Intelligent Search and the Long Tail of Enterprise Knowledge
To get the most out of their knowledge assets and collective expertise, organizations should look for ways to tap into the “Long Tail” of their collective enterprise knowledge. The Long Tail theory, developed by Chris Anderson in 2004, describes the profound impact of giving customers access to the widest possible variety of items, including “niche” items. As the chart below reveals, the vast majority of potential value resides within the volumes of niche products represented by the area in red.
Many KM initiatives struggle to succeed because when it comes to collective enterprise knowledge, companies don’t make all of their “niche” knowledge and expertise available so team members can easily engage and use it.
Part of the challenge is that knowledge lives in a wide variety of places, including email, CRM, WCM, collaboration platforms, social media channels, and in the minds of individual team members. And wherever this knowledge resides, it is rarely appropriately tagged or categorized, making it difficult for others to find, learn from, and re-purpose.
Fortunately, search technologies, when architected and managed correctly, can empower an organization to securely connect individual team members with an entire “knowledge eco-system”.
To tap into the Long Tail of knowledge, companies should look to a search-driven solution that delivers on four key capabilities:
CONNECT
Information that employees need to accomplish their job tasks resides in a variety of places as mentioned earlier. The first requirement then is to connect with all systems and sources that hold content associated with the business—e.g. knowledge bases, ECM, CRM, communities, PLM, social, collaboration, directories, email—both on-premise and in the cloud. Key to this connectivity are the abilities to crawl all types of structured and unstructured content; to honor native, user-level permissions; and do it at scale. Connectors which require no custom development to deploy, enable the precise scoping of crawls, and utilize an early-binding security-trimming approach are ideal.
CONSOLIDATE
Once connectivity has been established, the search solution should enable the building of a unified index, using data format converters to normalize the heterogeneous content. The solution’s text analytics engine then extracts themes, named entities, and even identifies sentiment within the content, while a data enrichment engine automatically tags, classifies and categorizes it according to enterprise taxonomies. During this analytics and enrichment process, the solution should also identify and classify subject-matter experts based upon their specific work-product, communications, and other contextual clues.
Importantly, this unified index should also include real-time (or near-real time) indexing capabilities that scale, in order to always reflect the “right now” state of the company’s various information assets and experts.
CONTEXTUALIZE
Utilizing the insights discovered during the text analytics and enrichment process, the search solution should be able to model relevance for each piece of content and each user across a variety of contextual dimensions. With this model, signals like the user’s job role, geographic location, and the project she is working on can be used in conjunction with her search query to fine-tune the relevance of her search results, or even to automatically suggest helpful content and experts.
ENGAGE
Finally, a search solution that successfully taps into the Long Tail of Knowledge is one that makes it easy and intuitive for the user to use. Knowledge workers need an interface that is optimized to their job role and unique preferences. For example, a marketer might prefer an interface with rich document previews. An engineer, on the other hand, might prefer an interface providing parametric search and a dedicated panel that displays experts based upon the last query run or project opened. The ideal search solution should provide a broad library of plug-and-play modules so administrators can swiftly build numerous, specialized search UIs.
Where exactly knowledge workers interact with the search solution depends upon the company’s unique situation. Often, the search solution might need to be integrated directly into one of the already widely-used platforms within the organization such as a Salesforce or SharePoint-driven corporate intranet. Alternatively, a stand-alone, browser-based interface might be preferred. Given the variety of potential approaches, a successful solution should provide the APIs and tools necessary to swiftly build rich search interfaces on a variety of platforms, and should leverage Responsive Design to ensure a seamless experience for users on mobile devices.
Search-driven technologies have made possible a new era in knowledge management, and by looking to solutions that connect, consolidate, contextualize, and engage, companies can finally capture the Long Tail of Knowledge.
Coveo search and relevance technology harnesses information from anywhere and recommends knowledge and experts relevant to the context of users. Unlock your organization’s collective knowledge and expertise to outperform, out-maneuver and out-nimble any competitor. Learn why the future of knowledge management is search-powered at www.coveo.com or contact us at info@coveo.com and 1.800.635.5476.