, Senior Analyst at Ovum.

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What is Cognitive Search? How a New Generation of Platform is Transforming Enterprise Insights?

Despite the effort from technology vendors to deliver relevant, contextual, and actionable insights with their applications, most organizations have been slow if not reluctant to embrace these advances in search-driven experiences. In fact, a lot of companies have been burned by their past enterprise search experiences.

The good news is that something is shaking the world of Enterprise Search – some would say ‘finally.’ New industry investments and R&D effort are changing the search experience to provide more relevant results and deeper insights to users in their work context.

As we enter the era of “cognitive computing,” new search solutions combine powerful indexing technology with advanced Natural Language Processing (NLP) capabilities and Machine Learning algorithms in order to build an increasingly deep corpus of knowledge from which to feed relevant information and 360° views to users in real-time. This is what leading analyst firms call “Cognitive Search” or “Insight Engines.”These cognitively-enabled platforms interact with users in a more natural fashion, learn/progress as they gain more experience with data and user behavior, and proactively establish links between related data from various sources, both internal and external.

In a recent brief, Forrester defines cognitive search as:

“Indexing, natural language processing, and machine-learning technologies combined to create an increasingly relevant corpus of knowledge from all sources of unstructured and structured data that use naturalistic or concealed query interfaces to deliver knowledge to people via text, speech, visualizations, and/or sensory feedback.”

How does cognitive search work to deliver relevant knowledge?

  • It extracts valuable information from large volumes of complex and diverse data sources. It is crucial to tap into all available enterprise data whether internal or external, both structured and unstructured, to provide deeper insights to users in order for them to make better business decisions. Cognitive search provides this connection to provide comprehensive insights.
  • It provides contextually and relevant information. Finding relevant knowledge across all available enterprise data requires cognitive systems using Natural Language Processing (NLP) capable of “understanding” what unstructured data from texts (documents, emails, social media blogs, engineering reports, market research…), and rich-media content (videos, call center recordings..), is about. Machine Learning algorithms help refine the insight gained from data. Trade and company dictionaries and ontologies help with synonyms and with relationships between different terms and concepts. That means a lot of intelligence and horse power “under the hood” of a system providing “relevant knowledge” or insight.
  • It leverages Machine Learning Capabilities to continuously improve the results relevancy. Machine Learning algorithms (amongst the most popular ones: Collaborative Filtering and Recommendations, Classification by Example, Clusterization, Similarity calculations for unstructured contents, and Predictive Analysis) provide added value by continuously refining and enhancing the search results in an effort to provide the best relevancy to users.

Thanks to new technology advancements, cognitive search brings to data-driven organizations a new generation of search enabling them to go far beyond the traditional search box, empowering its users to get immediate and relevant knowledge at the right time on the right device.

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