In Part 1 of this series, I discussed connecting people to people. In this second part, I discuss connecting people to content.
There are three main ways to connect people to content they seek: navigation, search, and tagging. Navigation is a hierarchy of links managed by a site owner. Search enables user-generated queries that yield results based on crawling a domain. Tagging is done by content owners or by users to associate content with specific topics so that it can be associated with related content and found by users.
Navigation allows users to view a managed set of links and click on any one of them to access desired content. It can be provided through menus, top ten lists, an index, a site map, or a knowledge map.
Users appreciate the offer of multiple forms of navigation so they can use the form they most prefer. Some want to use a traditional menu. Other people may like a top ten list because it considers what other people are using. Some want to avoid sifting through menu options and prefer an index, so they can proceed quickly and directly. Others want guidance on where to go based on their current requirements and will benefit from using a map.
On a website or in an app, a menu is a way of accessing content or navigating to another website or app. This is through a drop-down menu, a horizontal navbar, a hamburger menu (three vertical bars), a kebab menu (three vertical dots), breadcrumbs, or any other set of links presented on a webpage or app.
A good menu is intuitive and easy to use. Users know where to find it on any webpage or app and the items in the list make sense. Breadcrumbs show where you are in a website hierarchy and allow direct navigation to any higher level in the hierarchy.
Here are useful categories for navigating via menus:
- Internal organizational structure
- Formal taxonomy (industry or internal)
- Products and services
- Specialties and roles
Top Ten Lists
Going beyond basic menus, top ten lists can help guide users to the most helpful content and sites by providing links ranked by popularity. Examples of such lists include the top ten most visited sites on the intranet, most downloaded documents, most checked-out books, and or searched-for content. These can be updated automatically or through a monthly refresh process based on the web and content statistics in monthly usage reports.
This is another form of curation, in this case, enabling connection. If you curate top ten lists and offer them as part of navigation, then instead of users just puzzling through where to go, or randomly navigating around, they can go right to a list relevant to what they are seeking and are guided to what to look at first.
A popular navigation tool is an index, consisting of links organized in alphabetical order. This allows users who want to avoid spending time hunting through a menu to look up a topic directly. If they know the exact term for what they need, they can visit the index, find the word that represents their desired topic, and go directly to that site or content.
Include synonyms as in a thesaurus so that regardless of which term a user chooses, they are guided to the right place. For example, under the L section, you might include “Listserv — see Threaded Discussions.”
The final two forms of navigation are site maps and knowledge maps. A site map shows the overall structure of a website and all available pages in a single view.
A knowledge map includes all key resources with an icon for each, their descriptions, and links to each one. These resources are mapped to the different user roles, business processes, and knowledge requirements. This should answer the question, “Where do I go if I play role X, am using process Y, and have need Z.” For example, “I am an acquisitions specialist, I am procuring a new asset for the library, and I need the standard procedure for competitive bidding.” A knowledge map should allow users to quickly zero in on this set of conditions and link to the required knowledge component.
Search is one of the main ways users try to find information. Providing search functionality is essential, but not always easy to do well. Many users are not good at searching. They often just type in a single word and hope they will magically find what they’re looking for.
There are several ways to help connect them to the content they need. Chapter 2 of my book on the 5 Cs discussed best bets and curated answers, but there are other search techniques that should be considered as part of a knowledge management initiative.
Enterprise search tools allow searching for sites, documents, files, list items, content, answers to questions, and other digital information. They allow specifying the scope or domain of the search, whether to search on text or metadata, and how results should be presented.
For many users, search is the primary tool they use to find information, answer questions, and learn about a topic. The success of Google Web Search on the Internet has resulted in the widespread expectation that searching within an organization should work the same way. Users would like to enter just a few words into a search text box and generate a list of results that match exactly what they are seeking. Too many hits are not desirable, nor are too few, nor are irrelevant ones.
There are significant differences between the quality of results returned by an Internet search and from enterprise search. Page ranking is typically done based on a large sample of links, which works well in the gigantic realm of the Internet, but not as well in the smaller confines of an intranet.
Users should be able to narrow or broaden the types of content, the domains or sites, and the range of metadata values to be included in the search. They should be able to search for text strings, metadata values, or content titles. Familiar syntax such as Boolean operators, quotation marks, and command words used by popular search engines such as Google should be offered. The ability to refine searches, use advanced search functions, and remember previous searches should be provided.
Enterprise search should look across all of the organization, including as many internal systems and databases as possible. This prevents people having to individually navigate to each one of those systems, search within it, and then go to the next one to search again. Enterprise search should crawl all available internal content and allow users to search in just one place to find it.
The following features are recommended for inclusion in enterprise search:
- Type-ahead search, autocomplete, incremental search, incremental find, find/filter as you type, or real-time suggestions — to save typing and match new searches to previous ones
- Best bets — presented at the top of the results with thumbnails and links, highlighted in a way that differentiates them from other results
- Authoritative recommendations — marked with special badges that can only be assigned authoritative sources
- Quick answers — self-contained content that offers enough information to possibly avoid the need to click through to other sites
- Options to feed the search to the most relevant ESN group or to a help desk to get answers from real people
- Synonyms — closest matches from a curated thesaurus
- Closest matches from the intranet index
- Closest matches from the organizational hierarchy
- Closest matches from internal and external taxonomies
- Closest matches from user tags
- Related — since you downloaded X, try Y; since you visited A, try B
- Sorted by attributes
- Most visited or downloaded
- Most liked
- Most reused — add “I reused this document” or “I found this useful” button, similar to a like button, but more specific, to all content, and encourage users to click on this button for content they were able to reuse
- Most tagged — allow content to be tagged with “recommended” or “good example” or “proven practice”
- Most recent
Enterprise search may not be able to access all desired content. Federated search is a way of doing multiple searches and returning the results in a single interface.
Some of the content could be external, or it may be in databases that require special authorization. Federated search avoids requiring users to navigate to multiple sites to conduct individual searches.
A federated search tool can store addresses and access codes for restricted internal systems and conduct a series of separate searches, including all such systems or just ones selected by the user. It can access external search engines such as Google and Bing. In addition, it can display all search results in a single view.
Faceted search is a technique that involves augmenting traditional search techniques with a faceted navigation system, allowing users to narrow down search results by applying multiple filters based on faceted classification of the items. A faceted classification system classifies each information element along multiple explicit dimensions, called facets, enabling the classifications to be accessed and ordered in multiple ways rather than in a single, pre-determined, taxonomic order.
Using predefined topics, faceted search shows how many items there are in each topic and allows searching within that topic only. This can greatly narrow the scope of a search to increase the likelihood of finding only the desired items and not being overwhelmed by other extraneous results.
Tagging is a technique that can help people connect to content. Tagging is adding non-hierarchical keywords or terms to content. It allows related items to be listed, searched for, navigated to, and aggregated.
Tags are a form of metadata that users can apply to help them retrieve content according to their own view of how it should be categorized. Tags can be applied to webpages, documents, files, lists, people, photos, music, social media posts, and any other form of electronic content. These tags can also allow others to find content based on a folksonomy.
Tag clouds are a good user interface for presenting tags to users. A tag cloud is a visual depiction of user-generated tags attached to online content, typically using color and font size to represent the prominence or frequency of the tags depicted.
Metadata is information about content. Metadata allows content to be found through browsing, searching, and other means. It defines the context of the information, how it is classified within a taxonomy, and how it is related to other content. Metadata may be applied automatically based on the origin of the content, assigned by the content owner when submitting it to a repository, or added by a knowledge manager to ensure it is done properly.
Metadata should be based on the standard taxonomy defined for the organization. It should be embedded in repository entry forms as mandatory fields with picklists so contributed content is correctly classified. Search engines should offer the option to search by the available metadata fields so that results will be as specific as possible.
Whenever possible, metadata values should be supplied from a table, rather than entered as free-form text in an input field. The reason for this is that if, for example, each user is allowed to enter company names, then there will be many variations, and it will be difficult to find all content associated with one company. If one user enters “Google” and another enters “Alphabet,” the value of metadata is diminished. Offering a picklist containing the standard company names will prevent this problem.
In formal repositories where metadata can be added to each record in the database, this capability can be restricted so only knowledge management professionals are allowed to do so. Or it can be open to anyone contributing to the repository. In either case, the metadata should be limited to terms included in the standard classification that is part of the corporate taxonomy.
For some user-generated content, especially in social software, tagging can be performed by the users. This results in a folksonomy, where users have created their own tags. The tags are not controlled and are not from a master list. This is more egalitarian, and it makes it easier for an individual user to find content they have tagged, but it comes with some drawbacks. Tags can proliferate, with many that are very similar. There can be misspellings and word variations that can start to fragment content instead of having a single unifying tag. There could be five user-created tags to describe the same thing.
A folksonomy is a system in which users apply public tags to online items, typically to aid them in re-finding those items. This can give rise to a classification system based on those tags and their frequencies, in contrast to a taxonomic classification specified by the owners of the content when it is published. Folksonomies, also known as social tagging, are user-defined metadata collections. Users do not deliberately create folksonomies and there is rarely a prescribed purpose, but a folksonomy evolves when many users create or store content at particular sites and identify what they think the content is about. The problem with a folksonomy as opposed to a taxonomy is that there are no imposed standards, and thus inconsistent tags will likely exist for information that should be tagged uniformly.
As opposed to having people apply standard metadata or user-defined tags, auto-classification uses artificial intelligence to have the computer do the work. This process scans documents and content to determine the key subjects and then adds tags automatically.
There are several advantages of auto-classification. It can reduce tag proliferation. Doing the tagging automatically avoids having content with inaccurate or missing metadata. And it can do a better job than the average user of applying all meaningful tags.
But auto-classification is imperfect and may not do nearly as good a job as an expert taxonomist could. It is still an emerging technology, and one worth evaluating and piloting.
In Part 3 of this series, I discuss connecting content to content.