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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 which 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.
In this video, you will learn about the origins and evolution of search in organizations and explore with us the broad applications of Enterprise Search, such as :
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Remove limitations, fuel your workforce – Knowledge silos, disjointed teams, and legacy systems are no joke. Sinequa helps you overcome infrastructure and organizational barriers that stand in your way, helping you create a connected, efficient, modern workplace.
Today’s knowledge for tomorrow’s innovation – With Sinequa, you can transform your organization into a knowledge-driven powerhouse, enabling every team to turn insights into your next breakthrough. Sinequa’s cutting-edge technology harnesses the power of AI to augment knowledge-intensive work, enabling teams to spend less time searching and more time doing.
A customer-first mindset – Our product roadmap is driven by the needs of our customers, and informed by the latest advancements in AI. Powerful capabilities such as cutting-edge natural language processing and deep learning enable our customers to take advantage of our enterprise-ready Generative AI.
Sinequa’s Intelligent Search platform’s in-depth analysis provides Alstom’s employees with a thorough understanding of unstructured data, including the text coming from very complex technical and normative documents. This allows greater efficiency and real time savings for Alstom’s data scientists.
Tristan Le Masne, Vice President Internal Audit & Internal Control, Alstom
Studies reveal the cost of employees spending time finding knowledge:
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.
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.
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:
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.
The potential influence of artificial intelligence and machine learning on enterprise search can be understood as two important capabilities:
Making more information accessible – Making data digitally accessible using techniques such as optical character recognition, machine vision, scanning documents, and analyzing more data types. An AI application can also accomplish this by automatically adding metadata to backlogs of enterprise data.
Enabling companies to ask deeper questions – Enabling the capability of searching for broader concepts as opposed to strict keywords. This is helpful for finding insights on a general topic instead of simply every document including a few terms. Employees could search for documents and information beyond what directly pertains to a single keyword.
When observing the differences between search applications of the past and those of the present, one can see that artificial intelligence could help broaden a bank’s access to data. At the same time, the technology could transform the way in which employees search for that data, thus capitalizing on that access even more.
