The Anatomy of AI

Here is a conceptual analogy to help illustrate how taxonomies and ontologies support AI. While inspired by the human musculoskeletal system, this analogy is not intended to represent anatomical accuracy, but rather to illustrate how taxonomies provide foundational structure and ontologies enable flexible, contextual connections of knowledge assets within AI systems.

Just like the musculoskeletal system gives structure, support, and coherence to the human body, taxonomies and ontologies provide the structural framework that organizes and contextualizes knowledge assets for AI. Here is the analogy: the spine and the bones represent the taxonomies, in other words, the hierarchical, backbone structure for categorizing and organizing concepts that describe an organization’s core knowledge assets. Similarly, the joints, ligaments, and muscles represent the ontologies that provide the flexibility to connect related concepts across assets in an organization’s knowledge domain. 

Depending on the organization’s domain or industry, certain types of knowledge assets become more relevant or strategically important. In the case of a healthcare organization, key knowledge assets may include content such as patients’ electronic health records, clinical guidelines and protocols, multidisciplinary case reviews, and research publications, as well as data such as diagnostic data and clinical trial data. Taxonomies that capture and group together key concepts, such as illnesses, symptoms, treatments, outcomes, medicines, clinical specialties can be used to tag and structure these assets. Continuing with the same scenario, an ontology in a healthcare organization can incorporate those key concepts (entities) from the taxonomy, along with their properties and relationships, to enable alignment and consistent interpretation of knowledge assets across systems. Both taxonomies and ontologies in healthcare organizations make it possible to connect, for instance, a patient’s health record with diagnostic data and previous case reviews for other patients based on the same (or similar) conditions, including illnesses, symptoms, treatments, and medicines. As a result, healthcare professionals can quickly access the information they need to make well-informed decisions about a patient’s care.

Where AI is Failing

On multiple occasions, AI has repeatedly failed to provide reliable information to employees, customers, and patients, undermining their confidence in the AI supported system and sometimes leading to serious organizational consequences. You may be familiar with the case in which a chatbot of a medical association was unintentionally giving harmful advice to people with eating disorders. Or maybe you heard in the news about the bank with a faulty AI system that misclassified thousands of transactions as fraudulent due to a programming error, resulting in significant customer dissatisfaction and harming the organization’s reputation. There was also a case in which an AI-powered translation system failed to accurately assess asylum seekers’ applications, raising serious concerns about its fairness and accuracy, and potentially affecting critical life decisions for those applicants. In each of these cases, had the corresponding AI systems effectively aggregated both unstructured and structured knowledge assets, and reliably linked them to encoded expert knowledge and relevant business context, these cases would have produced very different and positive outcomes. By leveraging taxonomies and ontologies to aggregate key knowledge assets, the result of these cases would have been much more closely aligned with intended objectives, ultimately, benefiting the end users as it was initially intended. 

How Taxonomies And Ontologies Enable Explainable AI

When knowledge assets are consistently tagged with taxonomies and related via ontologies, AI systems can trace how a decision was made. This means that end users can understand the reasoning path, supported by defined relationships. This also means that bias and hallucinations can be more easily detected by auditing the semantic structure behind the results.

As illustrated in the healthcare organization example, diagnoses can be tagged with medical industry taxonomies, while ontologies can help create relationships among symptoms, treatments, and outcomes. This can help physicians tailor treatments to individual patient needs by leveraging past patient cases and the collective expertise from other physicians. Similarly, a retail organization can enhance its customer service by implementing a chatbot that is linked to structured product taxonomies and ontologies to help deliver consistent and explainable answers about products to customers. More consistent and trustworthy customer interactions result in streamlining end user support and strengthening brand confidence.

Do We Really Need Taxonomies and Ontologies to be Successful With AI?

The examples above illustrate that explainability in AI really matters. Whether end users are patients, bank customers, or any individuals requesting specific products or services, they all want more transparent, trustworthy, and human-centered AI experiences. Taxonomies and ontologies help provide structure and connectedness to content, documents, data, expert knowledge and overall business context, so that they all are machine readable and findable by AI systems at the moment of need, ultimately creating meaningful interactions for end users.  

Conclusion

Just like bones, joints, ligaments, and muscles in the human body, taxonomies and ontologies provide the essential structure and connection that allow AI systems to stand up to testing, be reliable, and perform with clarity. At EK we have extensive experience identifying key knowledge assets as well as designing and implementing taxonomies and ontologies to successfully support AI initiatives. If you want to improve the Knowledge Intelligence (KI) of your existing or future AI applications and need help with your taxonomy and ontology efforts, don’t hesitate to get in touch with us. 

The post How Taxonomies and Ontologies Enable Explainable AI appeared first on Enterprise Knowledge.

In a recent blog, “Top Ways to Get Your Content and Data Ready for AI,” Sara Mae O’Brien-Scott and Zach Wahl gave an approach for ensuring your organization is ready to undertake an AI Initiative. While the previous blog provided a broad view of AI-readiness for all types of Knowledge Assets collectively, this blog will leverage the same approach, zeroing in on actionable steps to ensure your content is ready for AI. Content, also known as unstructured information, is pervasive in every organization. In fact, for many organizations it comprises 80% to 90% of the total information held within the organization. Within that corpus of content, there is a massive amount of value, but there also tends to be chaos. We’ve found that most organizations should only be actively maintaining 15-20% of their unstructured information, with the rest being duplicate, near-duplicate, outdated, or completely incorrect. Without taking steps to clean it up, contextualize it, and ensure it is properly accessible to the right people, your AI initiatives will flounder. The steps we detail below will enable you to implement Enterprise AI at your organization, minimizing the pitfalls and struggles many organizations have encountered while trying to implement AI.

1) Understand What You Mean by “Content” (Knowledge Asset Definition) 

In a previous blog, we discussed the many types of knowledge assets organizations possess, how they can be connected, and the collective value they offer. Identifying content, or unstructured information, as one of the types of knowledge assets to be included in your organization’s AI solutions will be a foregone conclusion for most. However, that alone is insufficient to manage scope and understand what needs to be done to ensure your content is AI-ready. There are many types of content, held in varied repositories, with much likely sprawling on existing file drives and old document management systems. 

Before embarking on an AI initiative, it is essential to focus on the content that addresses your highest priority use cases and will yield the greatest value, recognizing that more layers can be added iteratively over time. To maximize AI effectiveness, it is critical to ensure the content feeding AI models aligns with real user needs and AI use cases. Misaligned content can lead to hallucinations, inaccurate responses, or poor user experiences. The following actions help define content and prepare it for AI:

• Identify the types of content that are critical for priority AI use cases.
• Work with Content Governance Groups to identify content owners for future inclusion in AI testing. 
• Map end-to-end user journeys to determine where AI interacts with users and the content touchpoints that need to be referenced by AI applications.
• Inventory priority content across enterprise-wide source systems, breaking knowledge asset silos and system silos.
• Flag where different assets serve the same intent to flag potential overlap or duplication, helping AI applications ingest only relevant content and minimize noise during AI model training.

What content means can vary significantly across organizations. For example, in a manufacturing company, content can take the form of operational procedures and inventory reports, while in a healthcare organization, it can include clinical case documentation and electronic health records. Understanding what content truly represents in an organization and identifying where it resides, often across siloed repositories, are the first steps toward enabling AI solutions to deliver complete and context-rich information to end users.

2) Ensure Quality (Content Cleanup)

Your AI Model is only as good as what’s going into it. ‘Garbage in, garbage out’, ‘steady foundation’, ‘steady house’, there are any number of ways to describe that if the content going into an AI model lacks quality, the outputs will too. Strong AI starts with strong content. Below, we have detailed both manual and automated actions that can be taken to improve the quality of your content, thereby improving your AI outcomes. 

Content Quality

Content created without regard for quality is common in the everyday workflow. While this content might serve business-as-usual processes, it can be detrimental to AI initiatives. Therefore, it’s crucial to address content quality issues within your repositories. Steps you can take to improve content quality and accelerate content AI readiness include:

• Automate content cleanup processes by leveraging a combination of human-led and system-driven approaches, such as auto-tagging content for update, archival, or removal.
• Scan and index content using automated processes to detect potential duplication by comparing titles, file size, metadata, and semantic similarity.
• Apply similarity analysis to define business rules for deleting, archiving or modifying duplicate or near-duplicate content.
• Flag content that has low-use or no-use, using analytics.
• Combine analytics and content age to determine a retention cut-off (such as removing any content older than 2 years).
• Leverage semantic tools like Named Entity Recognition (NER) and Natural Language Processing (NLP) to apply expert knowledge and determine the accuracy of content.
• Use NLP to detect overly complex sentence structure and enterprise specific jargon that may reduce clarity or discoverability.

Content Restructuring

In the blog, “Improve Enterprise AI with Semantic Content Management” we note that content in an organization exists on a continuum of structure depending on many factors. The same is true for the amount of content restructuring that may or may not need to happen to enable your AI use case. We recently saw with a client that introducing even just basic structure to a document improved AI outcomes by almost 200%. However, this step requires clear goals and prioritization. Oftentimes this part of ensuring your content is AI-ready happens iteratively as the model is applied and you can determine what level of restructuring needs to occur to best improve AI outcomes. Restructuring content to prepare it for AI involves activities such as:

• Apply tags, such as heading structures, to unstructured content to improve AI outcomes and enhance the end-user experience.
• Use an AI-assisted check to validate that heading structures and tags are being used appropriately and are machine readable, so that content can be ingested smoothly by AI systems.
• Simplify and restructure content that has been identified as overly complex and could result in hallucinations or unsatisfactory responses generated by the AI model.
• Focus on reformatting longer, text-heavy content to achieve a more linear, time-based, or topic-based flow and improve AI effectiveness. 
• Develop repeatable structures that can be applied automatically to content during creation or retroactively to provide AI with relevant content in a consumable format. 

In brief, cleaning up and restructuring content assets improves machine readability of content and therefore allows the AI model to generate stronger and more accurate outputs. To prioritize assets that need cleanup and restructuring, focus on activities and resources that will yield the highest return on investment for your AI solution. However, it is important to recognize that this may vary significantly across organizations, industries, and AI use cases. For example, an organization with a truly cross-functional use case, such as enterprise search, may prioritize deduplication of content to ensure information from different business areas doesn’t conflict when providing AI-generated responses. On the other hand, an organization with a more function-specific use case, such as streamlining legal contract review, may prioritize more hands-on content restructuring to improve AI comprehension.

3) Fill Gaps (Tacit Knowledge Capture)

Even with high-quality content, knowledge gaps that exist in your full enterprise ecosystem can cause AI errors and introduce the risk of unreliable outcomes. Considering your AI use case, the questions you want to answer, the discovery you’ve completed in previous steps, and the actions detailed below you can start to identify and fill gaps that may exist. 

Content Coverage 

Even with the best content strategy, it is not uncommon for different types of content to “fall through the cracks” and be unavailable or inaccessible for any number of reasons. Many organizations “don’t know what they don’t know”, so it can be difficult to begin this process. However, it is crucial to be aware of these content gaps, particularly when using LLMs to avoid hallucinations. Actions you may take to ensure content coverage and accelerate your journey toward content AI readiness include: 

• Leverage systems analytics to assess user search behavior and uncover content gaps. This may include unused content areas of a repository, abandoned search queries, or searches that returned no results. 
• Identify content gaps by using taxonomy analytics to identify missing categories or underrepresented terms and as a result, determine what content should be included.
• Leverage SMEs and other end users during AI testing to evaluate AI-generated responses and identify areas where content may be missing. 
• Use AI governance to ensure the model is transparent and can communicate with the user when it is not able to find a satisfactory answer.

Fill the Gap

Once missing content has been identified from information sources feeding the AI model, the real challenge is to fill in those gaps to prevent “hallucinations” and avoid user frustration that may be generated by incomplete or inaccurate answers. This may include creating new assets, locating assets, or other techniques identified which together can move the organization from AI to Knowledge Intelligence. Steps you may take to remediate the gaps and help your organization’s content be AI ready include:

• Use link detection to uncover relationships across the content, identify knowledge that may exist elsewhere, and increase the likelihood of surfacing the right content. This can also inform later semantic tagging activities.
• Identify, by analyzing content repositories, sources where content identified as “missing” could possibly exist.
• Apply content transformation practices to “missing” content identified during the content repository analysis to ensure machine readability.
• Conduct knowledge capture and transfer activities such as SME interviews, communities of practice, and collaborative tools to document tacit knowledge in the form of guides, processes, or playbooks. 
• Institutionalize content that exists in private spaces that aren’t currently included in the repositories accessed by AI.
• Create draft content using generative AI, making sure to include a human-in-the-loop step for accuracy. 
• Acquire external content when gaps aren’t organization specific. Consider purchasing or licensing third-party content, such as research reports, marketing intelligence, and stock images.

By evaluating the content coverage for a particular use case, you can start to predict how well (or poorly) your AI model may perform. When critical content mostly exists in people’s heads, rather than in documented, accessible format, the organization is exposed to significant risk. For example, an organization deploying a customer-facing AI chatbot to help with case deflection in customer service centers, gaps in content can lead to potentially false or misleading responses. If the chatbot tries to answer questions it wasn’t trained for, it could result in out-of-policy exceptions, financial loss, decrease in customer trust, or lower retention due to inaccurate, outdated, or non-existent information. This example highlights why it is so important to identify and fill knowledge gaps to ensure your content is ready for AI. 

4) Add Structure and Context (Semantic Components)

Once you have identified the relevant content for an AI solution, ensured its quality for AI, and addressed major content gaps for your AI use cases, the next step in getting content ready for AI involves adding structure and context to content by leveraging semantic components. Taxonomy and metadata models provide the foundational structure needed to categorize unstructured content and provide meaningful context. Business glossaries ensure alignment by defining terms for shared understanding, while ontology models provide contextual connections needed for AI systems to process content. The semantic maturity of all of these models is critical to achieve successful AI applications. 

Semantic Maturity of Taxonomy and Business Glossaries

Some organizations struggle with the state of their taxonomies when starting AI-driven projects. Organizations must actively design and manage taxonomies and business glossaries to properly support AI-driven applications and use cases. This is not only essential for short-term implementation of the AI solution, but most importantly for long-term success. Standardization and centralization of these models help balance organization-wide needs and domain-specific needs. Properly structured and annotated taxonomies are instrumental in preparing content for AI. Taking the following actions will ensure that you have the Semantic Maturity of Taxonomies and Business Glossaries needed to achieve AI ready content:

• Balance taxonomies across business areas to ensure organization-wide standardization, enabling smooth implementation of AI use cases and seamless integration of AI applications. 
• Design hierarchical taxonomy structures with the depth and breadth needed to support AI use cases.
• Refine concepts and alternative terms (synonyms and acronyms) in the taxonomy to more adequately describe and apply to priority AI content.
• Align taxonomies with usability standards, such as ANSI/NISO Z39.19, and interoperability/machine readability standards, such as SKOS, so that taxonomies are both human and machine readable.
• Incorporate definitions and usage notes from an organizational business glossary into the taxonomy to enrich meaning and improve semantic clarity.
• Store and manage taxonomies in a centralized Taxonomy Management System (TMS) to support scalable AI readiness.

Semantic Maturity of Metadata 

Before content can effectively support AI-driven applications, organizations must also establish metadata practices to ensure that content has been sufficiently described and annotated. This involves not only establishing shared or enterprise-wide coordinated metadata models, but more importantly, applying complete and consistent metadata to that content. The following actions will ensure that the Semantic Maturity of your Metadata model meets the standards required for content to be AI ready:

• Structure metadata models to meet the requirements of AI use cases, helping derive meaningful insights from tagged content.
• Design metadata models that accurately represent different knowledge asset types (types of content) associated with priority AI use cases.
• Apply metadata models consistently across all content source systems to enhance findability and discoverability of content in AI applications. 
• Document and regularly update metadata models.
• Store and manage metadata models in a centralized semantic repository to ensure interoperability and scalable reuse across AI solutions.

Semantic Maturity of Ontology

Just as with taxonomies, metadata, and business glossaries, developing semantically rich and precise ontologies is essential to achieve successful AI applications and to enable Knowledge Intelligence (KI) or explainable AI. Ontologies must be sufficiently expressive to support semantic enrichment, traceability, and AI-driven reasoning. They must be designed to accurately represent key entities, their properties, and relationships in ways that enable consistent tagging, retrieval, and interpretation across systems and AI use cases. By taking the following actions, your ontology model will achieve the level of semantic maturity needed for content to be AI ready:

• Ensure ontologies accurately describe the knowledge domain for the in-scope content.
• Define key entities, their attributes, and relationships in a way that supports AI-driven classification, recommendation, and reasoning.
• Design modular and extensible ontologies for reuse across domains, applications, and future AI use cases.
• Align ontologies with organizational taxonomies to support semantic interoperability across business areas and content source systems.
• Annotate ontologies with rich metadata for human and machine readability.
• Adhere to ontology standards such as OWL, RDF, or SHACL for interoperability with AI tools.
• Store ontologies in a central ontology management system for machine readability and interoperability with other semantic models.

Preparing content for AI is not just about organizing information, it’s about making it discoverable, valuable, and usable. Investing in semantic models and ensuring a consistent content structure lays the foundation for AI to generate meaningful insights. For example, if an organization wants to deliver highly personalized recommendations that connect users to specific content, building customized taxonomies, metadata models, business glossaries, and ontologies not only maximizes the impact of current AI initiatives, but also future-proofs content for emerging AI-driven use cases.

5) Semantic Model Application (Content Tagging)

Designing structured semantic models is just one part of preparing content for AI. Equally important is the consistent application of complete, high-quality metadata to organization-wide content. Metadata enrichment of unstructured content, especially across silo repositories, is critical for enabling AI-powered systems to reliably discover, interpret, and utilize that content. The following actions to enhance the application of content tags will help you achieve content AI readiness:

• Tag unstructured content with high-quality metadata to enhance interpretability in AI systems.
• Ensure each piece of relevant content for the AI solution is sufficiently annotated, or in other words, it is labeled with enough metadata to describe its meaning and context. 
• Promote consistent annotation of content across business areas and systems using tags derived from a centralized and standardized taxonomy. 
• Leverage mechanisms, like auto-tagging, to enhance the speed and coverage of content tagging. 
• Include a human-in-the-loop step in the auto-tagging process to improve accuracy of content tagging.

Consistent content tagging provides an added layer of meaning and context that AI can use to deliver more complete and accurate answers. For example, an organization managing thousands of unstructured content assets across disparate repositories and aiming to deliver personalized content experiences to end users, can more effectively tag content by leveraging a centralized taxonomy and an auto-tagging approach. As a result, AI systems can more reliably surface relevant content, extract meaningful insights, and generate personalized recommendations.

6) Address Access and Security (Unified Entitlements)

As Joe Hilger mentioned in his blog about unified entitlements, “successful semantic solutions and knowledge management initiatives help the right people see the right information at the right time.” But to achieve this, access permissions must be in place so that only authorized individuals have visibility into the appropriate content. Unfortunately, many organizations still maintain content in old repositories that don’t have the right features or processes to secure it, creating a significant risk for organizations pursuing AI initiatives. Therefore, now more than ever, it is important to properly secure content by defining and applying entitlements, preventing access to highly sensitive content by unauthorized people and as a result, maintaining trust across the organization. The actions outlined below to enhance Unified Entitlements will accelerate your journey toward content AI readiness:

• Define an enterprise-wide entitlement framework to apply security rules consistently across content assets, regardless of the source system.
• Automate security by enforcing privileges across all systems and types of content assets using a unified entitlements solution.
• Leverage AI governance processes to ensure that content creators, managers, and owners are aware of entitlements for content they handle and needs to be consumed by AI applications.

Entitlements are important because they ensure that content remains consistent, trustworthy, and reusable for AI systems. For example, if an organization developing a Generative AI solution stores documents and web content about products and clients across multiple SharePoint sites, content management systems, and webpages, inconsistent application of entitlements may represent a legal or compliance risk, potentially exposing outdated, or even worse, highly sensitive content to the wrong people. On the other hand, the correct definition and application of access permissions through a unified entitlements solution plays a key role in mitigating that risk, enabling operational integrity and scalability, not only for the intended Generative AI solution, but also for future AI initiatives.

7) Maintain Quality While Iteratively Improving (Governance)

Effective governance for AI solutions can be very complex because it requires coordination across systems and groups, not just within them, especially among content governance, semantic governance, and AI governance groups. This coordination is essential to ensure content remains up to date and accessible for users and AI solutions, and that semantic models are current and centrally accessible. 

AI Governance for Content Readiness 

Content Governance 

Not all organizations have supporting organizational structures with defined roles and processes to create, manage, and govern content that is aligned with cross-organizational AI initiatives. The existence of an AI Governance for Content Readiness Group ensures coordination with the traditional Content Governance Groups and provides guidance to content owners of the source systems on how to get content AI ready to support priority AI use cases. By taking the following actions, the AI Governance for Content Readiness Group will help ensure that you have the content governance practices required to achieve AI-ready content:

• Define how content should be captured and managed in a way that is consistent, predictable, and interoperable for AI use cases.
• Incorporate in your AI solution roadmap a step, delivered through the Content Governance Groups, to guide content owners of the source systems on what is required to get content AI ready for inclusion in AI models.
• Provide guidance to the Content Governance Group on how to train and communicate with system owners and asset owners on how to prepare content for AI.
• Take the technical and strategic steps necessary to connect content source systems to AI systems for effective content ingestion and interpretation.
• Coordinate with the Content Governance Group to develop and adopt content governance processes that address content gaps identified through the detection of bias, hallucinations, or misalignment, or unanswered questions during AI testing.
• Automate AI governance processes leveraging AI to identify content gaps, auto-tag content, or identify new taxonomy terms for the AI solution.

Semantic Models Governance

Similar to the importance of coordinating with the content governance groups, coordinating with semantic models governance groups is key for AI readiness. This involves establishing roles and responsibilities for the creation, ownership, management, and accountability of semantic models (taxonomy, metadata, business glossary, and ontology models) in relation to AI initiatives. This also involves providing clear guidance for managing changes in the models and communicating updates to those involved in AI initiatives. By taking the following actions, the AI Governance for Content Readiness Group will help ensure that your organization has the semantic governance practices required to achieve AI-ready content: 

• Develop governance structures that support the development and evolution of semantic models in alignment with both existing and emerging AI initiatives.
• Align governance roles (e.g. taxonomists, ontologists, semantic engineers, and AI engineers) with organizational needs for developing and maintaining semantic models that support enterprise-wide AI solutions.
• Ensure that the systems used to manage taxonomies, metadata, and ontologies support enforcing permissions for accessing and updating the semantic models.
• Work with the Semantic Models Governance Groups to develop processes that help remediate gaps in the semantic models uncovered during AI testing. This includes providing guidance on the recommended steps for making changes, suggested decision-makers, and implementation approaches.
• Work with the Semantic Models Governance Groups to establish metrics and processes to monitor, tune, refine, and evolve semantic models throughout their lifecycle and stay up to date with AI efforts.
• Coordinate with the Semantic Models Governance Groups to develop and adopt processes that address semantic model gaps identified through the detection of bias, hallucinations, or misalignment, or unanswered questions during AI solution testing.

For example, imagine an organization is developing business taxonomies and ontologies that represent skills, job roles, industries, and topics to support an Employee 360 View solution. It is essential to have a governance model in place with clearly defined roles, responsibilities, and processes to manage and evolve these semantic models as the AI solutions team ingests content from diverse business areas and detects gaps during AI testing. Therefore, coordination between the AI Governance for Content Readiness Group and the Semantic Models Governance Groups helps ensure that concepts, definitions, entities, properties, and relationships remain current and accurately reflect the knowledge domain for both today’s needs and future AI use cases.  

Conclusion

Unstructured content remains as one of the most common knowledge assets in organizations. Getting that content ready to be ingested by AI applications is a balancing act. By cleaning it up, filling in gaps, applying rich semantic models to add structure and context, securing it with unified entitlements, and leveraging AI governance, organizations will be better positioned to succeed in their own AI journey. We hope after reading this blog you have a better understanding of the actions you can take to ensure your organization’s content is AI ready. If you want to learn how our experts can help you achieve Content AI Readiness, contact us at info@enterprise-knowledge.com. 

The post How to Ensure Your Content is AI Ready appeared first on Enterprise Knowledge.

The way knowledge assets are defined is crucial for an organization because it directly impacts how they are managed, leveraged, and protected. This includes identifying which knowledge assets have strategic value, how to manage them to make them accessible for timely decision making, which management policies should be applied to ensure effective knowledge sharing, retention, continuity, and transfer, and which steps are necessary to comply with industry regulations.

This blog highlights the types of knowledge assets that are commonly found in organizations and provides industry-specific examples based on typical Knowledge Management (KM) Use Cases.

Examples Of Relevant Knowledge Asset Types Per Industry

As illustrated in the previous section describing the different types of knowledge assets, these assets encompass more than just content or data. They may include people’s expertise and experience, transaction records, policies, and even facilities or locations. Depending on the industry or organization type, certain knowledge assets may be prioritized in early use cases because they play a more central role in those specific contexts.

	A manufacturing company looking to improve its supply chain processes would benefit significantly from tagging, managing, leveraging, and protecting operational and logistical resources — such as equipment, facilities, and products — and linking them to reveal relationships and dependencies across the supply chain. By also tagging and connecting additional knowledge assets, such as structured data and analytical resources — including order history, transactions, and metrics — and content and documentation — such as process descriptions and reports — the company may gain deeper visibility into operational bottlenecks, enhance forecasting accuracy, and improve coordination across departments. This holistic approach can enable more agile decision-making, reducing downtime and supporting continuous improvement across the entire manufacturing lifecycle.
	A bank that is looking to maintain compliance, uphold governance standards, and minimize regulatory risk can benefit from managing, leveraging, and protecting its key knowledge assets in a standardized and connected way. By using key terminology to tag governance and compliance resources — such as corporate policies, industry regulations, and tax codes — alongside operational and logistical resources  — such as locations and facilities — and corresponding subject matter experts, the bank builds a foundation for semantic alignment. This will allow the bank not only to associate branches and operational sites with the specific policies and regulatory obligations they must meet, but also help ensure that the bank complies with jurisdiction-specific requirements, reduces audit exposure, and strengthens its ability to respond to regulatory changes with agility and confidence.
	A healthcare organization relies on clinical expertise and institutional memory to diagnose and treat patients. By capturing, tagging, and sharing expertise and experience from physicians and multidisciplinary teams, doctors, nurses, and other support personnel will be able to timely access the expert-based information they need to diagnose and treat their patients more accurately. Additionally, having access to content and documentation from clinical cases and structured data from research studies will also help improve decision-making for the personnel of this healthcare organization.

Do you know which priority knowledge assets and related KM use cases can transform your organization by empowering teams to surface hidden insights, accelerating decision-making, or fostering operational excellence? If you need help uncovering the most valuable use cases and the associated knowledge assets that unlock meaningful transformation in your organization, we can help. Contact us to learn more. 

The post Breaking Down Types of Knowledge Assets and Their Impact appeared first on Enterprise Knowledge.

Baquero Cakici and O’Brien-Scott provided real-world examples of how taxonomy and ontology transform the way people interact with and manage learning content, including through recommendation engines, metadata hubs, and chatbots. The speakers also shared practical considerations for designing semantic models for AI use cases and guiding change enablement and adoption as semantic solutions are rolled out. 

Participants in the session gained insights into:

• Practical considerations, design methods, and best practices for designing learning taxonomies and ontologies to catalyze AI transformations;
• Examples of how AI solutions such as recommendation engines, chatbots, and metadata hubs can help solve learning content management challenges; and
• Key insights from real-world taxonomy and ontology design projects for successful transformation on learning content management.

The post Butterfly Effect: Taxonomy and Ontology as AI Catalysts in Enterprise Learning appeared first on Enterprise Knowledge.

If your organization is considering the adoption of Search-Focused AI solutions, EK is here to help. With our extensive expertise, we specialize in crafting and deploying customized and actionable solutions that enhance organizations’ information search and knowledge findability. Please feel free to contact us for more information.

The post KM Fast Track to Search-Focused AI Solutions appeared first on Enterprise Knowledge.

Their presentation “Climbing the Ontology Mountain to Achieve a Successful Knowledge Graph” focused on how ontologies have gained momentum as a strong foundation for resolving business challenges through semantic search solutions, recommendation engines, and AI strategies. Cakici and Doughty explained that taxonomists are now faced with the challenge of gaining knowledge and experience in designing and documenting complex solutions that involve the integration of taxonomies, ontologies, and knowledge graphs. They also emphasized that taxonomists are well poised to learn how to design user-centric ontologies, analyze and map data from various systems, and understand the technological architecture of knowledge graph solutions. After describing the key roles and responsibilities needed for a team to successfully implement Knowledge Graph projects, Cakici and Doughty shared practical ontology design considerations and best practices based on their own experience. Lastly, Cakici and Doughty reviewed the most common use cases for knowledge graphs and presented real world applications through a case study that illustrated ontology design and the value of knowledge graphs.

The post Climbing the Ontology Mountain to Achieve a Successful Knowledge Graph appeared first on Enterprise Knowledge.

Each use case presented in this blog includes:

• A Persona: the end user who has the need to access certain employee information to achieve a specific business goal. This could be an organization’s top executive, a team lead, or an employee.
• A Sample Question: the end user’s goal in the form of a question that describes what the end user wants to achieve. 
• Possible Data Sources: the information systems and data sources that would be necessary to pull information into the Employee 360 View and accomplish the end user’s goal. These may include Recruiting Systems, HR Systems of Record, 360 Feedback and Employee Survey Systems, Learning Management Systems (LMS), Enterprise Resource Planning (ERP) Systems, Project Management Systems, Document Management Systems (DMS), and Customer Relationship Management (CRM) Systems.
• Why it is important: the reasons why an Employee 360 View is an innovative and effective approach to achieve the end user’s goal.

Use Case 1: Project Resource Allocation and Projection

Persona: Operations Manager in a Consulting Firm 

Sample Question: Which consultants have the right accounting and project management skills and level of expertise to be assigned to this new project team and ensure success?

Possible Data Sources: Recruiting Systems, HR System, 360 Feedback System, LMS, ERP, Project Management Systems, DMS, CRM. 

An Employee 360 View can pull information about an employee’s current and past experience, their skills and strengths, how their supervisors and peers perceive their job performance, the knowledge areas and skills recently acquired through training, the types of projects and teams that they have managed, their current utilization rate, the expertise they have demonstrated through document authoring and publishing, and the type of clients that they worked with to better determine whether an employee is the right candidate for the new project team.  

Why is this important? Assigning an employee to the right project team can help them influence key project decisions, increasing the likelihood of a successful project. Consequently, this can make them feel that they are making a difference, increasing their morale, job performance, and the performance of their project team. Similarly, a good balance of project experiences can help employees gain new skills throughout their lifecycle at the company, increasing employee’s productivity, employee satisfaction, and helping the employee become more valuable to the company. By focusing on each individual employee project allocation, companies can become better at optimally utilizing all of their employees. As an additional benefit, organizations will be able to project organizational capacity and resource gaps using this combined data and analytics.

Use Case 2: Performance Evaluation and Promotion

Persona: Sales Director

Sample Question: Who from my sales department excelled at their job this year and deserves to be promoted to a manager role?

Possible Data Sources: HR System, 360 Feedback and Employee, ERP, CRM.

An Employee 360 View can help gather information about an employee, such as promotions and awards, feedback provided by their supervisor and colleagues, new leads acquisition, business development goals, sales targets and achievements, and interactions with clients to help determine whether an employee should be promoted to a sales manager role.

Why is this important? Promoting the right employees at the right time can help boost their motivation and morale. Consequently, this can result in high productivity and prevent losing valuable employees. Making an informed promotion decision requires having access to not only the skills and knowledge acquired or to whether the employee has achieved or surpassed their sales targets, but also understanding the highs and lows of their interactions with supervisors, peers, and clients.

Use Case 3: Training and Capacity Building

Persona: Marketing Director

Sample Question: Are there any good training courses that would help John achieve his marketing campaign targets and close more deals for a sustainable business growth?

Possible Data Sources: HR System, 360 Feedback System, LMS, ERP. 

An Employee 360 View can help gather information about an employee, such as what skills and knowledge they have acquired by attending past conferences or training sessions, what strengths they have developed by participating in specific projects, and also what areas for improvements their supervisors and peers see in them that could benefit from new learning activities or a coaching program. 

Why is this important? Providing opportunities for growth and development is key for increasing employee satisfaction and represents a strong motivator for employees, sometimes as much or even more important than a salary increase. Internal and external training programs, mentorship and coaching opportunities, and job swaps are only a few of the activities that employees expect from an organization to learn new skills and help them move up in their career. Businesses with happy employees due to better career growth paths will consequently benefit from increased productivity and more value from their staff. Training employees in new tools and approaches is an effective way to improve the collective capabilities of staff, achieve capacity building, and make a long-term impact in the organization.

Use Case 4: Identifying Employees Who May be at Risk of Leaving

Persona: HR Director

Sample Question: Which employees may be unhappy and could be considering leaving the company?

Possible Data Sources: HR System, LMS, ERP, DMS, CRM.

An Employee 360 View can help gather information about an employee, such as changes in their learning path (less interest in getting training), a decrease in the amount of content they create and share with their peers, lower productivity than before, an increase in the number of projects they manage, low feedback scores, lower quality interactions with their clients, and missed target sales. When isolated, these pieces of information may not mean anything, but when put together can help identify a specific employee who could benefit from some coaching, training, or simply from a discussion to help them make adjustments to their current jobs or tasks. 

Why is this important? It is equally important to invest in hiring good employees as it is to invest in retaining them. Identifying lower performers (that were once higher performers) early enough and taking action can help improve morale, engagement level, and productivity. Consequently, this can help the company retain valuable employees and save on hiring costs. 

Use Case 5: Expert Locator

Persona: Data Specialist in a Large Consulting Firm 

Sample Question: Which other consultants have expertise with designing data integration solutions for federal agencies who can help me answer a question for my current project? 

Possible Data Sources: Recruiting Systems, HR System, LMS, ERP, Project Management Systems, DMS, CRM. 

An Employee 360 View can pull information about other consultants including their current and past experience, their business and technical skills, areas of subject matter expertise, recent trainings, the projects and clients they have worked with, and the documents they have authored and published, so that employees can connect with people in their organization who have the expertise, skills, and knowledge that they are looking for. An Employee 360 View can act as an expert locator solution for organizations.

Why is this important? Having the ability to connect “the people who have the knowledge” with “the people who need the knowledge” at the right time helps avoid reinventing the wheel to solve similar problems and increases efficiency, especially in large organizations that employ hundreds or thousands of people. When staff don’t know who knows what, an enormous amount of time is invested in trying to find the answer or recreating knowledge that already exists within the organization. Employee 360 Views not only help building new project teams projects by finding various expertise needed (as it was mentioned in Use Case 1: Project Resource Allocation and Projection), but also helps employees ask questions to the right people at the right time. 

Conclusion

Whether you are a top executive, a team lead, or an employee, at some point you will need access to consolidated employee information to support key corporate decisions. Employee 360 Views help forecast organizational capacity and resource gaps, support performance evaluation and job promotion decisions, enhance capacity building, identify employees who may be at risk of leaving, and connect subject matter experts within the organization.  

At EK, we can help design and implement an Employee 360 View that meets your organization’s business needs. For more information, contact us. 

The post Employee 360 Views: Common Use Cases appeared first on Enterprise Knowledge.

Imagine if a C-level executive could predict what skill gaps the organization will be experiencing within the next quarter and identify the right talent needed for organizational capacity building. What if a team lead could get a complete picture of an employee profile, job activity, and performance before deciding on an individual’s promotion or encouraging them to pursue a new training opportunity? And what if an employee could easily identify who the right experts are in a particular subject to support a project at a time of need?  These are the kinds of questions that an Employee 360 View can help answer. This blog will discuss what Employee 360 views are, which corporate data can feed them, and in which cases an organization can leverage them to increase productivity and efficiency.

Collecting data about an employee from multiple sources is a big challenge for many organizations. By attempting to solve various internal people and operational data challenges with targeted technology solutions, organizations have ended up with multiple, siloed systems that don’t talk to each other or that aren’t necessarily easy to integrate. These include HR systems of record, learning management systems (LMS), Client Relationship Management (CRM) and Enterprise Resource Planning (ERP) systems, just to mention a few among a long list of common operational and transactional platforms. When employee related data sits on multiple systems, it becomes very difficult for HR leaders to access the right information at the right time to make better informed decisions about the staff.

What are Employee 360 Views?

You may be familiar with a few other employee performance evaluation related terms that use the concept of 360, but these are often solely focused on HR processes. These include 360-degree review, 360-degree evaluation, and 360-degree feedback, among others. Even though they can provide helpful information about an employee, they are different from Employee 360 Views. An Employee 360 View is a single, robust, and centralized view of each employee’s digital information and professional career at a company. It gives corporate leaders and employees the ability to view and understand everything about an employee across their life cycle within an organization, leveraging all the information and data available in multiple, disparate systems and consolidating it into a single view for better decision-making. This can be achieved by developing a data management strategy and systems architecture that appropriately extracts, tags, transforms, standardizes, integrates, and warehouses employee related information siloed across disparate systems. This requires a combination of strategies to appropriately categorize and centralize the content into a single view. For example:

• Recognizing all potential employee identifiers across siloed systems and consolidating the data into a unique employee ID;
• Designing and implementing an enterprise taxonomy (e.g. skills, job roles, industries, topics, etc.) to categorize and tag data extracted from different systems; and
• Designing and implementing an ontology and a knowledge graph to imply hidden relationships between entities (e.g. job roles and skills, training courses and skills, projects and industries, etc.).

The networks that an employee is part of and the interactions that they have with others within the organization aren’t always explicit. Those may not be reflected in an organizational chart or other formal representations of an organization’s structure. An Employee 360 View is very powerful because it can help uncover people’s connections, relationships, and hidden networks. This may help identify ‘star performers’ or the ‘go to’ employees that staff typically reach out for support or who become part of project teams. Employee 360 Views can also help inform why an employee may be underperforming, what skills would be valuable to them to acquire to increase efficiency, or how their skills could be better leveraged to benefit their project teammates or colleagues.

Data Sources

So what are the main sources of information that can feed Employee 360 Views and what data can be collected from each of them? The following diagram illustrates the various information systems that contain employee information and can be used to create a unified view of the employee lifecycle.  

• Recruiting Systems: employee’s previous jobs, skills, and competencies acquired prior to applying or joining an organization.
• HR Systems of Record: jobs performed at the current organization, skills and competencies acquired, promotions, recognitions, and awards. 
• 360 Feedback and Employee Survey Systems: feedback provided by supervisors and colleagues as part of employee evaluations, pulse surveys to measure employee satisfaction and other HR topics.
• Learning Management Systems (LMS): past training courses, either internal or external. 
• Enterprise Resource Planning (ERP) Systems: projects and teams that the employee has been part of, products and services led or supported.
• Project Management Systems: previous and current projects managed, tasks and milestones completed.
• Document Management Systems (DMS): documents and publications authored by the employee.
• Customer Relationship Management (CRM) Systems: lead acquisition, client interactions, revenue management, target goals. 

A combination of some or all of these systems can help organizational leaders address specific use cases and answer particular questions about employees for better decision making. In my next blog, we will take a look at some of the most common Employee 360 View use cases, what organizational challenges they help solve, and why they are important.

Conclusion

Employee 360 Views aren’t just about the ability to consolidate an employee’s information into a single view. They help you gain valuable insights about your employees so that you can leverage that unified information to improve decision making at the right time, increase business value, retain your best people, and remain competitive. 

Do you need help designing and implementing an Employee 360 View for your organization? Contact us. We will be happy to help!

The post Employee 360 Views: What are they? appeared first on Enterprise Knowledge.

First, let’s describe what an orchestra is. An orchestra is an ensemble of instruments that includes woodwind, brass, string, and percussion sections. A group of musicians performs various pieces of music with these instruments, creating a captivating experience for an audience. Under the direction of the conductor, everyone needs to play music in harmony to ensure that the audience enjoys the music performance. An orchestra performance is an example of leadership, collaboration, coordination, learning, and exemplary execution, a lot like the characteristics needed to successfully manage knowledge in any organization. 

For the purpose of this blog, organizational content is the equivalent of the music that is delivered by an orchestra to the target audience. Let’s take a look at how similar content management is to an orchestra performance.

Orchestra Performance
Content Management
Music
Organizational Content
Conductor	Instruments	Musicians	Music Sheet	Audience
Content Lead	Content Types	Content Authors and Content Owners	Business Taxonomy	End Users (internal or external)

The Conductor (Content Lead)

An orchestra conductor has a vision of how the orchestra should sound when playing each piece of music. The conductor keeps an orchestra in time and together, lets each musician know their time of entry, and is able to give each musician direction about what they should be doing at any given moment during the performance. One of the main responsibilities of a conductor is to fully understand each piece of music and effectively communicate to the musicians so that they understand it completely, which is mostly done with gestures and the aid of a baton. Additionally, by being readily available to the musicians prior to the performance as well as visible from a podium during the performance, the conductor ensures that the communication channels with all orchestra members are effective at any given time (e.g. during rehearsal, on stage, etc.).

Similarly to an orchestra conductor, a Content Lead needs to have not only a clear vision of all key content areas in the organization, but also the ability to effectively communicate with content authors and content owners, so that they can create, tag, and maintain quality content. Defining a content governance plan, a taxonomy governance plan, and identifying effective communication channels and tools (what would be the gestures and the baton for the orchestra conductor) are essential to transfer that content management vision to content authors and content owners successfully. Examples of communication channels and tools may include recurring group meetings, one-on-one discussions, centralized content repositories, portals, and any other tools that can help govern content and taxonomies consistently. 

The Instruments (Content Types)

From the lively and sparkling sounds of violins to the dry and rattling sounds of percussion instruments, the graceful and clear sounds of a flute to the vibrant brass sections, listening to all the instruments playing together and in harmony in an orchestra is an impressive musical spectacle. Each instrument has a different appearance, a different purpose, and requires a specific technique to be played. They all produce different sounds that when put together, produce a magnificent piece of music. 

Even though content types are not as graceful as musical instruments, in content management, content types represent types of instruments, each with a purpose to create and manage a specific type of content. Content types are like templates for categories of content with corresponding taxonomies that allow managing information in a centralized, reusable way. Some content types are designed to create announcements, others to create corporate policies, but together, all content types help communicate key organizational content to the end users in a standard and consistent way. 

The Musicians (Content Authors and Content Owners)

Without exception, successful orchestras around the world have clearly defined roles and responsibilities. If the conductor has done a good job communicating the expectations of the musical performance to the musicians, and the musicians have mastered playing their own instruments, then they can play their instruments accordingly and transmit a unified vision of the music to the audience. Every musician must not only follow the same set of music sheets, but also understand their own role, the roles of their fellow musicians, and when the handoffs need to take place during the performance. 

Similarly, in content management, the content authors and content owners are like the musicians. They are tasked with very specific roles, in this case to create, tag, manage, and disseminate organizational content. If they have a good understanding of the organization’s content management objectives and have the knowledge management skills needed to perform their roles, they can effectively create and maintain organizational content, communicating a clear and unified vision of the content to the end users. In the same way that musicians spend time practicing and learning the skills to master their instruments, content authors and content managers need to clearly understand how to leverage content types and taxonomy to create and manage content and master the skills needed to meet their content management responsibilities.

The Music Sheet (Business Taxonomy)

In an orchestra, even though every instrument gets their own music sheet, the conductor gets a full score, or in other words, a music sheet that contains the musical notation for all instruments, so that the whole orchestra starts playing together at the same time and performs at the same tempo throughout the performance. 

An enterprise taxonomy represents that standard point of reference that can help orchestrate organizational content, so that content authors and content managers can ultimately leverage content types and taxonomy together to collaborate and produce consistently tagged, high-quality content.

The Audience (End Users)

Focusing on a particular target audience when planning and rehearsing for a performance helps musicians connect with their audience during the actual performance. Who is the audience? What is the music really trying to convey to the audience? From behind their music stands, the musicians sitting nearest the audience typically sit at a diagonal facing partly toward the conductor and partly toward the audience, so that the audience can be more engaged. Those in the front rows can look at the musicians closely, see them smile at the end of each musical piece, and more naturally react to the music with joy.  

In content management, learning about your audience is indispensable to serve end users with the content they need, when they need it. Depending on the type of organizational content and where it will be displayed (e.g. Intranet, portal, dashboard, etc.), your audience may be internal, such as employees, or external, including customers, partners, and even prospective groups. Understanding your audience means gaining a clear understanding of their motivations, needs, goals, and challenges, so that the content is delivered in a manner that meets their content needs, resonates with them, and appeals to them. The use of personas and user stories help organizations move from knowing their audience to most importantly, understanding their audience and delivering timely, targeted content. In the same way that a venue may solicit feedback from the attendees to identify how well received the orchestra performance was, there are multiple approaches that organizations can take to measure the effectiveness of their content and identify whether the content is performing as expected. Only if content is measured, it can be managed and improved.   

Conclusion

By helping your organization focus on these five elements, you could find yourself delivering an exemplary knowledge management performance alongside a content management team that earns a standing ovation. Next time you go to an orchestra performance, and while you enjoy the music, try closing your eyes and think about all that was required to make that performance happen. 

Need help with orchestrating your organization’s content management journey? Contact us.  

The post Five things that Content Management and an Orchestra Performance Have in Common appeared first on Enterprise Knowledge.

If you want to be successful with a merger and acquisition (M&A) transaction, consider developing a Knowledge Management (KM) Strategy to guide the process.  One of the most important factors in achieving a successful M&A transaction is “effective integration.” But what does effective integration mean? At EK, we encourage organizations to consider analyzing integration from the five KM perspectives of People, Process, Content, Culture, and Technology. This analysis includes defining both a current state and target state for all five areas. Let’s take a look at how KM best practices for each of those five KM areas can help you achieve an effective integration during a M&A transaction.

People

The people aspect of KM refers to how the overarching structure of leadership and staff supports knowledge flow throughout the organization, and how people communicate and connect with one another to more effectively create and share knowledge. Traditionally, a large portion of M&A transactions have focused on acquiring thoughts, methodologies, people, and relationships. When two separate entities combine forces to create a new organization, or when a company takes over another, people and relationships from both sides are inevitably impacted. Due to the COVID-19 pandemic, it is now even more important to determine whether both entities have enough employees and resources to successfully continue business operations. A KM Strategy can help identify and prioritize the skill sets required at all levels in the organization to grow the new, joint business. 

Most importantly, it is essential to analyze the softer and more human side of employees during a transaction. Employees from the two entities may experience different levels of anxiety that emanate from uncertainty around job security and future responsibilities. Addressing their concerns, and considering how to utilize their skills under the new structure will not only help establish high-performing teams, but most importantly, will boost morale and help keep employees happy. Increasing employee satisfaction in turn improves retention rates and reduces turnover, ensuring the organization holds on to motivated individuals who are then willing to create and share innovative content that would have otherwise been lost if they had been left the organization. Additionally, strengthening leadership and workforce around KM practices, such as establishing rewards and recognition mechanisms for sharing knowledge and embedding content management tasks in daily job processes  can help create value from the knowledge held by both organizations, while encouraging knowledge creation, retention, and collaboration. 

The creation of Communities of Practice and other similar knowledge sharing mechanisms can help improve the performance of members from both organizations by enabling individuals who share common objectives and work practices to discuss and find ways to improve them. Additionally, these knowledge sharing mechanisms can help encourage innovation in the new merged organization and enable individuals to learn from others that they might not interact with on a daily basis. Even though calculating the return on investment (ROI) of knowledge sharing mechanisms can be seen as challenging, consider estimating the cost savings due to knowledge sharing activities. For example, you can estimate the number of hours that members of a Community of Practice can save when they are able to find templates or subject matter experts on the community page to accomplish specific business objectives that otherwise would have taken them a long time. Another example of ROI for knowledge sharing activities is estimating call deflection for internal support teams. By having support documentation available in a knowledge sharing repository (e.g. an internal IT Support page), the new joint organization can save time and money as a result of the reduction of phone calls, emails, and chat sessions from employees to the IT team. 

Process

Before deciding to move forward with a M&A transaction, you need to understand each side’s business operations, along with associated business processes, key players, and any major pain points. Typically, mid-level managers from various business areas are tasked with integrating their own operations with the other company’s operations without fully understanding what it takes to be successful. A KM Strategy can certainly help with the effort of identifying and improving core business processes for the merged organization. From a KM perspective, a primary goal of optimizing business processes should be to systematically capture, manage, and enhance information, while making optimal use of resources, minimizing costs, and maintaining high-quality operations. Hand-in-hand with process optimization should be the decision on which business processes to automate. Business process automation involves identifying which specific steps or recurring tasks could be executed with a technology and then choosing the right tool to achieve it. Optimizing business processes and automating them wherever possible will allow organizations to experience better flow of information, increased efficiency, streamlined tasks, better utilization of resources, and end-to-end visibility of the new, joint operations. 

From a content management perspective, understanding how key organizational content gets created, tagged, and shared is also critical. Identifying not only what the major areas of content are, but also who the content creators and content owners are, where content is stored, which taxonomies are leveraged for tagging, and most importantly how content flows through the organization will get you in the right direction to manage content effectively. Successful integration of the two organizations in a M&A transaction involves translating very distinct content management processes from each party into new, joint content management processes that align with the business goals of the M&A transaction. As part of a KM Strategy, a  robust content and taxonomy governance plan will help you define the roles, responsibilities, policies, and processes necessary to support the flow of content in the new organization. 

Content

As you explore the possibility of a M&A, it is essential to determine the knowledge, information, and data management needs of the target state for the merged organization. Performing a content inventory and developing a content clean-up strategy for both entitiesare recommended first steps. A content inventory will allow the merged organization to assess the state of content from various content sources (e.g., document management systems, content management systems, shared drives, etc.), achieve a full understanding of each system’s content (types of content, volume, state, and location), and define a content strategy. The content strategy should address the optimization of content types as well as the definition of user-centric business taxonomies that would help achieve the ideal state for content creation, management, and search. A complete content inventory and a thorough clean-up effort are part of the foundational activities needed to achieve an optimal enterprise search experience, especially after a M&A transaction where outdated or inaccurate content could pose a risk to the operations of the merged organization.With so much information available from both organizations it will be difficult to determine what is current or what is relevant unless a content clean-up effort has been completed. 

Following the completion of the content inventory, both organizations need to assess the quality of their content to inform future clean-up or migration efforts (whether to maintain, delete, or archive certain content), and determine a governance plan to ensure the effectiveness and sustainability of any new content management activities. As a next step, it’s important to determine whether the existing technologies meet the business’ knowledge, information, and data management needs or whether new technologies will be needed after the M&A is complete. The goal of conducting a content inventory and developing a content clean-up strategy is to ensure that people have access to the most relevant and up-to-date content (NERDy content), while experiencing improved content findability and reduced content clutter that may result from the M&A effort.  

Culture

From a KM perspective, culture refers to employees’ willingness to share, collaborate, and support one another, and whether innovation and change are encouraged and valued across the organization. During a M&A, you are merging more than two companies’ assets, products, and services. Most importantly, you are merging people, personalities, and organizational cultures. Additionally, if you consider a global merger, you are integrating with other nation’s cultures and languages which adds even more complexity. Organizational culture can positively or negatively influence many aspects of a company including sales, profits, employee engagement, collaboration, and even recruiting activities. A unique and fun corporate culture can be considered among the most important assets for certain organizations. However, a M&A could change that overnight. 

Although a company’s culture is especially shaped by its founder and executives, it is highly influenced by employees’ experiences and daily work practices. From a KM perspective, people management practices from both organizations need to be analyzed and validated to determine compatibility, ideally before moving forward with the merger. The lack of compatibility in corporate cultures and people management practices increases the likelihood of a failed M&A transaction and poses a serious risk to the expected synergy among employees from both organizations to effectively work together to create, and share knowledge under the newly merged organization. Furthermore, this could impact the definition of measurable success criteria that make sense to everyone in the new organization to track productivity and progress with KM initiatives. The changes introduced by the COVID-19 pandemic have also posed new questions for organizations as leaders reflect on how to sustain culture and best manage their people while they are working remotely. A KM Strategy can help overcome many of these challenges by helping evaluate, among other things, whether the organizations involved in the transaction encourage innovation, knowledge sharing, collaboration, and openness to change, and whether measurable success criteria exist to demonstrate the value of KM initiatives.  Conducting a cultural assessment of both organizations before moving forward with the M&A is a key component of the KM Strategy to determine whether the merger of people, personalities, and organizational cultures would work. 

Technology

One of the biggest challenges during a M&A is dealing with the different systems and technologies from both organizations and not having a clear plan on which ones to use or how to consolidate them. Often, after a M&A, the merged organization ends up with a lot of different technologies, including “shadow IT” (technologies and applications introduced to the organization without explicit approval from an IT department) that makes it difficult for employees to work efficiently, and for the IT department to support them effectively. The use of “shadow IT” has increased exponentially in recent years, especially due to the availability of free or low cost cloud-based applications and services. After a M&A transaction is completed, the sudden appearance of siloed applications, or duplicated, overlapping technologies as a result of shadow IT from each organization makes managing content effectively and achieving enterprise search a challenge. Furthermore, by definition, technologies that are considered shadow IT are not standardized or accessible across the organization, compromising access to content and potentially impacting the completion of daily job functions successfully. The COVID-19 pandemic has also introduced a new technology challenge due to the manner in which M&A are now being developed and negotiated. Nowadays, executives and other key personnel – buyers, sellers, providers of M&A financing, and legal advisors – involved in M&A transactions are working remotely, increasing the need to identify real-time collaboration technologies that allow them to interact with each other in a time-efficient manner. 

Creating an inventory of technologies, systems and applications used by the two companies as part of a KM Strategy will get you started on the right path. Before making decisions about which systems to maintain and which ones to consolidate, you need to know what you have in-house. Next steps include determining the technology needs for the merged organization and conducting technology assessments. These assessments will help you make better, people-focused decisions when it comes to KM technology as you determine the need for office productivity systems, document management systems, content management systems, process automation applications, collaboration tools, taxonomy and ontology tools, search engines, and Artificial Intelligence (AI) solutions, among others. Finally, training end users and determining the level of support that your IT department can provide will help achieve the desired level of systems adoption. 

At EK, we believe that even though choosing the right technology is an important KM element in a M&A transaction, the other four elements (People, Process, Content, and Culture) should be the driving forces of the transaction. If those four elements are aligned correctly, choosing the technology that will support them should be the final step that optimizes operations and how people work together.

Conclusion

Despite the effort that organizations go through to achieve their goal of performance improvement through a M&A, the results are often disappointing and differ from the results originally predicted. Knowing that one of the biggest problems that companies encounter during a M&A transaction is the lack of planning around integration at various organizational levels, incorporating a KM Strategy early in the process is a smart way towards a smooth and successful M&A transaction.

If you need help designing a KM Strategy for your M&A, contact us.

The post 5 Reasons Why Mergers and Acquisitions Need KM appeared first on Enterprise Knowledge.