The post Webinar: Building a Connected Search Experience: Bringing KM and AI Together to Fuel Findability appeared first on Enterprise Knowledge.

Many have heard me utter one of my favorite phrases, that “happy employees are good at KM, and good KM makes happy employees.” I’ve always held EK up as the ultimate example of this pithy phrase, and it now seems that I can expand it to also note that good KM enables productive and natural remote work.  

With EK’s definition of what KM is and what value it offers, the linkages of how KM powers remote work become immediately apparent:

KM Enables Findability – With remote work, your employees no longer have the ability to walk down the hall in order to ask someone for the information or guidance they need to do their job.

I often reference the human search engines of an organization; those people that seem to know where all the right answers are hidden; but what if those people aren’t available? With remote work, employees need to be empowered to act more independently, but nevertheless act on the correct information. Good KM ensures they can find that actionable and accurate information. This begins with the foundational elements of KM, namely taxonomies, content cleanup and governance, and content types. These elements will power findability and discoverability in your organization, ensuring the right employees are finding the right information that they need in order to do their jobs. This will maximize productivity and minimize risk for your organization regardless of whether you’re remote working or not; but at its core, this is particularly necessary for organizations that are suddenly finding themselves with employees in makeshift home offices (i.e. their couch), rather than an office full of experienced and tenured employees to serve as their human search engines.

KM Enables Connections – With remote work, your employees no longer have the ability to meet at the water cooler and discover a like interest or form a learning/coaching relationship.

Whether your organization is local or global, face-to-face interactions remain a critical component of how people connect, how they find mentors/mentees, and how they learn from one another. When we lose the opportunity to discover each other in the same physical space, how do we ensure the right connections still happen? One key KM answer to this is to ensure that all your organization’s knowledge objects (content, data, people, etc.) are all tagged consistently in order that your employees can discover connections by traversing from one type of knowledge to another. For instance, perhaps one of your employees goes searching for an answer to a question about Enterprise Artificial Intelligence (AI). With consistent tagging and enterprise search, we want them to find the simple, short definition of Enterprise AI, and then not only discover white papers and blogs on the topic, but end up connecting that content to an expert on the topic (or a group of experts) from whom they can learn and with whom they can engage. In this case, the technological side of KM plays a potentially major role by creating digital communities of practice and expert finder tools and then harnessing advanced search to allow your employees to find the right materials. One of my favorite clients tells the story of two unique experts, both on the same obscure sub-topic of their field, who never were aware of each other for over 20+ years at the organization until we created a digital community of practice where they discovered each other. One of them quoted, “I thought I was alone for so long, and now I have someone I can learn from.” Now, the two of them are building that community of practice to mentor the next generation of experts. Like I said, good KM makes happy employees, and happy employees make good KM.

KM Enables Collaboration – With remote work, your employees can’t duck into a conference room and whiteboard ideas for a solution together.

I have to admit, I was a slow adopter to the concept of remote work. As a CEO, I was worried about my colleague’s ability to work together and learn from each other when they weren’t physically together. I am confident, however, that the right KM tools and processes vastly improve the effectiveness of collaboration in remote work situations.

In order to make the vast field of KM more digestible, we at EK divide it into five categories: People, Process, Content, Culture, and Technology. An effective KM strategy enables remote collaboration by:

1. Identifying the people who hold the knowledge, and the people who need the knowledge, making the appropriate connections so the right people are finding each other and working together;
2. Creating processes by which knowledge can move through the organization and ensuring that, as people are collaborating, there are the appropriate guides in place to guarantee organizational policies are followed and the right knowledge is graduating as corporate information. This is particularly important for remote work situations, where managers may have less ability to stay on top of day-to-day work efforts;
3. Making the right content available as starting points, templates, and guides so that the starting points of collaborative exercises are fruitful from the beginning; 
4. Creating a corporate culture of knowledge sharing that encourages collaboration and support for one another, regardless of whether employees are in the same room or on the other side of the globe, resulting in willingness and rewards for employees lending their time to support one another; and
5. Constructing the appropriate technologies to allow people to collaborate remotely in ways that feel natural to them. For many organizations, the default answer here is SharePoint or Google Docs, and with the right governance and design, both of those are fine options, but they are just two of many options in what might be an organization’s KM Suite of Technologies. Over the last week, for instance, we’ve been keeping in touch and sharing pictures of our home offices on Slack, collaborating on deliverables in Google Docs, and holding live meetings in Zoom. Any piece of technology is a KM tool if harnessed properly.

KM Enables Culture – With remote work, your employees can’t witness the behaviour of corporate leaders or follow the cues of more tenured employees.

With a focus on corporate culture, I’ll end where I started: happy employees are good at KM, and good KM makes happy employees. I believe one of the reasons we’ve so successfully transitioned to remote work at this time is that we’d already laid the foundation of corporate kindness and collaboration, we’ve already demonstrated that a culture of knowledge sharing and collegial support will be rewarded. As a result, our employees have found ways to stay in touch, for instance today a group suggested we institute a 15-minute open conference call just so everyone could check in. We, as leaders, have also played our part, leveraging our online collaborative platforms to get everyone talking, announcing new wins that we’d normally do in person via fun videos, and scheduling a virtual “happy hour” tomorrow for the whole team to get on video and catch up on the week. For any organization, the same can be made true, regardless of whether you’ve laid that foundation or not. With the right KM strategy, organizations can create the right expectations and rewards for their employees to stay engaged with the organization, and more importantly, with each other during periods of remote work. This, in turn, will result in higher productivity, employee satisfaction, and retention. 

Are you playing catchup with KM at this point? Do you need help to get started in the face of the current remote work reality? Contact us, and we’ll set up a (virtual) meeting.

The post The Value of Knowledge Management for Remote Work appeared first on Enterprise Knowledge.

The post KM Showcase 2020: Leveraging KM as the Foundation for AI appeared first on Enterprise Knowledge.

The conference has three primary tracks: 

• Case studies; 
• Methodologies and best practices; and, 
• Technologies. 

The case studies will provide real-life stories about how organizations around the world have implemented semantic solutions. The speakers will share what worked and what they would have done differently so that attendees can learn from their experiences. Organizations like NASA Jet Propulsion Laboratory (JPL) and the German Aerospace Center will talk about how knowledge graphs help send people to the moon. Presenters from Wells Fargo and Intuit will explain how they use knowledge graphs to make data more accessible and accurate. Organizations like eBay, Healthstream, and the Inter-American Development Bank will provide lessons learned as they have developed recommendation engines using semantic technologies.

We also have a great group of speakers who will share their methodologies and best practices. Experienced taxonomists like Heather Hedden and Gary Carlson will give guidance on the best ways to develop taxonomies, ontologies, and chatbots. Dean Algegang and Lulit Tesfaye will share their best practices for publishing linked data to the web and developing a semantic data strategy, respectively. Finally, Melissa Orozko intends to provide guidance on how to create a global ontology; while Bob Kasenchak will talk about transforming taxonomies to knowledge graphs.

Semantics technologies are changing rapidly. We have a number of speakers that will keep you up to date on the latest technologies and trends in the industry. Kurt Cagle, from Forbes, and Andreas Blumauer will speak about knowledge graphs for human beings and how to “cook” a knowledge graph. Brian Platz will share guidance on how to integrate time into semantic solutions. There will also be talks on multilingual tagging approaches and identifying digital twins.

This is just a shortlist of the kinds of companies, speakers, and topics that will be at the first US SEMANTiCs conference in Austin, TX from April 21-23. I hope to see you there!

The post Speakers, Companies, and Topics You Shouldn’t Miss at SEMANTiCS 2020 appeared first on Enterprise Knowledge.

Confused about these new technologies and why companies like Google, Amazon, and Microsoft rely on them? Join a panel of experts, including Alan Morrison (Sr. Research Fellow, Emerging Tech, PwC), Aaron Bradley (Knowledge Graph Strategist, Electronic Arts), Kurt Cagle (Contributing writer to Forbes Magazine), Lulit Tesfaye (EK Data and Information Management Practice Lead), and Yanko Ivanov (EK Technology Partner Manager) as they answer questions about how AI, Knowledge Graphs, and Semantic tools work together to solve complicated business problems. Additionally, the webinar will provide insight into the key topics and questions thought leaders will explore at this year’s SEMANTiCS conference in Austin, TX.

The webinar will be held on Thursday, January 30th from 1:00 – 2:00 PM EST. 

View event details and register here.

The post Upcoming Webinar: Knowledge Graphs, AI and Semantics: How do these technologies fit together? appeared first on Enterprise Knowledge.

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.

The post What is the Roadmap to Enterprise AI? appeared first on Enterprise Knowledge.

Deconstructing Artificial Intelligence: What are Enterprise AI Applications?

Enterprise AI entails leveraging advanced machine and cognitive capabilities to discover and deliver organizational knowledge, data, and information in a way that closely aligns with how humans look for and process information.

In order to succeed with AI, organizations will first need to identify which of their current enterprise information and data management challenges are a good fit for an AI solution, keeping in mind that AI is not a magic bullet that can solve all business problems. After selecting appropriate use cases, organizations must then build the foundational competencies to structure their information in a manner that is machine-readable. From our experience, the best suited enterprise use cases for advanced capabilities such as artificial intelligence and machine learning include:  

• Semantic Search & Natural Language Processing (NLP): Semantic search seeks to understand the meaning and context behind searched terms as opposed to just executing queries against keywords. It takes into consideration language, word variation, synonymous terms, location, and user preferences to simplify user experience by describing information closer to how the user would to another person. For the enterprise, this is made possible through semantic technologies and enterprise knowledge graphs that provide the architecture to discover and surface knowledge across disparate data sources with the flexibility to quickly modify and improve data flows. This further makes it easier to sustainably add new data sources (without making extensive changes) and support future business questions that are currently unknown. Successful organizations leverage semantic search to develop human centered applications using simple natural language (think applications such as chatbots and question answering systems).
• Scaled Data Governance through automated organization: Auto-tagging and classification automatically route and organize content and data to the right channel(s) to enable findability, discoverability, optimize enterprise information and data/content governance. The most successful data categorization solutions put in place consistent follow-up processes to manage and access data in the right place, removing the manual burden (usually error prone) from humans, and enabling the enterprise to consistently organize data based on predefined access and security requirements for reliable risk management and compliance purposes.
• Augmented categorization and classification of data: Augmented categorization leverages machine logic to organize data based on similarities between content, context, and/or users, and further enables the automatic assignment of non-topical concepts to documents such as sentiment (e.g. positive, negative). Once the enterprise determines the relevant categories and relationship model (think taxonomies and ontologies) that will then be used for this process for the machine to learn to define the organization and management of concepts that are unlikely to be explicitly mentioned in a particular document (e.g. emails, helpdesk requests, etc.). The most relevant business problems we have seen here include enabling intelligent routing for handling support tickets, determining if an email needs a follow up response, or further recommending the relevant response. 

• Discover relationships across disparate sources through recommender systems: A recommendation system works by defining relationships between information or content to provide a better understanding of how things fit together. They also track what is relevant, add context to random data, and suggest relevant information and content based on similarity of users, similarity of content, and the relationships between users and content. Recommendation systems using knowledge graphs and machine readable logic pick up on patterns that enable users to discover new facts and knowledge that would have otherwise remain hidden. 

• Advanced Analytics: Unlike basic analytics, advanced analytics uses machine learning and large sets of quantitative data to empower organizations to efficiently mine information, discover hidden facts, and identify patterns at a large scale. With this capability, the enterprise is able to understand the business through insights from large and disparate data sources to make relevant and timely decisions, as well as forecast or predict future outcomes.

Why Does the Enterprise Need to Invest in AI?

The most common business drivers for Enterprise AI include: 

Business Pace and Agility: The need to cope with rapid change and the speed of business while successfully balancing effective change management and user experience with increased personalization, knowledge retention, sustainability, and scalability over time is becoming one of the cornerstones of competitive advantage. This, for the enterprise, requires impeccable harmonization and autonomous operation of disparate data and content and information management solutions.

Data Dynamism, Governance, and Scale: According to Forbes, 90% of the data and information we have today was created in just the past two years. The volume and dynamism of organizational data and content (structured and unstructured) is growing exponentially, and organizations’ need significant efficiency to glean meaningful insights and value out of it to make better decisions.

Aging Technology and Infrastructure: Most organizations have been built to organize and manage data and information by type, department, or business function. To add to this complexity, many enterprise leaders say that their systems don’t talk to each other. Increased digitization, coupled with the fast aging of systems, is further fueling these silos and disparate sources for technological solutions to continue providing meaningful support to business problems.  

Why Organizations Fail with AI

The meaning and value of AI in the context of enterprise solutions is continuously evolving. Perceptions of AI have ranged from a robot that will answer all of our questions to that silver bullet application that will automate processes and augment analysis capabilities to predict and make our future better. However, many businesses make the key mistake of assuming that an organization can start and succeed with AI the moment they are given the green light. 

From our experience, organizations in various industries are leveraging or experimenting with some form of AI capabilities and seeing remarkable results. However, many have yet to gain any value from their AI investments. Here are a selection of reasons why: 

• Lack of clear business applications and relevant use cases: Much akin to any large and disruptive transformation, every organization should first understand how advanced intelligence can impact their business or help solve relevant business problems. Kicking off an AI effort without a business focus leads to a lack of a strategic approach, which fuels misalignment and the lack of operational and cultural changes required to make it a success. 
• The assumption that AI is a “Single Technology” solution: AI is not a single technology solution. It is a combination of related technologies that address multi-layered advancement requirements within an organization such as analysis, automation, perception, prediction, etc. Organizations looking to “plug-and-play AI” need to reset their expectations and plan for a multi-phase design, development, and integration effort.
• AI is not fully “there” yet: Although automation has started to relieve the burden of repetitive organizational tasks such as tagging and classification/categorization etc., AI is still an emerging and evolving field.  As such, it will continue to require human validation in order to scale effectively, especially in the use cases that require a high degree of accuracy. 
• Enterprise information and data is not ready for AI: Machines need to learn a human way of thinking and how an organization operates in order to provide the right solutions. To this end, the information and knowledge we work with on a daily basis needs to be machine readable for AI technologies to do anything with it. “Garbage in, garbage out” is a common refrain among AI practitioners; without high quality, well organized and tagged data, AI applications will not deliver effective results. 

What are the Steps to Getting Started with Enterprise AI?

In a previous blog, I shared how to organize your data by building a knowledge graph, creating the foundations necessary for a successful AI initiative. 

From our experience, the following key considerations continue to commonly deliver a scalable and adaptable AI capability for the enterprises we work with:

1. Define an overarching vision that outlines a clear meaning, use case definition, and business value of artificial intelligence for your enterprise. This step serves as the institutional footing to set end-user expectations as well as for solidifying internal capabilities to synchronize the “design and build” process.
2. Understand organizational information maturity, including assessments of current capabilities, the current state of your content or data, tools, processes, and skill sets, as well an evaluation of any existing AI efforts.
3. Develop an artificial intelligence strategy to align AI use cases across functions and departments, as well as define a delivery process that supports the organization’s long term strategy and allows for incremental delivery with regular validation of assumptions.
4. Develop a prioritized backlog to incrementally prove and deliver on Enterprise AI initiatives.
5. Plan for sustainability and governance. Create a scalable AI project prioritization and backlog creation process for future AI initiatives as well as establish data collection Standard Operating Procedures (SOPs) or data mining processes and confirm data quality and tracking policies for existing data sources.
6. Iterate and scale with each new business question and data source. 

Many large and successful initiatives we’ve led started small, with defined business goal(s), and were delivered incrementally to validate assumptions and drive enterprise alignment one use case at a time. Whatever your industry, our AI strategy approach, user-centered design approach, and in-house technical expertise can help you get started with a 1 to 2-Day Enterprise AI Foundations workshop that will help you understand artificial intelligence capabilities and their relevance to your unique business needs, as well as develop a shared vision with a strategy/roadmap to drive practical development.

The post What is Artificial Intelligence (AI) for the Enterprise? appeared first on Enterprise Knowledge.

• Limited understanding of the business application and use cases to define a clear vision and strategy.
• Not knowing where to start, in terms of selecting the most relevant and cost-effective business use case(s) as well as supportive business or functional teams to support rapid validations.
• There are multiple initiatives across the organization that are not streamlined or optimized for the enterprise.
• Enterprise data and information is disparate, redundant, and not readily available for use.
• Lack of the required skill sets and training.

Our experience at Enterprise Knowledge demonstrates that most organizations are already either developing or leveraging some form of Artificial Intelligence (AI) capabilities to enhance their knowledge, data, and information management. Commonly, these capabilities fall under existing functions or titles within the organization, such as data science or engineering, business analytics, information management, or data operations. However, given the technological advancements and the increasing values of organizational knowledge and data in our work and the marketplace today, organizational leaders that treat their information and data as an asset and invest strategically to augment and optimize the same have already started reaping the benefits and having their staff focus on more value add tasks and contributing to complex analytical work to build the business. The most pragmatic approaches for developing a tailored strategy and roadmap toward AI begin by looking at existing capabilities and foundational strengths in your data and information management practices, such as metadata, taxonomies, ontologies, and knowledge graphs, as these will serve as foundational pillars for AI. Below, I share in detail a series of steps and successful approaches that will serve as key considerations for turning your information and data into foundational assets for the future of technology.

What is AI?

At EK, we see AI in the context of leveraging machines to imitate human behaviors and deliver organizational knowledge and information in real and actionable ways that closely align with the way we look for and process knowledge, data, and information.

What is a Knowledge Graph?

An Enterprise Knowledge Graph provides a representation of an organization’s knowledge, domain, and artifacts that is understood by both humans and machines. To this end, Knowledge Graphs serve as a foundational pillar for AI, and AI provides organizations with optimized solutions and approaches to achieve overarching business objectives, either through automation or through enhanced cognitive capabilities.

Getting Started…

Step 1: Identify Your Use Cases for Knowledge Graphs and AI?

As an enterprise considers undergoing critical transformations, it becomes evident that most of their efforts are usually competing for the same resources, priorities, and funds. Identifying a solid business case for knowledge graphs and AI efforts becomes the foundational starting point to gain support and buy-in. Effective business applications and use cases are those that are driven by strategic goals, have defined business value either for a particular function or cross-functional team, and make processes or services more efficient and intelligent for the enterprise. Prioritization and selection of use cases should be driven by the foundational value proposition of the use-case for future implementations, technical and infrastructure complexity, stakeholder interest, and availability to support implementation. The most relevant use cases for implementing knowledge graphs and AI include:

• Intuitive search using natural language;
• Discovering related content and information, structured or unstructured;
• Reliable content and data governance;
• Compliance and operational risk prediction; etc.

For more information regarding the business case for AI and knowledge graphs, you can download our whitepaper that outlines the real-world business problems that we are able to tackle more efficiently by using knowledge graph data models.

Once your most relevant business question(s) or use cases have been prioritized and selected, you are now ready to move into the selection and organization of relevant data or content sources that are pertinent to provide an answer or solution to the business case.

Step 2: Inventory and Organize Relevant Data

The majority of the content that organizations work with is unstructured in the form of emails, articles, text files, presentations, etc. Taxonomy, metadata, and data catalogs allow for effective classification and categorization of both structured and unstructured information for the purposes of findability and discoverability. Specifically, developing a business taxonomy provides structure to unstructured information and ensures that an organization can effectively capture, manage, and derive meaning from large amounts of content and information.

There are a few approaches for inventorying and organizing enterprise content and data. If you are faced with the challenging task of inventorying millions of content items, consider using tools to automate the process. A great starting place we recommend here would be to conduct user or Subject Matter Expert (SME) focused design sessions, coupled with bottom-up analysis of selected content, to determine which facets of content are important to your use case. Taxonomies and metadata that are the most intuitive and close to business process and culture tend to facilitate faster and more useful terms to structure your content. Organizing your content and data in such a way gives your organization the stepping stone towards having information in machine readable format, laying the foundation for semantic models, such as ontologies, to understand and use the organizations vocabulary, and start mapping relationships to add context and meaning to disparate data.

Step 3: Map Relationships Across Your Data

Ontologies leverage taxonomies and metadata to provide the knowledge for how relationships and connections are to be made between information and data components (entities) across multiple data sources. Ontology data models further enable us to map relationships in a single location at varying levels of detail and layers. This, in turn, sets the groundwork for more intelligent and efficient AI capabilities, such as text mining and identifying context-based recommendations. These relationship models further allow for:

• Increasing reuse of “hidden” and unknown information;
• Managing content more effectively;
• Optimizing search; and
• Creating relationships between disparate and distributed information items.

Tapping the power of ontologies to define the types of relationships and connections for your data provides the template to map your knowledge into your data and the blueprint needed to create a knowledge graph.

Step 4: Conduct a Proof of Concept – Add Knowledge to your Data Using a Graph Database

Because of their structure, knowledge graphs allow us to capture related data the way the human brain processes information through the lens of people, places, processes, and things. Knowledge graphs, backed by a graph database and a linked data store, provide the platform required for storing, reasoning, inferring, and using data with structure and context. This plays a fundamental role in providing the architecture and data models that enable machine learning (ML) and other AI capabilities such as making inferences to generate new insights and to drive more efficient and intelligent data and information management solutions.

Start small. Conduct a proof of concept or a rapid prototype in a test environment based on the use cases selected/prioritized and the dataset or content source selected. This will give you the flexibility needed to iteratively validate the ontology model against real data/content, fine tune for tagging of internal & external sources to enhance your knowledge graph, deliver a working proof of concept, and continue to demonstrate the benefits while showing progress quickly. Testing a knowledge graph model and a graph database within such a confined scope will enable your organization to gain perspective on value and complexity before investing big.

This approach will position you to adjust and incrementally add more use cases to reach a larger audience across functions. As you continue to enhance and expand your knowledge across your content and data, you are layering the flexibility to add on more advanced features and intuitive solutions such as semantic search including natural language processing (NLP), chatbots, and voice assistants getting your enterprise closer to a Google and Amazon-like experience.

Ready for AI? Automate, Optimize, and Scale.

Core AI features, such as ML, NLP, predictive analytics, inference, etc., lend themselves to robust information and data management capabilities. There is a mutual relationship between having quality content/data and AI. The cleaner and more optimized that our data, is the easier it is for AI to leverage that data and, in turn, help the organization get the most value out of it. Within the context of information and data management, AI provides the organization with the most efficient and intelligent business applications and values that include:

• Semantic search that provides flexible and faster access to your data through the ability to use natural language to query massive amounts of both unstructured and structured content. Leveraging auto-tagging, categorization, and clustering capabilities further enables continuous enhancement and governance of taxonomies/ontologies and knowledge graphs.
• Discover hidden facts and relationships based on patterns and inferences that allow for large scale analysis and identification of related topics and things.
• Optimize data management and governance through machine-trained workflows, data quality checks, security, and tracking.

Organizations that approach large initiatives toward AI with small (one or two) use cases, and iteratively prototype to make adjustments, tend to deliver value incrementally and continue to garner support throughout. The components that go into achieving this organizational maturity also require sustainable efficiency and show continuous value to scale. As your organization is looking to invest in a new and robust set of tools, the most fundamental evaluation question now becomes ensuring the tool will be able to make extensive use of AI.

If you are exploring pragmatic ways to benefit from knowledge graphs and AI within your organization, we can help you bring proven experience and tested approaches to realize and embrace their values.

The post How to Build a Knowledge Graph in Four Steps: The Roadmap From Metadata to AI appeared first on Enterprise Knowledge.

In the webinar participants learned:
• Why Knowledge Management is critical to laying a foundation for AI in your organization.
• How AI can deliver value to your organization through integration with Enterprise Search.
• What you need in place to realize the benefits of AI and create a competitive advantage through improved Knowledge Management.

The post Webinar: Laying KM Foundations for Successful and Transformative AI appeared first on Enterprise Knowledge.

• 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.

The post The Value of Taxonomy: Why Taxonomy (Still) Matters appeared first on Enterprise Knowledge.