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  • Specialized Strategies for Enterprise Search

Specialized Strategies for Enterprise Search

Enterprise search is the practice of making content from multiple enterprise sources such as databases and intranets, searchable to a defined audience. "Enterprise search" is also used to describe the software of search information within an enterprise.

Enterprise search systems index data and documents from a variety of sources such as file systems, intranets, content management systems, e-mail, and databases. Many enterprise search systems integrate structured and unstructured data in their collections. Enterprise search systems also use access controls to enforce a security policy on their users.

Enterprise search as a standalone application for locating documents throughout the enterprise is still going strong, but many search engines are now embedded in applications that people use as their primary work environment. Search solutions are also used on more complex tasks such as locating relevant information found in either databases or diverse document repositories.

New search tools are emerging for searching geospatial data and processing it with other types of information to provide an enriched and informative blend.


One of the new enterprise search applications is SAVO.

SAVO focuses on driving greater sales productivity, and one way of doing this is providing the content that is needed by sales reps in context. Content can be stored in such a way where users can access it either locally or remotely. The SAVO platform provides search capabilities in a content repository, which contains approved information designed to support salespeople as they do their jobs. The search function is configured with a profile for each salesperson to push out the information that will be most useful to each individual.

By using profiles, overall selling methodology and model can be reinforced. The information pushed out might include a list of documents for a client meeting, a video clip, information by product line or a summary of facts about a competitor. To support the sales reps’ conversations, SAVO provides context-relevant information.

Although content for different phases of the sales cycle is predefined, if unexpected questions arise, sales reps need to have the right information at their fingertips.

By setting up rules for tagging and reviewing information as it is added to the repository, SAVO is able to keep the right information in front of the sales rep. All the information they need is in the backend. The strategy is not to present the rep with a blank search box. Instead, at the frontend, the rep is asked a series of questions such as what product they are dealing with, what type of document they want, whether it’s a customer-facing presentation or a brochure. In two or three clicks, they have reduced a potential list of 60 documents to about five.

Also searchable on the SAVO platform is previously tacit knowledge that has been captured in forums or in comments. Referred to as “tribal knowledge” by SAVO, the content is generated by employees who post questions and answers on a subject matter expert moderated forum within SAVO. That content can be sought on a proactive basis by the sales reps when they have a specific question. Between the profiled information and the responses to ad hoc queries, the sales reps are able to respond with accurate and timely information.

The search function is enabled on mobile devices as well as the desktop. Because sales is both a global and regional activity, SAVO supports multiple languages, including ideographic languages such as Japanese and Chinese, and the search function is available for all the foreign language versions of the product.

The search capability provided by SAVO is useful not only for salespeople in the field but also in onboarding. It helps new employees who are learning the ropes, because they can access instructional material, reference material and conversations posted by more experienced sales reps.


DtSearch is effective because it is a mature product with a very stable API.

Search applications with complex data models pose special challenges. One such example was a search application Contegra built for Carrier Corporation. The number of parts is large, and many parts have revisions to them that have been built over the years. Serial numbers, model numbers and other identifiers must be incorporated. Also, the system had to be able to identify what product the customer originally received because that would affect the replacement part needed. In addition to searching the database, the application must be able to locate technical documentation, brochures and other files in a variety of formats.

DtSearch is able to search across SQL databases and PDF documents, and then search parts referenced within documents. It sounds like a simple task to recognize parts numbers, but when you have 50,000 of them with complex relationships and want to do the search quickly, using the right search tool is very important.

It is able to understand the search needs of different users. Engineers see the world as a set of different systems, such as engines and drives systems, which have specific code numbers. There are hierarchical taxonomies, model numbers and serial numbers. Retrieving the exact information for each installation is critical when considering the action being taken is a certain type of repair or service. Each user group could have a different perspective on the data that requires it to be searched and viewed differently.

Particularly in specialized applications, the upfront work is critical. You would want to be sure that when the users do a keyword search, the information is presented in a meaningful context, so they can narrow it down to the exact part and the nature of the document, whether it is a technical publication, leaflet or catalog. A good content model enables both the field service staff and external customers to access information in a self-service mode, which cuts costs for the company.

Geospatial Search

Geospatial information has become increasingly important for many different applications and analyses ranging from marketing to agriculture. Yet the management of geospatial information has lagged that of any other kind of information. Although maps and imagery can be stored in a repository like any other digital files and searched according to indexed metadata, the ability to perform more complex searches on the data and process it once retrieved has been limited.

Voyager Search is a search solution designed specifically to manage spatial information. It combines modern search technologies with a unique understanding for geospatial data.

It includes an indexing solution that can extract information from nearly any type of geospatial data regardless of format. It later began supporting other non-spatial documents such as PDF and various Microsoft Office formats. It also offers a solution that enriches data through linking documents to a map.

Users are able to define a geographical area on a map and then search for relevant information about it. The user can find all the river data or stream flow data in that area, for example, or reports by dragging a box on a map. This ability is not available in other search engines.

Voyager Search can manage very large quantities of spatial data. Making data available and accessible while keeping it secure is a big challenge. A geospatially enabled search solution is critical not only to providing online access to geospatial intelligence, but also to broadening the analytical expertise of the organization. Voyager accomplishes that by providing the tools to index a wide variety of content and to add a layer of geospatial intelligence to the index.

The process for narrowing results from a search of geospatial data differs from that of other types of data. Search results from a demographics database, for example, might be narrowed by selecting only one income category. In a spatial search, “refine your search” might mean “view only content in a specific geography” in order to look at one aspect of the results.

Voyager Search can also create new files derived from geospatial and image data. For example, a petroleum engineer could look for seismic shockwave data from a certain time interval for use in the analysis. Emergency responders might look for recent areal photographs taken in their area and run them through an imagery analysis process to look for hot spots that would indicate the most recent fire perimeter. Voyager Search offers that capability, known as “geoprocessing,” which includes a variety of ways to manipulate geospatial data at the desktop.

These are just few new search applications currently on the market. Galaxy Consulting has over 17 years experience in enterprise search and enterprise search applications.

Categories: Enterprise Search
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