Understanding Taxonomy and Metadata

EK defines a taxonomy as a “controlled vocabulary used to describe or characterize explicit concepts of information, for the purpose of capturing, managing, and presenting information.” Metadata, therefore, can be thought of as the building blocks of a taxonomy; it is “descriptive data about a content item” or, in other words, characteristics of a document that allow you to categorize it. When working with clients, I like to have them visualize taxonomies as a series of drop down lists that can be used to categorize and tag their content. Some dropdown lists will have multiple levels (hierarchies) that allow users to determine the level of specificity and depth that is applicable, while other fields are a single level list (flat). For example, one of our clients enlisted our services to design a legal taxonomy that could be used to help them find critical contracts across their global offices. Metadata that made up this legal taxonomy included fields such as “Subject Matter” (a list of hierarchical terms 3-levels deep that captured what a contract was about), “Geographic Location” (a flat list of terms that captured the different global regions a contract could be executed by the organization) and “Document Type” (a hierarchical list of terms 2-levels deep that captured the various types of legal documents utilized by the organization). The terms that made up each of these metadata fields were used to tag, find, and search for legal documents in their system.

Understanding Search Filters

As my colleague Jenni Doughty pointed out in her blog “How Do I Implement a Taxonomy?”, one of the primary determinations that needs to be made when implementing a taxonomy is deciding how to implement each metadata field into the system, particularly when the use-cases, hierarchy, and quantity of terms within each metadata field can vary greatly. For SharePoint Online, a crucial consideration is how to best translate metadata into search filters, or the options that pop up on the left-hand side of a search results page that a user can select to narrow down their search results (see a snapshot of Enterprise Knowledge’s open-access Knowledge Base below for an example of an “Article Type” search filter).

Search filters are a powerful tool in your organization’s Knowledge Management (KM) arsenal, as they allow users to go beyond employing keywords in their search queries to track down the content item(s) that will help them take a necessary action. Search filters are largely driven by taxonomy fields; not only do search filters themselves directly align to taxonomy fields and their terms, but they are also fueled by content being tagged with the same terms that are captured in filters on the search results page (It is important to note, however, that there are search refiners for non-taxonomy metadata fields in SharePoint, such as date ranges, that allow a user to limit the search by a date metadata field). Ultimately, search filters not only make search an interactive experience for users, but gives users the ability to quickly and easily adjust their search results within a system like SharePoint Online. For more information about the technical side of search filtering, see my colleague’s blog, How to Optimize Search Relevance: Boosting and Filtering.

Metadata as Search Filters, Out-of-the-Box SharePoint Online

For out-of-the-box SharePoint Online (or SharePoint without add-ons or customizations), it is not currently possible to capture hierarchy in search filters, meaning that nested terms and parent-child relationships are not able to be visually or systematically depicted within search filters. Because of this, it is pivotal that your organization not only determines what metadata fields will best serve users as search filters, but also, whether displaying a single, selected level of a hierarchical field will provide users with an optimal and intuitive search experience.

Let’s put this into practice. For many of our clients, a metadata field for “Topic” is a great candidate for a search filter, as many users want to narrow down their search results by selecting terms that capture what a content item is about. Topical metadata fields, however, often contain up to 4-levels of hierarchy, are rooted in parent-child relationships, and can contain hundreds of different terms. For instance, your “Topic” metadata field might have term relationships that span 4-levels, like the following:

Because out-of-the-box SharePoint Online does not capture hierarchies or nested terms in its search filters, implementing every term in this type of field as a search filter would result in a single level list of hundreds of terms that would not only ignore the relationships that exist in your taxonomy, but would be visually burdensome and confusing for a user to sift through in order to find what they need. If we stick to our example with the 4-level term relationships that might exist in your “Topic” metadata field, this would mean that the terms detailed in the table above, if directly translated into search filters in SharePoint Online, would each be treated as individual entities in a flat list:

As depicted in the image above, when this full set of terms in “Topic” is implemented into SharePoint Online, the lack of ability to nest child-terms underneath parent terms makes it difficult for users to discern that any term relationships exist at all. Furthermore, in a real-world scenario where it is likely that an organization’s “Topic” metadata field contains hundreds of unique terms at various levels, it becomes difficult for users to digest the quantity and breadth of terms when there is no logical order to them in terms of their concept or level of specificity.

Because of Sharepoint’s out-of-the-box limitation with search filters, EK typically recommends employing a single level of a hierarchical field as a search filter (typically the top level) rather than the full exhaustive term list, particularly if this metadata field will help users narrow down their search results to find or discover the content that they need to do their jobs. Continuing with our 4-level metadata field “Topic,” selecting just the Level 1 terms as search filters would result in a more streamlined, understandable set of filters:

By selecting a single level of a metadata field, such as “Topic” for a search filter, it becomes paramount that your organization’s taxonomy is also implemented in a way that ensures all content items tagged with a more specific, deeper level term that also inherits the tag(s) of the parent level terms. For example, if the most applicable tag for a content item is the topic of “Chickpeas,” we would also ensure that the content item be tagged with the parent terms, which in this use case would include “Small Grains & Legumes,” “Crops,” and “Agriculture.”

By implementing a single level of a hierarchical metadata field into SharePoint Online’s search filters, users are still able to benefit from the ability to narrow search results by concepts such as “Topic” without becoming overly burdened by a long list of terms. In this way, your organization can use this method to help strike the balance between employing SharePoint’s native capabilities and ensuring that users have an optimized search experience.

Search Filter Alternatives: SharePoint Web Part Solution

If you are able to go beyond SharePoint Online’s out-of-the-box capabilities, however, it is worth considering whether translating the full hierarchy of a metadata field into search filters would provide users with enough value to warrant the purchase of a license for a web part solution that enables the nesting of search filters. While SharePoint Online alone does not allow for metadata hierarchy or term relationships within search filters, there are web parts available, such as the Aequos Modern Data Visualizer web part solution (for SharePoint Online modern), that can be purchased and configured with your organization’s SharePoint Online instance. While these types of technical solutions often require some additional customization and development work, when implemented according to best practices, they can give your organization’s users the ability to utilize more dynamic filters that recognize term relationships with the employment of different drop down levels:

Conclusion

Strategically translating your taxonomy into search filters in SharePoint Online will enable your organization to empower users to self-serve within and across your SharePoint sites, while fueling content findability and discoverability that leads to increased productivity and job satisfaction. At Enterprise Knowledge, we have helped numerous clients optimize their SharePoint Online instances, partnering on projects that range from smaller scale department taxonomy designs to the design and development of full SharePoint Online environments. Interested in learning more? Contact us today.

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As investment consultants, my parents were contacted by a variety of people looking for advice on how to handle their finances, from a small savings account to million dollar inheritances. These conversations with customers were usually over the phone, and it was my parents’ job to turn those quick calls into actual client leads. In order to answer difficult questions and sound like experts in the financial world, they depended on Charles Schwab’s intranet and internal help desk. This information database and direct access to human resources supplied employees with a wealth of knowledge in a time when the internet was brand new and most companies didn’t have websites to advertise their products and services.  

Knowledge Management, in its simplest terms, is about connecting the right information to the right people at the right time. This concept is especially important in organizations where contact centers and help desks are a significant business function and employees with less experience may be speaking directly with potential clients or customers. Every employee in an organization, regardless of age or experience, should be equipped with the knowledge and tools they need to become an expert in their field and successfully communicate their company’s breadth of information and services to people who call in. In addition to the KM tools that will be detailed later in this blog, company leadership should prioritize and foster a culture of knowledge sharing, where information is routinely organized and shared in the correct channels and systems for enterprise-wide use. 

Help desks are frequently the primary point of contact for customers who have questions about the company’s products or services. Given frequent turnover and a difficult hiring environment, companies struggle to hire and retain the most knowledgeable people for their help desks. This is a problem, but cannot be an excuse. If experts are not available to fill these positions, those employees who do receive the calls need direct access to use cases, platform permissions, step-by-step instructions, links, and everything else that would be required to resolve the customers’ problems. 

Contact centers often serve a much broader purpose and are essentially a customer service department that handles customer complaints, orders, inquiries, etc. In this case, callers may already be frustrated, upset, or dissatisfied, making seamless access to the right information at the right time even more critical to deliver quality customer service. 

Apart from the knowledge sharing culture and values that a strong KM foundation provides, there are several specific KM tools that we recommend for superior customer service in any of these business areas:

	Knowledge Base A knowledge base is a repository of enterprise-wide knowledge that should be the primary source for call agents to solve and respond to customer queries. Similar to the one that my parents described working with all those years ago, a functional knowledge base should have intuitive search capabilities and a user interface that allows for easy and rapid navigation. This will improve employees’ experience as well as customer satisfaction, as employees will feel confident and empowered when they have the necessary resources at their fingertips to excel in their jobs.
	Artifacts  Call agents should also have direct access to artifacts that can be sent directly to customers for more detailed information or future reference. These can be FAQs, articles, how-to guides, device instructions, videos, or any other simple visual guide that can act as a follow-up to a customer call.
	Intelligent Chatbots There is no denying that in many companies, contact centers and help desks have declined in usage as customers have more and more access to self-service channels. Tech-savvy customers expect a useful and streamlined self-service experience, especially when contacting a larger company. Artificial intelligence (AI) tools like chatbots can be extremely effective and dependable to solve customer problems and provide human-like resolutions using Natural Language Processing (NLP). When integrated with a knowledge base and visual guides, call agents can deflect calls directly to a chatbot with the confidence that those customers will get the answers they need as quickly as possible. This type of holistic support ensures that an organization provides assistance to every single customer while keeping employees from burnout and reducing support costs.

By establishing systematic and repetitive ways to deliver information to customers, an organization will possess consistent and positive customer experience as one of its key differentiators. Modern-day customers are used to digital self-service, but we all know how frustrating it can be to dial numbers over and over without ever finding a sufficient answer to a query. Until chatbots can entirely replicate human assistance, prioritizing Knowledge Management for contact centers and help desks will continue to improve customer service Key Performance Indicators (KPIs) and give service organizations a competitive advantage unlike any other. 

Measuring customer service KPIs is a great way to quantify the effectiveness of KM in these parts of an organization. These KPIs can vary depending on the organization, but they include metrics like agent training time, agent errors, repeat calls, mean call time, resolution time, etc. Effective KM can help your organization lower customer service costs by reducing the time and efforts agents spend responding to customer inquiries, thereby building a strong business case for continued KM transformations. Are customer issues usually resolved during first contact? Are customers experiencing faster resolution times? Are agents prioritizing proactive development of self-service content based on common issues faced by customers? These are questions that KM stakeholders should seek answers to in order to identify service gaps in these departments and measure Return on Investment (ROI) from the implementation of the tools described above. These tools can be highly effective in improving these KPIs, and organizations should develop reporting that shows hard progress against these metrics to garner buy-in and support for KM efforts.

Once implemented, these tools can immediately begin demonstrating the benefits of KM for contact centers and help desks:

• Findability: With consistent and intuitive tagging of all content within a knowledge base, a call agent will be able to find direct answers to customer queries faster, easier, and more completely. A clear and easy user interface within a self-service portal will allow customers to quickly find answers to their questions and understand what an organization has to offer them. 
• Consistency: Information governance is a key tenant of good KM. An organization should establish governance processes for its knowledge base to ensure content remains new, accurate, and complete for call agents’ reference, varying from content reviews to ownership to workflows. Here at EK, we have seen countless knowledge bases overrun with outdated and obsolete content, and good governance practices are the best way to counteract that trend. 
• Collaboration: As mentioned before, a culture of knowledge sharing is a powerful way to ensure call agents and support staff are equipped for any customer question, even without the implementation of actual KM tools. Agents can work confidently knowing that they are surrounded by others who are willing to help and distribute knowledge in whatever way they can, adding another resource for agents who cannot immediately find what they are looking for in a knowledge base.  
• Consumability: Structured content (that with predefined formats and organization) will be easy for agents to read, quickly understand, and then act upon. Good KM will ensure content is delivered to agents in the right format, scale, and scope for the situation, maximizing readability and minimizing cognitive load. 
• Flexibility: Most of the time, an agent will need a quick and concise answer for a customer. However, in times when deeper answers are needed or desired, agents will have opportunities and resources to explore related content that is tagged similarly in the knowledge base.
• Supportability: Good KM dictates clear job roles and organizational structure. In more serious situations, agents will know when and to whom the situation should be escalated.

EK has experience with many projects of this nature, utilizing KM best practices to improve the efficiency of contact centers and help desks. One example is the work we did with the principal revenue collection agency of a national government overseas. In this engagement, the agency was having difficulty standardizing and managing content in their internal tool designed to guide service agents towards the correct information they need to support their customers. To help these service agents more easily locate content and navigate complex regulations and concepts, EK provided comprehensive Content Transformation Services which included Content Strategy and Governance Design. As a result of these efforts, the agency was positioned to standardize the way information is captured and managed across the enterprise, enabling content to become more findable, scannable, and intuitive to follow for service agents. Service agents spent less time finding applicable content within their internal tool, translating to a decrease in mean-time-to-resolve (MTTR) customer inquiries. 

Overall, Knowledge Management in contact centers and help desks makes it smoother and more efficient for agents to find and use information. Customers expect and will often demand timely, personalized service; if these needs are not met, the organization will likely lose that customer. Every organization with a contact center or help desk must make sure their agents are equipped and empowered with the right knowledge and tools to correctly answer questions and provide relevant information. By investing in KM in these areas, your organization can ensure the satisfaction and longevity of both customers and employees. Here at EK, we offer many services to help organizations improve document management, content governance, search functionality, and so much more that can further the best practices detailed above. If you think your organization could benefit from Knowledge Management, contact us today to learn more about our services.

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Motivation

Motivation is a complex factor to understand, but psychologists, neuroscientists, and learning theorists have amassed quite a body of research. We know that motivation can be positively influenced by intrinsic motivation, experiences of success, and overall positive system user experience.

Learning ecosystems that leverage curiosity and interest to drive intrinsic motivation create much better learning outcomes than learning ecosystems which depend upon compulsory training or fear. There are obviously a lot of process and cultural elements that influence curiosity and interest-driven learning, but knowledge management technology has a role to play as well. In knowledge management, we talk a lot about the findability and discoverability of information:

• Findability describes the ability of a system user (in this case a learner) to find the information for which they came to the system. If I want to learn the basics of graphic design, I might execute a search for “graphic design basics.” Findability refers to my ability to find a beginner eLearning course so I can get started.
• Discoverability describes the ability of the learner to discover new information in the system which is useful – but for which they weren’t even searching. In the example above, I search for “graphic design basics” and find an eLearning course, but I also find an entire training plan with multiple levels of graphic design proficiency and supporting learning assets for each. I didn’t know those additional resources were there, but I’m thankful to discover them as they provide me not only with the course, but with a roadmap to continue advancing my skills.

A well-designed knowledge management portal supports both findability and discoverability of learning assets. Enabling the discoverability of additional learning assets and learning paths inspires curiosity and helps create an intrinsic motivation to learn.

Research shows that learners who experience success are also motivated to keep learning. Knowledge management technology can build success experiences into your organization’s learning ecosystem by automatically conferring certificates when learners complete metadata-enabled learning paths. Knowledge management technology can also create personalization of feedback by leveraging some of the same tools we use to deliver a multitude of content personalization experiences – componentized content and a robust metadata strategy.

Motivation is also strongly linked to the overall user experience a learner has with the learning technology. Learning is a process which requires sustained attention and effort, and if a learner is frustrated with outdated information, a lack of cues to guide attention, or visual clutter which creates cognitive overload, motivation is greatly reduced.

Attention

It is difficult for learners to sustain attention, and many learning activities take place in an online environment where there is fierce competition for that attention. Many traditional training approaches rely on unrealistic expectations of our ability to pay attention. Full-day, instructor-led workshops or even hour-long webinars are examples where learner attention can drop off drastically.

Knowledge management technology can provide solutions to this problem. Componentized content can enable the chunking of educational content in such a way that the same core components of content are reusable across multiple learning contexts. SCORM packages promised this benefit, but SCORM was only designed for reuse within eLearning courses. With ever-increasing demands on learner attention, we know that diverse learning opportunities – including informal learning and social learning – are absolutely critical. Componentized content in a CCMS can actually enable the reuse of content in any context – not just in courses.

A Headless CMS delivery architecture can provide further benefits and allow for the personalized delivery of these reusable learning asset components across multiple learner experiences. If you’ve created a reusable learning asset that explains how to create a budget report, a Headless CMS would enable you to publish that information to:

• A checklist that provides context for a project manager to create and update the report; and
• An explanatory reference sheet for a department director that explains how to apply the information in the report for department-level strategic planning.

When we leverage the latest knowledge management technology to create reusable, componentized learning assets, which can be reused across multiple learning experiences, we allow ourselves to create shorter, varied, and personalized learning experiences, which will help our learners sustain their attention and improve learning outcomes.

Summary

A modern workforce faces many demands for their time and attention and it’s easy for learning to get put on the back burner – even for those of us who love learning. When designing a learning ecosystem, it’s important to remember the learning theory that helps us best support learners and set them up for successful learning outcomes. Supporting learner motivation and attention are key. Knowledge management technology has the potential to improve the motivation and attention of learners – and thereby increase learning outcomes. If you’d like to apply knowledge management best practices to the design and development of your learning ecosystem, EK can help.

The post Knowledge Management Technology to Improve Learning Outcomes appeared first on Enterprise Knowledge.

My colleagues have previously written about the importance of these use cases, how to define them, and how to determine complexity based on each scenario. In this blog, I’ll focus on a few simple and common use cases that we have tackled lately here at EK. My next blog will be a follow-up on designing your taxonomy for a more advanced use case. Each of the use cases I discuss in this blog have specific considerations and requirements that influence taxonomy design, so let’s dive into each one and the specific design considerations that follow.

Search/Findability

A common use case for a taxonomy is search/findability. When staff are unable to find company content through search in their corporate intranet, document management systems, or knowledge bases, a taxonomy can help. A taxonomy improves the findability of content and knowledge at an organization by providing distinct tags and key terms that describe content to help the items surface in search. When designing a taxonomy for a search use case, there are a few considerations to keep in mind.

Synonyms are key in a taxonomy designed for search/findability. For a term to successfully and frequently appear in search results, you should add relevant and equivalent terms as synonyms. You should consider including as synonyms other terms such as common misspellings, alternative terms, or past names that the term may have had. This allows users to find information based on the way they intuitively describe and look for things, improving findability. This also accommodates a variety of users who may use different terms to describe the same content, therefore allowing your taxonomy to be more usable and complete. For example, if a user is searching for content related to the term “boat,” but all content related to boats is tagged with the term “ship,” it would be important to ensure that “boat” is added as a synonym for “ship” so that the user can find their desired content when searching with either of these terms.

When designing a taxonomy for search/findability, you should dedicate focus to a topical taxonomy. A topical taxonomy describes the “aboutness” of content, or the meaning and context behind the content that may not be explicitly called out in a given document. For example, for one of our clients in the healthcare industry, we developed a topical taxonomy that categorizes terms such as conditions, disorders, and medical procedures, all which represent what the content references. A well-designed topical taxonomy supports search because it reflects how users often search – for what the content is about.

Usability is another important consideration for this use case, regarding the amount and design of the levels of the taxonomy. If the main use case of the taxonomy is to support search solely behind the scenes, meaning the taxonomy will not be visible to users, you may decide to design a more flat taxonomy, with only a couple of levels, as users do not see or need the hierarchy when solely searching instead of browsing. A relatively flat taxonomy also supports filtering during search, as users do not want to get caught up in a complicated hierarchy when they are looking for terms to filter their results.

For example, EK’s intranet taxonomy on the left supports filtering during search

Some hierarchy to the taxonomy is needed if content authors are manually tagging content, as hierarchy helps content authors navigate the taxonomy to tag their content, but limiting the hierarchy for a search use case is a strong option. Furthermore, granular terms in the lower levels of the taxonomy, or the first level in a flat taxonomy, are most important to support keyword search, as these terms reflect the words users use to search.  

Browsing/Navigation

For a browsing/navigation use case, however, the design considerations of a taxonomy will differ. Sometimes a user will find more value in browsing for the answer to their question instead of searching for it, as browsing allows them to view the general context and relationships of a topic as well as discover new topics and answers they may not have known they needed. For instance, a staff member may browse their corporate intranet for a specific HR policy and discover new policies or details they were not previously aware of.

So, if this represents your use case, you’ll want to design a taxonomy that reflects the specific terms that users use in their daily lives, especially since synonyms will not be of much assistance in this case. Users will rely only on the taxonomy terms presented to them in the hierarchical navigation format. You should conduct focus groups, interviews, and thorough analyses to source the highest level taxonomy terms and overall, ensure the taxonomy you create and present for navigation is both usable and understandable to end users.

Similarly, you may also want to consider developing a taxonomy with only one or two levels so as to limit the amount of clicking a user needs to do to locate the content they’re looking for.

For example, EK’s intranet taxonomy supports navigation to view our services, as seen through the two taxonomy levels displayed above

One situation we often encounter at EK is that our clients want to differentiate how a user browses content within systems such as a knowledge base or the corporate intranet based on specific departments or business areas. The browsing/navigation use case supports a taxonomy at the enterprise level; however, personalized views based on department may be accomplished through user views within the system the taxonomy is contained in and where the user is searching. 

Conclusion

While the findability and browsing/navigation use cases may be considered basic use cases for a taxonomy, they present important opportunities to showcase the true value of a taxonomy. In my next blog, I will describe two more advanced taxonomy use cases and the best ways to design your taxonomy with them in mind.

Want help designing your taxonomy for a findability or navigation use case? We can help! Contact us at info@enterprise-knowledge.com to get in touch.

The post Designing Your Taxonomy to Fit Your Use Case: Common Taxonomy Use Cases (Part 1) appeared first on Enterprise Knowledge.

Step 1: Review Your Primary Taxonomy Use Cases

Whether you are trying to tackle some of the most common use cases (e.g., search, browsing, overall findability) or more advanced use cases (e.g., predictive analytics, chatbots, recommendation engines), it’s important to understand the business challenge you are trying to solve, or the new functionality you are looking to implement. A taxonomy is most effective when implemented in support of a specific business need or use case, and when each metadata field provides direct value in support of that need. It is not sustainable to implement every metadata field imaginable, as the manual burden and time needed to apply those tags can overshadow the intended value to users. As a result, we recommend implementing metadata fields strategically, in support of clear use cases, and not overburdening a system or users with too much metadata. 

For this blog, let’s imagine that our primary use case is improving the findability of both content and people on our organization’s knowledge base. Our organization sells products and services in the Information Technology industry, everything from software licenses to implementation and consulting services. Over the past year, we’ve recognized the importance of a useful and user-friendly repository for sharing knowledge amongst colleagues, keeping up to date on current offerings, and being able to provide our customers with real-time information. As a result, we are working on a pilot with the goal of improving findability in our knowledge base, and have therefore designed a taxonomy that consists of the following metadata fields: 

Along with designing our taxonomy, we’ve identified that we want to use said taxonomy as both search filters and navigation menus to improve the findability and discoverability of information in our knowledge base. We’ve also defined key criteria for each field to help us understand how each one may be used in support of our use cases: 

• The kind of field (hierarchical or flat list);
• The composition and size of the field (how many levels of hierarchy and how many terms); and 
• The scope of the field (primary – applicable to all content or secondary – applicable to subsets of content).

Step 2: Determine How to Implement Each Field

For immediate application of each of the fields from the taxonomy on content in our knowledge base, we need to determine how each field should be implemented based on our use cases. The three most common applications of metadata fields in a knowledge base are as navigation menus, search filters, or synonym dictionaries for the search index. Our taxonomy can be used as a tool to support each of these features in potentially different ways. For example, Function might be a good candidate for a navigation menu, so that employees who work within each function can readily find content related to their roles. 

As you saw in the table above, Topic is a hierarchical list with 3 levels of depth and over 400 terms. As a result, we need to consider its size and composition when implementing. We’ve indicated that we would like to use Topic as both a search filter and to add synonyms to our search dictionary so users can enter similar or equivalent terms and receive the same results (e.g., SaaS and Software as a Service). The hierarchy and size of Topic doesn’t have any implications on the implementation of synonyms, but in order to use it for search filters, we need to make some decisions. 

Search filters often appear on the left side of a search results page and in most systems, can only display as flat lists, without hierarchy. This is largely due to system limitations and/or the concern of overburdening a user with too much information on one screen. Can you imagine if you had search results with 4 filters, one of which has over 400 terms listed? That would most likely mean the filters on the left would require scrolling or expanding to even be able to read all of the terms. Instead, we can do some research with users to understand if there is a specific level of Topic that would be most helpful for filtering content in a search result. For example, Level 1 of Topic has only 15 terms, which would be a much more reasonable filtering list. Document Type, Project Phase, and Customer Location may also be search filter candidates, and with simple, flat hierarchies, they will not have the same complexity as Topic.

Step 3: Determine Which Fields to Implement First

Our taxonomy has five metadata fields, three of which are primary and two are secondary. In order to decide whether or not we should tackle implementing them all as part of this initial pilot of our knowledge base, let’s go back to the use case and decide what will give us the most value for our effort. Our knowledge base improvement pilot is focused on improving the findability of operational content related to our offerings and our current customers. We are not including project-related content in this first iteration. Knowing this, it likely makes the most sense to spend our time and effort on implementing Topic, Document Type, and Function as they are primary metadata fields, and will be applicable to all content in the knowledge base. We may also want to implement Customer Location as one of our initial information types in the pilot is customer information, but we can wait to implement Project Phase as we aren’t including that content yet. By implementing the most important or relevant metadata fields first, we can save some time and effort in the pilot.

Step 4: Establish Metrics to Measure Taxonomy ROI

During and continuing after implementation of the primary taxonomy, keep track of any key performance indicators (KPIs) or metrics that you can use to measure the Return on Investment (ROI) of your new taxonomy. The value of the taxonomy lies in the support of our intended use cases, in addition to being a foundation for future efforts. It may be helpful to evaluate both the taxonomy and its ROI by using the four themes of Alignment, Usability, Completeness, and Readiness, remembering that the ROI of the taxonomy, both hard and soft, can be found in the use cases which are improved by the taxonomy. In our example use case of improving findability in the knowledge base, our potential ROI metrics may come from documenting the time spent searching for information, a reduction in unsuccessful searches, or the tracking of search terms and logs, to name a few starting points. Ideally, once we’ve identified which metrics we would like to track, we should take baseline measurements before the implementation is complete so that we can track improvements based on our new taxonomy.

Step 5: Establish Governance & Iteratively Implement Additional Fields

As soon as you’ve begun implementing the taxonomy, it’s important to begin meeting as a taxonomy governance team and reviewing user feedback from the implementation in real-time. Remember that all changes or suggestions must be evaluated from the enterprise perspective to ensure standardization and to analyze impacts for all stakeholders.

Then, as an operational taxonomy governance team, you can work with your stakeholders to implement additional metadata fields from the taxonomy as necessary. Often these fields are secondary or tertiary for the initial use case, or primary for new use cases. For example, once we’ve decided to bring in project-related content, we may want to consider implementing the Project Phase metadata field and tagging content appropriately as it is migrated into the knowledge base. The governance team will also receive and process requests for additional secondary metadata for specific teams or subsections of content, and ensure the taxonomy grows in a sustainable and scalable manner.

Conclusion

Many of our clients have little to no metadata applied to their content when we first engage. Or, in a few cases, they have a ton of disparate systems all with their own metadata. In either situation, it’s not enough to just design a new taxonomy. We also need to implement the taxonomy in a way that is usable, intuitive, and serves our users’ information needs. 

If this sounds like your organization, we’d love to help you tackle taxonomy design, validation, and implementation. Contact us!

The post How Do I Implement A Taxonomy? appeared first on Enterprise Knowledge.

One of, if not the most prominent, causes of this inability to quickly find information that EK has seen more recently is that an organization possesses multiple content repositories that lack a clear intention or purpose. As a result, users are forced to visit each repository within their organization’s technology landscape one at a time in order to search for the information that they need. Further, this problem is often exacerbated by other KM issues, such as a lack of proper search techniques, organization mismanagement of content, and content sprawl and duplication. In addition to a loss in productivity, these issues lead to rework, individuals making decisions on outdated information, employees losing precious working time trying to validate information, and users relying on experts for information they cannot find on their own. 

Along with a solid content management and KM related strategy, EK recommends that clients experiencing these types of findability related issues also seek solutions at the technical level. It is critical that organizations take advantage of the opportunity to streamline the way their users access the information they need to do their jobs; this will allow for the reduction of time and effort of users spent searching for information, as well as the assuage of the aforementioned challenges. This blog will explain how organizations can proactively mitigate the challenges of siloed information in different applications by instituting a unique set of technical solutions, including taxonomy management systems, metadata hubs, and enterprise search, to alleviate these problems.

With the abundance and variety of content that organizations typically possess, it is often unrealistic to have one repository that houses all types of content. There are very few, if any, content management systems on the market that can optimally support the storage of every type of content an organization may have, let alone possess the search and metadata capabilities required for proper content management. Organizations can address this dilemma by having a unified, centralized search experience that is able to search all content repositories in a secure and safe manner. This is achieved through the design and implementation of a semantic layer – a combination of unique solutions that work together to provide users one place to go to for searching for content, but behind the scenes allow for the return of results from multiple locations.

In the following sections, I will illustrate the value of Taxonomy Management Systems, Enterprise Search, and Metadata Hubs that make up the semantic layer, which collectively enable a unique and highly beneficial set of solutions.

Taxonomy Management Systems

In order to pull consistent data values back from different sources and filter, sort, and facet that data, there must be a taxonomy in place that applies to all content, in all locations. This is achieved by the implementation of an Enterprise TMS, which can be used to create, manage, and apply an enterprise-wide taxonomy to content in every system. This is important because it’s likely there are already multiple, separate taxonomies built into various content repositories that are different from one another and therefore cannot be leveraged in one system. An enterprise wide taxonomy allows for the design of a taxonomy that applies to all content, regardless of its type or location. An additional benefit of having an enterprise TMS is that organizations can utilize the system’s auto-tagging capabilities to assist in the tagging of content in various repositories. Most, if not all major contenders in the TMS industry provide auto-tagging capabilities, and organizations can use these capabilities to significantly reduce the burden on content authors and curators to manually apply metadata to content. Once integrated with content repositories, the TMS can automatically parse content, assign metadata based on a controlled vocabulary (stored in the enterprise taxonomy), and return those tags to a central location.

Metadata Hub

The next piece of this semantic layer puzzle is a metadata hub. We often find that one or more content repositories in an organization’s KM ecosystem lack the necessary metadata capabilities to describe and categorize content. This is extremely important because it facilitates the efficient indexing and retrieval of content. A ‘metadata hub’ can help to alleviate this dilemma by effectively giving those systems their needed metadata capabilities as well as creating a single place to store and manage that metadata. The metadata hub, when integrated with the TMS can apply the taxonomy and tag content from each repository, and store those tags in a single place for a search tool to index. 

This metadata hub acts as a ‘manage in place’ solution. The metadata hub points to content in its source location. Tags and metadata that are being generated are only stored in the metadata hub and are not ‘pushed’ down to the source repositories. This “pushing down” of tags can be achieved with additional development, but is generally avoided as not to disrupt the integrity of content within its respective repository. The main goal here is to have one place that contains metadata about all content in all repositories, and that this metadata is based on a shared, enterprise-wide taxonomy.

Enterprise Search

The final component of the semantic layer is Enterprise Search (ES). This is the piece that allows for individuals to perform a single search as opposed to visiting multiple systems and performing multiple searches, which is far from the optimal search experience. The ES solution acts as the enabling tool that makes the singular search experience possible. This search tool is the one that individuals will use to execute queries for content across multiple systems and includes the ability to filter, facet, and sort content to narrow down search results. In order for the search tool to function properly, there must be integrations set up between the source repositories, the metadata hub, and the TMS solution. Once these connectors are established, the search tool will be able to query each source repository with the search criteria provided by the user, and then return metadata and additional information made available by the TMS and metadata hub solutions. The result is a faceted search solution similar to what we are all familiar with at Amazon and other leading e-commerce websites. These three systems work together to not only alleviate the issues created by a lack of metadata functionalities in source repositories, but also to give users a single place to find anything and everything that relates to their search criteria.

Bringing It All Together

The value of a semantic layer can be exemplified through a common use case:

Let’s say you are trying to find out more information about a certain topic within your organization. In order to do this, you would love to perform a search for everything related to this certain topic, but realize that you have to visit multiple systems to do so. One of your content repositories stores digital media, i.e. videos and pictures, another of your content repositories stores scholarly articles, and another one stores information on individuals who are experts on the topic. There could be many more repositories, and you must visit each one separately and search within each system to gather the information you need. This takes considerable time and effort and in a best case scenario makes for a painstakingly long search process. In a worst case scenario, content is missed and the research is incomplete.

With the introduction of the semantic layer, the searchers would only have to visit one location and perform a single search. When doing so, searchers would see the results from each individual repository all in one location. Additionally, searchers would have extensive amounts of metadata on each piece of content to filter to ensure that they find the information they are looking for. Normally when we build these semantic layers the search allows users the option to narrow results by source system, content type (article, person, digital media), date created or modified, and many more. Once the searcher has found their desired content, a convenient link is provided which will take them directly to the content in its respective repository. 

Closing

The increasingly common issue of having multiple, disparate content repositories in a KM technology stack is one that causes organizations to lose valuable time and effort, while hindering employees’ ability to efficiently find information through mature, proven metadata and search capabilities. Enterprise Knowledge (EK) specializes in the design and implementation of the exact systems mentioned above and has proven experience building out these types of technologies for clients. If your company is facing issues with the findability of your content, struggling with having to search for content in multiple places, or even finding that searching for information is a cumbersome task, we can help. Contact us with any questions you have about how we can improve the way your organization searches for and finds information within your KM environment.

The post The Importance of a Semantic Layer in a Knowledge Management Technology Suite appeared first on Enterprise Knowledge.

When talking with our clients about their knowledge management needs, we frequently hear examples like this that highlight the importance of accessing information at the time of need so that staff can do their jobs efficiently. Oftentimes staff members can’t quickly find the content they need, or they’re sifting through outdated, unusable information, or the content may not even exist. 

It is important to note that accessing information at the time of need is only the end component of the larger process. In order to make accessing the content possible, it must first be captured in a meaningful format so that others may act on it. For many organizations, this is such a broad challenge, they don’t know where to begin even when they know they’re suffering from the results of poor content capture.

In these cases, we guide organizations to approach this strategically, focusing on capturing the highest value content in order to make a seemingly insurmountable challenge achievable. The first step in this approach is to identify important moments when key content is created, before finally capturing that valuable content in a consistent way at the time of creation and making it accessible across the organization. The key is starting small and identifying these high value moments of content capture.

How Do We Identify High Value Moments of Content Capture?

So, you’ve decided to start small and standardize your content capture behind a high value moment. How do you figure out when these moments occur in your business cycles?

EK’s approach to content capture does not revolve around a piece of content, but instead around what event triggers the creation of the piece of content. These high value moments are key moments in your company’s business processes or cycles. To put it simply, consider the moments when your company is at its highest point, and when your team is coming together to celebrate. Think about the times when you pop open a bottle of champagne.

In a pharmaceutical company, these moments might be getting approval for a study from the government or the conclusion of a successful clinical trial. For a real estate company, these moments might be closing a large deal with a client, starting development on a new building, or launching a new service.

Publicly-traded companies, on the other hand, are often defined by the moments their stock price rises and falls. Evaluate the moments when your stock rises exponentially and identify the patterns of activities that led to this rise. These activities are your high value moments of content capture. 

One of EK’s own personal high value moments is at the close of a project. Our EK Rockstars take advantage of this high value moment to not only celebrate our successes, but ensure that essential pieces of content are effectively preserved.

Successfully identifying these high value moments of content capture is important because at these specific moments, an individual or team is taking an action and consciously or unconsciously drafting a plan to move forward. Knowledge should be captured at the moment it is created, as these moments are when opinions are shaped and when the content is the most relevant in context. This allows more of the tacit knowledge, or knowledge shaped by experiences that often lives in an individual’s head, to be effectively captured as well. During the time of creation, an individual can most efficiently and appropriately capture the implicit ideas and stories around a piece of content, such as: Why is this information important? What is the story behind this piece of content? Why or why was it not successful? Incorporating this information into the capture of the content ensures it is not lost over time. The tacit knowledge behind a piece of content gives other staff members key information and context through which to apply the content to their own needs down the line.

How Do We Capture the Content at These High Value Moments?

Now that you’ve identified your company’s high value moments of content capture, how do you institutionalize a consistent practice of content creation and preservation around this moment? Use the following considerations to implement the process:

1. Include Highly-involved Individuals
2. Standardize Workflow
3. Set Expectations
4. Embed Processes

1. Include Highly-involved Individuals

First, think about the people who are the most involved in generating knowledge associated with the important moment. The knowledge derived from these individuals comes in two main forms: structured content and tacit knowledge from experience. It is essential that both of these types of knowledge are captured for

 the high value moment. Interview the staff members you have identified to be highly involved. Take account of what pieces of content they are producing associated with the high value moment, and ask them about what may not be captured in the content itself, such as the story and context behind a deliverable and why it was or was not successful. Depending on insight from your highly-involved individuals, the types of content you decide to capture may range from project deliverables to meeting notes to lessons learned. 

At the close-out of a project at EK, the key pieces of knowledge formulated by the team members highly involved in the project include lessons learned. EK Rockstars take advantage of the high value moment of a project close-out to not only preserve and store final deliverables in a consistent format, but also gather all project members together to hold a retrospective to capture lessons learned and areas for improvement to be preserved for future reference. This retrospective allows the project members to reflect on the significance of the project, recording the tacit knowledge that will be essential for future teams to improve upon past performances.

2. Standardize Workflow

Next, think about the workflow of the creation and preservation of content during these high value moments and begin their standardization. For each piece of knowledge created during the high value moment, you should be able to clearly answer the following items: what the knowledge is, where this knowledge is stored (i.e. in what system), who is responsible for capturing it, when it should be captured, and why it is important to do so. 

To standardize the process, there are many tools and techniques to consider. Content types present a strong benefit, in both templating a key piece of knowledge, but also applying consistent metadata so that the content can be surfaced in search and located via browsing. When you map out exactly what the pieces of knowledge associated with the moment should look like, as well as what metadata should be applied, this content will be uniformly preserved any time the high value moment occurs. Additionally, you may consider developing a tool as simple as a checklist. In this way, following the high value moment, staff members will know exactly what they need to capture and how. 

Since high value moments often mirror decision-making moments and the time of need for other staff who will be searching for or creating similar content in the future, it is thus important that staff actively work to capture and preserve their content and knowledge in a standardized fashion accessible to others. When staff get into the habit of capturing their content in a consistent way at the time it is created, it will become more of a given that content will then be available to them at their time of need.

3. Set Expectations

Within this process, it is essential that expectations are clear regarding who is responsible for each step of the process. Someone must own each step, whether it is the individual most involved in the moment, or another specified individual, such as in the business development team. These tasks must be built into the responsibilities involved in the project or high value moment. Furthermore, the expectations should be included in job roles and discussed in performance reviews to ensure their effective completion. 

4. Embed Processes

Overall, it is essential that the standardization of the content capture process is embedded into existing processes at your company. Content authors do not want to feel like they need to go through countless steps in order to complete one task. Consider ways to integrate workflows for writing, saving, and updating content in existing systems and tools.

For example, at EK, we recognized the need for a comprehensive team space in which to write and develop our thought leadership. Upon the creation of this team space, each team member can now work in an environment where the technology enables our collaboration, and there is no need to create final copies of the content in a separate location. The technology and content types we created enable a seamless workflow.

When this process becomes a clear, integral part of an organization or team’s content workflows, accessible knowledge throughout the organization is no longer reliant on knowledge contributors sharing their knowledge when they have time or when it is convenient to them. Identifying these high value moments and encouraging content capture at the very moment it is created both increases the knowledge available to all and furthers collective ownership of KM practices.

How Do We Evaluate the Successful Completion of the Process?

After creating and implementing a standardized workflow for capturing the content, you should evaluate the quality of your content capture process and the content that has been captured to date. How can you ensure that you are, in fact, successfully capturing key content? At its core, following a high value moment, the validation consists of evaluating whether all the key content has been successfully preserved in the standardized format you previously defined. Is the content located in the central location that was specified? Are all of the correct metadata tags applied, such as client name, subject area, and project type? Are the individuals responsible for the content creation, upload, and updates performing their roles? The point of this exercise is not to get the whole process perfect each time. Start identifying patterns around what is working and what is not working, and analyze if there are specific teams or groups for which this process is working more effectively than others. In this way, you can begin identifying opportunities for growth and adaptation to your staff’s needs. 

Closing

High value moments of content capture represent important opportunities for your company to effectively and consistently preserve the content you need for success. Without a consistent method and plan for capturing this essential content, there is no guarantee staff will be able to access information at their future times of need.

Do you need help identifying your company’s high value moments and better capturing content at these points in your business cycles? EK is here to help. Please contact us to learn more. 

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The rate at which we are producing new information is unprecedented. Each year our ability to capture and create information becomes easier. As a result, the need to find information becomes more and more important. Designing the structure of information environments (intranets, websites, digital libraries, etc.) in a way that promotes the findability and discoverability of content is critical. When users cannot find the information that they need and are unable to locate information they didn’t previously know existed, the information created is wasted, innovation is stifled, and duplication of effort is highly likely. 

Taxonomies are one tool that can be leveraged to organize information environments by providing a consistent controlled vocabulary that allows content authors to label content like documents or web pages consistently. This ensures that content will be surfaced correctly in search results and that each information item will appear correctly in navigation menus. As a professional Information Architect and Taxonomist, I frequently see poorly designed taxonomies. One common taxonomy design error is the use of overly specific language that does not align to content. This means that the vast majority of content contained in the information environment cannot be easily mapped to the taxonomy and the terms contained in the taxonomy are not easily understood by end-users. One way I avoid this pitfall is by applying the Five Laws of Library Science when designing a taxonomy. The Five Laws of Library Science were proposed by S.R. Ranganathan long before the introduction of intranets and websites. Although the focus of these principles was on operating a library system, I believe that applying Ranganathan’s original principles leads to a more usable digital environment and a taxonomy that is directly aligned to content. Since its publication in 1931, the Five Laws of Library Science have made a tremendous impact on library and information professionals. In my opinion, these laws should be carefully considered when investing in your intranet or designing your next taxonomy. This blog is seeks to provide an examination of the following: 

• S.R. Ranganathan’s Original Five Laws of Library Science; 
• The application of each law in today’s digital world; and
• The business value each law provides in today’s digital world.

Books are for Use

The first law “Books are for Use” means that books should not be hidden from library patrons. Libraries serve humanity byproviding knowledge and ensuring it is accessible. The first law of library science is directly related to the design of taxonomies in digital environments. Taxonomies, when integrated with intranets, websites, digital asset management systems, or digital libraries, provide access to content. Digital content like physical books offers knowledge to end-users. When designing taxonomies, our word choice and position in the hierarchy should be carefully selected with the end-user in mind. The purpose of our taxonomy should always be to serve end-users by ensuring the knowledge and information we are describing is easily accessible. 

This law also goes beyond taxonomy design. Too often when we implement security permissions in a digital environment, we create overly restrictive permissions preventing end-users from accessing content that they need. By applying this law to our taxonomy development process and the development of access rights, we can work towards putting our content to use. 

Today, the value of this law translates to increased innovation and reduced duplication of effort. If content is not thoughtfully organized in a way that promotes use or even worse, if content is inaccessible due to overly restrictive permissions, end-users do not know that the content exists. This means that content does not serve its purpose, leading to potential duplication and loss of innovation. 

Every Reader His/Her Book

The second law of library science denotes libraries exist to distribute knowledge to a variety of different patrons. Librarians should not judge the patron’s reading material and seek to serve all. This is as relevant today as it was in 1931. When creating a taxonomy, we should not design taxonomies to suit the needs of a select group of individuals. Every user has content that they need to access. The taxonomy should always seek to serve each user group. By including each user group in initial research and validation, we can break down information silos and promote the inclusion of a diverse array of content. Adherence to this law will improve morale and contribute to a productive company culture through information access providing real business value. 

Every Book His/Her Reader

The third law Ranganathan proposed is “Every Book His/Her Reader. This means that each book in the library is useful, regardless of specificity or how small the groups seeking the book may be. In the digital age, our purpose as Taxonomy Designers and Information Architects is to make information accessible. The third law is directly applicable to designing the structure of an information environment. Clients often ask if specific content items deserve taxonomy terms to describe them. Following the Third Law of Library Science, as long as the information item is not being deprecated, the taxonomy should describe each content item. Following this law ensures that each information item is labeled and categorized. This offers a positive user experience for all end-users and ensures that content can be easily surfaced no matter how specific in the event of a legal event or crisis. 

Save the Time of the Reader

The fourth law of library science, states that each person should be able to locate the material they desire quickly and with ease. In the digital age, we should carefully analyze the type of knowledge organization system that we believe will allow end-users to find content the fastest. Although each knowledge organization system has its pros and cons, in many cases we find that a faceted taxonomy is the preferred knowledge organization system. Structurally, faceted taxonomies allow individuals to quickly find content items by simultaneously searching and browsing. We also need to incorporate language in the taxonomy that resonates with the group who will ultimately be seeking the information we are describing. Combining relevant language and a knowledge organization system that promotes ease of access, ensures that we are saving time for the information seeker.

Today, the cost of not quickly finding information is high. The ability to quickly locate information leads to user satisfaction and comfort. This means that when users can find what they need quickly they are more likely to use your platform. In a corporate environment, there is a real tangible cost to not finding information. Here is a simple calculation you can use to calculate the cost of not quickly finding information: 

The seconds, minutes, and hours spent looking for information have a clear cost beyond users abandoning the platform in corporate environments. 

The Library is a Growing Organism

The final law of library science proposed by Ranganathan states that libraries continually change. In the digital world, no taxonomy effort is ever complete. As user interests and domains of knowledge evolve, so does the need to change the underlying taxonomy. If you are investing in the development of a taxonomy or digital product, you need to accept this and develop frameworks that support the evolution of our information environment. Once the structure of a taxonomy or information environment is completed, following this law is critical. In order to ensure our digital product stays relevant and continues to offer value, we must develop a clear governance framework that allows for the evolution of the taxonomy over time. 

Conclusion

The Five Laws of Library Science are as relevant today as they were in 1931. When these laws are applied to a digital environment, we can ensure that the information contained in the environment is easily accessible, audience-focused, and forward-thinking. Although following each of these laws is recommended, at the end of the day, end-user needs are the primary driver of a good taxonomy design. In the face of a clear business case, some of these laws may not apply. If you need help with the design and implementation of a taxonomy that follows these principles, connect with one of our knowledge management experts by contacting Enterprise Knowledge.

The post Applying The Five Laws of Library Science to your Next Taxonomy or Digital Product appeared first on Enterprise Knowledge.

This infographic is a visual introduction to how facets can improve item, document, and content findability, regardless of the form and structure of that content. Other factors, like customized action-oriented results and an enterprise-wide taxonomy, allow for an even more advanced search experience. EK has experience in designing and implementing solutions that optimize the way you use your knowledge, data, and information, and can produce actionable and personalized recommendations for you. If this is something you’d like to speak with the experts at EK about, reach out to info@enterprise-knowledge.com.

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If you are exploring ways your own enterprise can benefit from implementing AI capabilities, we can help! EK has deep experience in designing and implementing solutions that optimizes the way you use your knowledge, data, and information, and can produce actionable and personalized recommendations for you. Please feel free to contact us for more information.

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