Introduction to Enterprise Search

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.

Connectors

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.

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.