When designing an enterprise taxonomy, the number of possible metadata fields that can be used to describe content can be overwhelming. Depending on the type of content, the size of the organization, and the breadth of that organization’s functions, the overhead involved in applying a large number of metadata fields to describe content can quickly outweigh the value of tagging. 

While having multiple metadata fields is reasonable for enterprises that possess a large breadth of varied content, it is important for any organization to identify and place weight on the primary fields. In other words, primary fields are the metadata that can, and should, be applied to all enterprise content. Examples of this might be Topic, Content or Document Type, and Function. A good rule of thumb is to have no more than 5 primary metadata fields that a user is required to enter manually for any given piece of content. This helps content authors to not feel weighed down by overabundant and/or unnecessary metadata fields.

It can be hard to identify whether a metadata field is primary, secondary, or tertiary, and there is truly no single, hard rule. One way to begin determining whether a metadata field might be primary, secondary, or tertiary is to think of the metadata field in terms of scope. Ask yourself the following questions:                                                                                      

These questions may help you to identify, at a high level, which metadata fields might be primary, secondary, or tertiary. However, don’t forget to consider other important factors such as: business use cases, content lifecycle management, whether the fields are consumed by downstream systems, etc. To confirm, or if you are still questioning the field’s tier, here are EK’s basic definitions of primary, secondary, and tertiary fields in terms of metadata:

Primary Metadata Field – A field that can apply to all content across all systems.
Secondary Metadata Field – A field that can apply to a subset of content across all systems.
Tertiary Metadata Field – A system or function-specific field.

Let’s think about examples for each of these types of metadata fields. The table below compares the three tiers of metadata fields and how we can define each:

Once you’ve identified and defined the primary fields for your organization’s content, secondary and tertiary fields can be used to supplement where needed. However, as we mentioned earlier, an overabundance of metadata can overburden users and be as ineffective as too little metadata. In most businesses, finding time for content creation is already difficult. Asking content authors to enter more than 5 or so fields will add additional time to the content creation and tagging process, increasing the likelihood that content will not be tagged correctly or at all. Also, too many metadata fields can narrow results too drastically, actually serving to make it harder for users to find content. 

If you still have questions or want some help identifying the optimal primary metadata fields for your content, contact us at info@enterprise-knowledge.com. We’ll apply these best practices and our years of experience to streamline the process for your benefit.

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• Findability – The most common use case for taxonomy is, as we call it findability. In short, making it fast, simple, and intuitive for an end user to find what they’re looking for, either through search, browse, or any combination thereof. Taxonomy plays a number of roles here, from driving site navigation/information architecture, to improving search weighting, to enabling filtering/faceting on search.
• Discoverability – Going beyond findability, discoverability is about making end users aware of information they weren’t necessarily seeking, thereby providing them more complete answers. This is often surfaced via push recommendations. The idea here is that, with consistent taxonomy applied as metadata on content, tools can recommend content with similar metadata, helping users to find more than they were initially seeking.

Both findability and discoverability translate to more information getting to the user, ideally faster and more completely. This means less time looking for information and more time acting on a complete set of information. Moreover, improved findability and especially discoverability translates to a greater awareness of the information that already exists within the enterprise, meaning users are less likely to waste time recreating information that already existed within the enterprise but of which they were unaware. An additional element of this is:

• Awareness and Alignment – When we’re consistently tagging not just our content, but also our people with a well-designed taxonomy, we’re creating a great view of the organization as a whole. This means users are more likely to discover content elsewhere in the organization similar to that which they’re working upon, as well as people within the organization that hold similar or sought after expertise. 

Improved awareness and alignment means that users within an organization are more likely to connect with other end users that can help them learn, complete their tasks, or develop new knowledge. This translates to improved collaboration and coordination, with traditional silos of knowledge breaking down and new enterprise communities of knowledge and learning developing. 

Over time, improved awareness and alignment results in greater upscaling of employees as they find and leverage people from whom they can learn more effectively, as well as improved innovation within the organization as more experts collaborate across geographic and organizational boundaries. This leads to:

• Standardization – Enterprise taxonomy can align disparate systems, people, and processes, helping the organization to better communicate, collaborate, and integrate.

Standardization can result in lower administrative burden and greater integration of different information stores and organizational groups. Different systems that leverage the same taxonomies can be more effectively integrated in search. In addition, a great value add to effective enterprise taxonomy is that these controlled vocabularies begin seeping into conversations and day to day language, meaning that the overall way that people describe what their needs are and what they’re doing becomes more consistent, again, enabling greater collaboration and clearer communication.

As an organization begins mastering their overall information management with taxonomy, a common outcome is:

• Understanding – As taxonomy is consistently applied to content as tags, an organization has a better understanding of their content. A well-designed taxonomy applied consistently to content will ensure an organization understands what their content is about, who its for, and ideally, how it is being used.

Greater understanding of an organization’s content means that the organization can be more strategic about the content they’re creating and maintaining. An organization that understand what their content is about and how it is being used can identify gaps in their own knowledge and proactively work to address those gaps. Moreover, understanding content can help an organization decide what is no longer of value and should be archived or dispositioned. This, in turn, reduces organizational overhead from maintaining content that shouldn’t be kept, and decreases organizational risk from keeping content that is old and outdated.

Though all of these taxonomy value propositions have held true over the decades, the most common conversation today is about:

• Artificial Intelligence Readiness – A well-designed enterprise taxonomy serves as a critical building block for an organization to design ontologies, a key element of Knowledge AI.

Organizations that are investing in taxonomy now will possess a distinct advantage in designing and establishing enterprise ontologies, opening the path to Knowledge AI and creating greater avenues to integrate their content, data, people, and everything else that matters to their business.

Still struggling to get started with taxonomies, unable to convince your leadership of their value, or ready to take the next steps in maturity to ontologies and AI? Give us a call and let’s get started.

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You can register for the event by visiting the KMA meetup site.

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Presentation Overview

Analytics is a critical tool that allows business owners to make fact-based decisions about taxonomies. Taxonomy management involves capturing terms and concepts, analyzing their usefulness, and managing the employment of the concepts and terms within different contexts. Pitts and Little present best practices on design and maintenance of taxonomies, as well as discuss the role of the governance plan. The presentation focuses its discussions around the broad areas of design methodology. Pitts and Little share sustainable methods for maintaining taxonomies and integrating changes into their systems design processes.

What You Will Learn:

• Best practices for taxonomy management

• How to use analytics to drive continuous improvement of the taxonomy

• How to make taxonomy governance plans work in an agile environment.

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EK assists government program and IT leaders in the strategy, design, and implementation of Knowledge and Information Management systems, leveraging Agile approaches to facilitate all aspects of our work. Staffed by a diverse group of senior consultants with decades of experience across the public and private sectors, EK’s mission is to form true partnerships with our clients, listening and collaborating to create tailored, practical, and results-oriented solutions that enable them to thrive and adapt to changing needs.  EK is already a longstanding and trusted partner to federal agencies and Fortune 500 companies.

Since being founded in 2013, EK has applied its expertise in strategy, design, and development of Knowledge and Information Management systems, with proven approaches for Taxonomy Design, Brand and Content Strategy, Change Management and Communication, and Agile Transformation and Facilitation for a range of federal agencies, nonprofits, and commercial organizations.  

The GSA contract allows Federal customers to easily and efficiently purchase the wide range of Agile Knowledge and Information Management IT services offered by EK. The GSA establishes long-term, government-wide contracts with vendors to streamline and standardize the process of procuring products and services for the entire Federal government. EK’s inclusion as a GSA Schedule 70 vendor enables all Federal government agencies to obtain Agile support, Knowledge Management consulting, and Information Technology services at approved pricing and with license terms from a trusted vetted consultancy.

“EK is pleased to make our Agile, Knowledge Management, and Information Management services available to government agencies through the GSA Schedule,” said Zach Wahl, President and CEO of EK. “This provides another avenue by which we will be able to support the Federal government with our unmatched experience and capabilities.”

In awarding EK a GSA Schedule 70 contract, the Federal government has determined that EK is recognized as a preferred vendor that is fully authorized to conduct business directly with Federal government agencies.

Federal, State, and local agencies can obtain information about EK GSA Schedule 70 contract services on the GSA Advantage web site at: www.gsaadvantage.gov (Contract Number: GS-35F-486DA) or by contacting EK directly at 571-403-1109.

About Enterprise Knowledge

Enterprise Knowledge (EK) is a services firm that integrates Knowledge Management, Information Management, Information Technology, and Agile Approaches to deliver comprehensive solutions.  Our mission is to form true partnerships with our clients, listening and collaborating to create tailored, practical, and results-oriented solutions that enable them to thrive and adapt to changing needs.

Our core services include strategy, design, and development of Knowledge and Information Management systems, with proven approaches for Taxonomy Design, Project Strategy and Road Mapping, Brand and Content Strategy, Change Management and Communication, and Agile Transformation and Facilitation.  At the heart of these services, we always focus on working alongside our clients to understand their needs, ensuring we can provide practical and achievable solutions on an iterative, ongoing basis.

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Whether you’ve dedicated every waking moment to watching the Rio Olympics or simply kept up with the latest highlights on your phone, you’ve no doubt heard about how Michael Phelps won his 22nd medal, and broke a 2,000 year old record in the process. But Michael Phelps is just one of the athletes participating in this year’s Olympics. This year, there are 6,225 male and 5,137 female athletes participating in the games, representing 205 countries. Those athletes are competing in 306 events, for 42 sport disciplines. Hearing all of these numbers, you might wonder: How can anyone keep track of that much information? As a result of effective business taxonomy design, the official website of the Rio 2016 Olympics is able to track all the games and provide timely information mainly categorized by Country, Athlete, and Sport.

Business taxonomies are designed for the purposes of content categorization and findability. They must be simple, consistent, and intuitive for end-users, and preferably have a flat structure. The taxonomy in the Rio 2016 Olympics website meets these criteria.

From the homepage, the site allows you to browse and discover content by Sport, Athlete, Country, Venue, and Date. From the business taxonomy perspective, these five facets are called “enterprise metadata” or “core metadata”, which means that virtually every piece of content in the site can be tagged with them. In the case of the Olympics website, every game or event could be tagged with these five facets.

For instance, going back to the Men’s 200 Individual Medley Final that took place on August 11th when Michael Phelps won his 22nd Olympic medal, you may notice that this event was tagged with the following metadata:

• Sport: Swimming
• Athletes: Phelps Michael, Hagino Kosuk, Wang Shun, etc.
• Countries: USA, Japan, China, etc.
• Venue: Olympic Aquatics Stadium
• Date: August 11, 2016

These tags allow people to browse the site and find the Men’s 200 Individual Medley Final event from different perspectives. This particular event took place on August 11th, so I chose the Schedule and Results option and then selected the date to filter only the events that took place on that day. Then, I was able to refine the results by Sport (swimming). I quickly found all the details about the Men’s 200 Individual Medley Final event, including the venue where it took place, which athletes participated and from which countries.

After looking at the information for this swimming event, I wanted to learn more about the gold medals that have been awarded so far at Rio 2016. I clicked on the Medal Count option from the left navigation and found that I was able to browse the number of medals by three metadata fields (Countries, Athletes, and Sports), which are again part of the core metadata that we identified earlier.

When I thought about learning more about Michael Phelps’ performance throughout this year’s Olympics, I clicked on the Athletes option from the left navigation and realized that I could search by two facets (Country and Sport), again, two fields that are part of the core metadata. As you can see, the well-designed taxonomy helped me to quickly find the information I was looking for.

Besides the core metadata, in business taxonomy there is also the concept of “secondary metadata”, which means that additional facets could be applied to certain pieces of content to provide more granularity. For example, an athlete could be tagged with Age, Gender, and even with Type of Record (World Record or Olympic Record), if applicable. These three fields are called secondary metadata because they provide an additional level of detail about the athletes. In short, the core and secondary metadata used in the Rio 2016 Olympics website has been applied not only for classification purposes, but for also for intuitive browsing and discovery of content. By applying metadata and secondary metadata to every piece of content, the creators of this website have provided a lot of flexibility and a wide range of options for visitors to search and find what they need.

Although the Rio Olympics website is just one example of business taxonomy design, as you continue with your work day, think about how an intuitive business taxonomy could enhance search and findability at your organization. At Enterprise Knowledge, we work with organizations from many different industries to identify their information management needs and build customized business taxonomies. These taxonomies are generally used to tag content within an organization’s intranets, public websites, document repositories, and other information systems. But because every company is different and has unique needs, the business taxonomies we build are designed to meet each client’s specific requirements.

Contact Enterprise Knowledge to help you design a business taxonomy that’s worthy of a gold medal.

In the meantime, I’ll sit back and continue to watch and enjoy the Olympics…  

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Step #4: Measure the Findability of Your Content

Search, which is central to enterprise-wide knowledge transfer, is simple in theory; a user enters a set of key terms in a search engine and the search engine retrieves records that match the key terms. In reality, the architecture of a search engine is nothing more than:

• Search Outputs: Search results the user sees after requesting information
• Query Management:  The way a system formats and matches requests for information
• Search Inputs: Content a system looks through to find the requested information

Yet while it’s true that search is a simple and well-understood progression, creating an effective enterprise-wide search is a multifaceted process that requires in-depth analysis. In my previous blogs, I’ve talked about some of those necessary steps, such as designing a user-centric taxonomy, making sure that facets are consistent, as well as testing the taxonomy. In my final blog of this taxonomy series, I’ll be discussing the metrics that can be used to measure the effectiveness of a taxonomy.

The addition of a taxonomy supports accurate search and guided navigation, which cannot be achieved with a search engine alone. However, implementing a taxonomy requires consistent maintenance and refinement of the search data in order to ensure its effectiveness. Objectively scrutinizing enterprise-wide search data allows us to tune search and update the underlying taxonomy to create a positive search experience. When examining search data, there are a number of metrics that can help us measure the effectiveness of a taxonomy:

These analytics can be used to better identify the most effective search terms and determine if they are reflected in a taxonomy. To capture this information, we can simply ask users to search for specific content and analyze their search habits, making note of search depth, bounce rate, time after search, and any search refinements that occur. There are also a number of tools that can be used to help calculate these analytics. Google Analytics and PIWIK are two popular choices that can be used for both intranets and public facing websites.

Once you implement these metrics, the taxonomy should be updated periodically to reflect the search trends, which will result in a more efficient and accurate search system. The key to properly updating a taxonomy with effective search inputs is to understand user intent. This is no easy task, it involves a deep understanding of the keywords used by employees when searching. Therefore, in addition to the metrics provided above, it is also important to look at:

• Where users searched and what they did next: Did the user reach the desired page or attempt another search? Are users generally visiting the same pages when searching the same key words?
• Measuring search quality: By looking at search refinements, search depth, bounce rate, and time after search while paying close attention to the search terms used to reach the page, we can get a good idea of the quality of the search.
• For example, if a high number of users searched the intranet using a specific keyword and 80% of users left the page the keyword led to immediately, correcting the underlying taxonomy is necessary.

In short, understanding the intent of the user allows us to get into the minds of the searcher. Therefore, a better understanding of the search terms used by users allows us to create a better taxonomy and a better search experience.

Although the concept of search is a relatively simple idea, there are many considerations that go into the implementation process. Despite the challenges that come with developing an effective enterprise search, the benefits to doing so are clear. Sue Feldman of IDC found that knowledge workers spend from 15% to 35% of their time searching for information and 40% of corporate users reported that they couldn’t find the information they need to do their jobs on their intranets. The subsequent costs are significant; in another recent survey conducted by IDC, the time spent searching for information averages 8.8 hours per week, at a cost of $14,209 per knowledge worker per year. In short, the time and costs associated with unstructured information are too significant to ignore.

We hope that through this blog series, you have a better understanding of how much thought and consideration goes into designing and evaluating a quality taxonomy and enterprise search. To learn more about how you can improve the findability of your content, connect with one of our knowledge management experts by contacting Enterprise Knowledge.

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Step #3: Validate Your Taxonomy

Previously, I’ve talked about how to design a user-centric taxonomy, as well as how to ensure that your facets are consistent. Today’s blog will address the next key task: testing the taxonomy. In order to continue to provide you all with practical and applicable advice, I’ll address some of the most common and efficient forms of testing that can be executed on a taxonomy prior to deployment.

Taxonomy Testing

Faceted search is one of the most significant search innovations to date.  It allows users to combine searching and browsing with a simple keyword search. We must treat faceted search the same as any other component in an information environment. This means testing the underlying taxonomy the same way other elements of an information architecture are tested. These are the the most common and efficient forms of testing that can be executed on a taxonomy prior to deployment.

Card Sorting

Card sorting is the most common form of user experience testing for taxonomy. It’s a technique where users group related terms using index cards or online programs, such as Optimal Workshop or Usability Tools. There are two forms of card sorting, open and closed.

Open Card Storing

Open card sorts require participants to group potential taxonomy values together and assign a broader category of their own. For example, users could group index cards labeled 401(k), Healthcare, Holidays, etc. under an index card labeled Benefits. Open card sorting is done earlier in the taxonomy design process, when taxonomic categories are not clear. This test helps uncover how users believe the taxonomy should be classified.  

Closed Card Sorting

In closed card sorts the categories have already been designated, allowing only prelabeled cards and categories. Closed card sorts are used to validate the taxonomic structure already developed. In closed card sorting tests, users are presented with a series of prearranged categories. Users then assign taxonomy values to each category using index cards or an online program. When the card sorting exercise is finished you will likely notice that the participants chose slightly different categorization schemes.  This is typical and should be formally documented. The most effective way to document participant responses is through a standardization grid:

A standardization grid captures the number of participants that chose a specific categorization scheme.  This allows the taxonomist to choose the most appropriate classification scheme within the taxonomy.

Tree Testing

Tree testing, or reverse card sorting, is used after the open card sort is validated through closed card sorting. The hierarchy developed through closed card sorting is presented to the user and the user is asked to complete a series of tasks. Depending on how the taxonomy will be used, the tasks will vary.  Example tasks could include:

• Where in the taxonomy would you find a specific document?
• What value would you use to tag a specific document?

These tasks are written on index cards, and users are asked to place the task cards in the taxonomy. A standardization grid is used to collect the number of participant that chose:

Test Tagging

Once the structure of the taxonomy is in place, one final test is necessary. The taxonomy needs to be tested by tagging actual documents or other information products. It is important that the test administrator encourages users to talk through the searching process and note any problems that occur. Ideally this test involves having users tag and search for content within a content management system.    If it is not possible to perform this test in a content management system, a taxonomy testing tool can be developed in a spreadsheet:

Users can tag content using drop down menus populated by taxonomy values. This method does not allow users to search for content but does allow us to see if the taxonomy is exhaustive and flexible enough to support a large number of content from across the organization. As with any variation of user testing, the test administrator needs to note any gaps, ambiguity, or other issues found when using the testing tool.  

Now you are well on your way to designing and validating your taxonomy. The question still remains, has the taxonomy you’ve created improved the findability of your information? In the final blog of this series, I’ll share some critical metrics for determining whether your taxonomy is delivering the results you expect.

Need help facilitating these testing methods? Contact Enterprise Knowledge.

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