Multi-Dimensional Search: Powering Data Discovery
It is a true, yet frequently repeated, cliché to say that we are living in an age of total data overload. Individuals and businesses seem to be on a never-ending search to dig through this data to find meaningful information. Struggling with multiple silos of data, dealing with structured and unstructured sources, understanding their contents and finding and correlating data all remain key priorities. Today, most businesses constrain themselves to search along a single dimension or rely on keyword or pattern-based full text search.
A new chapter is emerging in search—turning the data overload into a mother lode of opportunities in boundless search for real information. The next generation of enterprise search platforms and search-based applications will play key roles in analyzing data from which information and business knowledge can be extracted. Certainly, today's data mining challenge is not much different from California's gold mining challenge of 160 years ago—having the right tools at your disposal offers a clear edge over competitors and is often the key factor in determining success.
As the data landscape continues to change and business needs morph with it, agility and flexibility will be essential. Finding useful nuggets of information will require more than the data-mining equivalent of a pickaxe. In today's complex data landscape, you must be able to quickly, efficiently and almost effortlessly, tap into your data by searching in multiple dimensions concurrently.
What is "multi-dimensional search"? This next-generation method for finding and accessing information builds on tried and true full text and faceted search techniques, but in addition provides dynamic, on-demand content analysis, which presents different entry points for exploring data.
Multi-dimensional search automatically develops indexes around not only the document content, but also around semi-structured information including:
- Document metadata such as author/creator, date, source, category and document type;
- Descriptions found in glossaries, encyclopedias and ontologies; and
- Attributes derived through text analysis.
It offers multiple lines of inquiry when exploring a topic, thus giving the end-user a faster, more intuitive and personal experience as they discover related information. When doing a search on "social media strategy," for example, the engine provides not only all matching results, but also a breakdown of the search results sorted by metadata categories (as defined by the administrator)—e.g. authors on the topic; type of document; publication date; geographic location; and language. A multi-dimensional approach to content analysis can determine not only the literal matches in the data, but offers contextual filters based on a real-time analysis of the metadata and other attributes of the matches that can help guide a user more effectively.
As the user explores data, multi-dimensional search supports multiple facets that are dynamically generated for all matching results no matter what type of content or sources. Each facet can be dynamically searched and browsed, too, so that the user can do lateral explorations outside of the original search. For example, exploring and viewing a breakdown of all topics written by an author or focusing on data from a certain geographic location are both use cases easily solved with multi-dimensional search. This is different from traditional faceted search where the facets do not change as you search and must be determined before the data is indexed.
The user "interacts" with the data by including or excluding one or more dynamic facets to narrow or expand the search granularity as needed. For example, show all "social media strategy" documents that are of type PowerPoint or PDF, authored by John Doe and Jane Smith and published during a specific time range. The multi-dimensional search engine dynamically recalibrates and presents on the fly new context-based filters on each new data set as the user moves through the discovery process. The engine works in lock step with the user, constantly delivering new controls to navigate through the data. It gives the end user the power and control to narrow in on very specific and relevant data sets and offers the flexibility to explore and discover new paths.
Multi-dimensional search: Ideal for creating search-based applications. While the search process is often thought of as a means to an end, multi-dimensional search is a means to a beginning—forming the basis for organizing data and opening boundless opportunities to building valuable search-based applications. With multi-dimensional search, one achieves data organization, content analysis and extraction of correlations with extreme granularity of information that enriches search-based applications. The results are a deeper, richer and more empowering information-gathering and knowledge-building process for the user, and allows for a higher quality understanding and application of enterprise data for the business as a whole. By searching across structured and unstructured data with accuracy, application developers are able to provide relevant, productivity-enhancing dashboards for various business purposes such as understanding customer feedback, analyzing market trends over time and discovering context for business milestones.
Search-based applications: The sky is the limit. Once an efficient way of understanding the granular and correlated contents of enterprise data is available, businesses can develop and monetize a variety of search-based applications for a variety of scenarios, such as:
- Help knowledge workers seek personally relevant information;
- Assist customers during the purchasing process;
- Sell valuable data-intensive statistics and reports to partners or customers;
- Help product management teams speed time to market;
- Aid marketing teams in understanding what their competition is doing;
- Improve collaboration among colleagues and identify opportunities for collaboration with geographically distributed offices; and
- Correlate trends in social media, market intelligence and sales trends in order to fine-tune marketing programs.
As businesses search for value in their various silos of data, multi-dimensional search is poised to bring meaning, organization and invaluable data correlation which was previously costly and almost impossible to find. Simply put, the possibilities are endless and the value is immeasurable.
Q-Sensei delivers powerful multi-dimensional search solutions to explore, control and leverage the wealth of data. For more information, visit www.qsensei.com.