Why an Insight Engine Is the Secret Weapon of Your Digital Transformation


Most of the world’s content is not on Google: Some 80% of it is housed behind firewalls at the world’s companies and organizations.  Every day, your employees are generating new project ideas, performing R&D, executing financial transactions, serving customers, generating data in business applications and databases, and much, much more – structured data that may or may not be available for search, given its format or where it is stored.

However, your organization is also producing a wealth of information such as emails and text files, social media, and log data – unstructured data that may be captured by content management systems but has weak search capabilities. In addition, your employees may need to search multiple applications for the answers to their questions, slowing their path to progress and increasing the possibility of error. You know that if you could just integrate and understand this content, you could better anticipate and plan for market change. You could also use these insights to drive digital transformation efforts and streamline operations, fueling business growth.

Between 60% to 73% of corporate content is never analyzed for insights or larger trends. Globally, the opportunity cost of this unused data is $3.3T. Source: VentureBeat.

Why Your Organization Needs an Insight Engine to Surface and Make Sense of Your Content?

It’s clear that your organization struggles to access the full wealth of your content and transform it into something that is accessible and usable by your employees. At the same time, enterprise content is growing fast, meaning that these challenges are worsening all the time. While many organizations are embarking on complex and costly data strategy projects to develop analytics for digital transformation, there is a faster way to accomplish your goals – implement an insight engine. After all, why tackle a difficult project that will take months and heavy lifting from data scientists, when a partner is willing to tackle the hard work for you right now.

Challenges Organizations Face Implementing Big Data Analytics

Source: Statista 2020.

Think of an insight engine as your organization’s customized version of Google. We all love the ease of use, up-to-the-second intelligence, and vast scope of Google. It enables us to find what we’re looking for so simply and easily. With an insight engine, you gain those same qualities for your organization, transforming enterprise search into a simple, meaningful activity that can help your employees become more creative and innovative each time they use your platform.

You could spend minutes or hours searching for information you need and try to integrate it and understand it by yourself, knowing that the content you access may be incomplete, incorrect, or out-of-date. Or you can use an advanced search engine that finds the right information for you, surfacing highly relevant results for you as you search.

Sinequa uses 200 out-of-the box connectors and built-in converters to access, enrich and unify content from more than 350 document formats and make it available for search via a single index. Next, Sinequa applies natural language processing (NLP) to give the text meaning that’s way beyond simple keyword searching, making it infinitely more usable by you and your employees. Imagine developing a business strategy and being able to access all relevant historical and current performance data, market trends, vendor and competitor information, and more all at once. You can use this data to create a document that’s a powerful predictor of where you’re headed – one that gets immediate buy-in from your leaders and sets the right course for your organization.

Want to make your organization even more intelligent? Use Sinequa’s machine learning (ML) algorithms to simplify and speed up your analytics projects. Use new insights to make decisions and place market bets before your competitors can. Gain speed to market in data-driven industries such as financial services, pharmaceuticals and life sciences, counter-terrorism, and national security, where time is often a critical source of advantage. Rest assured that ML is making your content smarter all the time, allowing you to gain the insights you need to outpace competitors or adversaries.

Have a need for industry- or application-based search? You can build your own custom search application on top of the Sinequa Insight Engine to help staff complete critical or complex tasks specific to their role and business function. Let’s explore that further.

How AI-Powered Search Enhances Digital Transformation?

Organizations can use intelligent search to make faster gains across business-critical and digital transformation initiatives such as:

  • Developing a 360-degree customer view: You want to create a comprehensive view of your customer relationships, their needs, and buying patterns to drive marketing and sales, product development, and customer service. Imagine being able to integrate customer interactions, web searches, transactions, social posts, and more, using that data to deepen relationships, drive revenues, and increase your profitability.

  • Ensuring regulatory compliance: If your organization operates in multiple geographies, you need to comply with all national and local regulations. Sinequa can simplify this process. As an example, Sinequa’s cognitive search engine can detect and automate the classification of data such as more than 30 different types of personally identifiable information (PII) covered by the General Data Protection Regulation (GDPR).

  • Improving customer service: Customer service interactions can often make or break a relationship with long call hold times, frequent call transfers, and incorrect data. Instead, your organization can use its own business search engine to provide personalized and highly relevant information that helps ensure an exceptional experience. A $717B finance company that struggled with customer complaints and IT-related data challenges used Sinequa to enable self-service, identify experts, and surface insights, saving $4.6M in operational costs and $16.8M in staffing costs.

  • Conducting R&D: Many companies rely on R&D to develop innovative new products and need to integrate and synthesize multiple data sources to focus their efforts. A $7B biotech leader uses the Sinequa platform to find internal experts and promising new molecules to speed drug development. The organization has increased data findability and accuracy by 9X, which will reduce drug development timeframes by 300 days, increasing potential revenue by up to $102M.

Enterprise search could help your organization tap its full breadth of insights, connect internal experts to problem-solve and innovate, streamline compliance, and deliver exceptional customer service. And that is just for starters. The list of potential use cases is as unique as every organization and its business challenges.

Want to learn more? Watch this Forrester webcast, “AI-Powered Search and Analytics to Enable the Information-Driven Enterprise. “

Watch the webcast now.


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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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“Building a Business Case for Enterprise Search – the Role of the CIO” By Martin White – Intranet Focus Ltd


Information is a business-critical asset

Get your complimentary copy of the report 

Over the last two decades I’ve walked through the doors of a very wide range of organisations to start work on enhancing the performance of an enterprise search application. Although that might be the working title of the project the reality is somewhat different. In almost every case the enterprise search team knew what actions to take but lacked the resources (people and technology) to do so. In effect my role is to persuade the CIO of the importance of enterprise search.

Almost without exception the question I am asked is what the return on investment will be if the search team is expanded (it is always too small!) or the decision is made to upgrade or replace the existing application. I can understand why the question about RoI is being asked but have to explain to the CIO that RoI is not a sensible approach to assessing the business case for search investment.

This is especially the case at the present time. The Covid-19 pandemic will have a significant impact on business strategy and operations for the foreseeable future. Organisations have to be able to respond quickly and authoritatively to new challenges and to new opportunities, doing so with employees working primarily from home and facing severe restrictions on meeting together and travelling between office locations. Although they can be networked together with video and social technologies they also need to be networked to the information assets of the organisation. These information assets are not just internal documents but also specialized external information services providing intelligence on changes in markets and the activities of customers and competitors.

Delivering information across the enterprise

In the enterprise employees write documents primarily for their colleagues and immediate managers. They rarely have a wider enough picture of the operations and interests of the enterprise to be able to alert others to what they have written. It is not just documents that contain information of value but also a wide range of enterprise applications.

The primary challenge for all employees is being able to find the information they need as a matter of urgency without knowing in which repository or application it is stored.

This is where enterprise search becomes business-critical. The low adoption of enterprise search is largely because the CIO finds it difficult to make a business case. In the past there was a tendency to focus on ‘productivity’ as the business case but that is very difficult to measure. At the present time the best way to justify an investment in a scalable and extensible enterprise search application is to show the Board the way in which effective access to internal and external information can make a measurable reduction in business risk.

Define who should own Enterprise Search

To make this case the CIO needs to work closely with line-of-business managers across the enterprise. All of them face a common problem in making effective use of information assets but do not have a common management platform to bring these to the attention of each other and the CIO, and then up to the Board for approval. Invariably they cannot define with certainty what the employees they are responsible for need in terms of information, so there is an urgent requirement to listen to and collate these requirements. In organisations which have highly mature enterprise search applications there will always be a search support team that is constantly assessing these needs and working with the CIO, business managers, experienced search users and the search application vendor to make the best possible use of the Technology. In making the business case the CIO also needs to take on full responsibility for information quality.

Balancing opportunity and risk

In this report I have set out the core characteristics of enterprise search applications and the way in which a business case can be made. Across many different types of organisations and business sectors I have found that using a risk-based business case can be very effective as it immediately aligns the enterprise search strategy with the business strategy. In my experience a significant improvement in the levels of search application satisfaction can be achieved in just three months of coordinated action across the organisation led by the CIO.

Not only will this programme of action make a significant short term impact it will also provide the evidence needed to invest in enterprise search technology to ensure that the organisation is positioned to take advantage of the opportunities that are now emerging whilst minimizing the business risk of taking decisions without having effective access to information.

Get your complimentary copy of the 9-page report

About the Author


Martin White has gained an international reputation over the last twenty years for his understanding of how to manage the information assets of organisations, ranging from the United Nations and the World Bank to some of the world’s leading pharmaceutical companies. Many of his clients have been multinational organisations with complex information management and information discovery challenges. Since founding Intranet Focus Ltd in 1999, he has worked on over 100 search-related projects. He is the author of eight books on information management, including Making Search Work in 2008 and the second edition of Enterprise Search in 2015. A book on Managing the Enterprise Search Experience is scheduled for publication in 2021.

Martin has been a Visiting Professor at the Information School, University of Sheffield,
since 2002, specialising in information management and information retrieval. He has
been a member of the Editorial Board of the International Journal of Information Management since 1997 and serves on the Committee of the Information Retrieval Specialist Group of the British Computer Society. Martin is a Fellow of the Royal Society of Chemistry, a Fellow of the British Computer Society and a Member of the Association for Computing Machinery (USA).


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Enterprise Search in the Digital Workplace

easy-enterprise-searchThis article originally appeared on the APQC blog.

Any knowledge-intensive organization of significant size either has a digital workplace or is scrambling to establish one. As Gartner so clearly stated back in 2017, the goal of the digital workplace as a business strategy is to boost employee engagement and agility through a more “consumerized” work environment. Employees should be empowered and motivated to get the information they need and then act on it to further the goals of the organization. The tools and resources provided to employees should seem familiar—like those used outside of work — in order to drive adoption and maximize ongoing productivity.

Building the Foundation

Gartner’s “building block” diagram illustrates the components necessary to enable and sustain an effective digital workplace. Note the “Information” building block, described as data and content being delivered in context. As organizations continue to pursue initiatives around digitization and digital transformation, they often create new challenges for their employees to overcome. Many of these challenges emanate from content and data that is accumulating quickly and constantly across siloed repositories in different formats and languages. For employees, navigating this complexity means wasted time, missed insights, and lost opportunities. The answer to this problem is enterprise search.

Gartner's Building Blocks of The Digital Workplace

Collaboration that Scales

A modern enterprise search platform includes a combination of capabilities that work together to provide information from enterprise content and data. It provides information relevance that is tuned to user needs and can improve through self-learning over time. It can scale in multiple directions, enabling many end-users to simultaneously access relevant information and insights from huge, diverse volumes of content and data. Modern search provides speedy response times, even in the most complex and demanding environments where time is literally money. The user experience can be easily configured to accommodate specialty use cases. Analytics are automated wherever possible, allowing the machine to do analysis while users apply judgment and make decisions.

Driving Business Value

Depending on the environment and application of enterprise search, the business impact can take on different forms. In some cases, enterprise search can drive revenue.  This has been proven in large pharma companies that are able to bring new drugs to market faster and in manufacturing and service organizations that can generate more proposals without sacrificing quality. In other cases, enterprise search drives cost optimization, accelerated productivity, and responsive compliance.


What companies need now is a practical means of connecting the dots to tap the potential value of all the content and data that resides across enterprise systems.  Doing so will address all kinds of business challenges, including those that were unforeseen in the implementation of each individual system.  Intelligent search platforms such as Sinequa can enable organizations to rise to these challenges and help transform the way professionals, businesses, and industries interact and operate in the digital world.


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Sinequa Featured in IDC Technology Spotlight Dedicated to Manufacturing Organizations


Constrained by manual processes and lost productivity, AI-powered search platforms are critical for manufacturers to streamline production and decision support.

We are proud to announce that we’ve been featured in a new IDC Technology Spotlight report: Digital Transformation of Manufacturing Through Intelligent Search. The report, written by Jeffrey Hojlo, program director, Product Innovation Strategies and Hayley Sutherland, senior research analyst, AI Software Platforms highlights the importance of how intelligent search — the combination of artificial intelligence, machine learning, big data analytics, and enterprise search — can optimize a manufacturer’s Industry 4.0 strategy.

According to the report and a recent IDC study, knowledge workers in the manufacturing industry remain constrained by manual processes. On average, knowledge workers waste up to 18 hours per week searching for, combining, and reformatting information. This time usage creates the general impression of inefficiency and brands the overall experience of information access as discouraging. Further, the manufacturing company’s yield might be injured, and important business conclusions could be drawn using inadequate information.

“Manufacturers should look for tools like Sinequa’s that are contextually aware and semantically savvy, able to analyze and categorize info from across the organization to deliver the most relevant insights”

With the demand for AI technologies that enable intelligent analytics increasing every year, IDC estimates that enterprise data from things like IoT devices, smart devices, email, metadata and other sources created annually is expected to grow exponentially.

“Sinequa’s platform allows manufacturing companies to streamline procedures and create operational efficiencies that alleviate the major challenges highlighted in this report,” said Scott Parker, director of product marketing at Sinequa. “By offering a broad AI-powered platform including search, content analytics, semantic understanding and auto categorization technologies, Sinequa provides relevant details to users when they need it, while supporting a range of machine learning algorithms and capabilities that improve findability and relevance.”

Harnessing the power of intelligent search, “manufacturing organizations see faster time to decision, better business decisions, and increased employee productivity as the most common benefits,” said Sutherland. “To achieve this, manufacturers should look for tools like Sinequa’s that are contextually aware and semantically savvy, able to analyze and categorize info from across the organization to deliver the most relevant insights.”

Download a complimentary copy of the IDC Technology Spotlight report: Digital Transformation of Manufacturing Through Intelligent Search.

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