The Retirement Cliff and Its Implications

The “retirement cliff” refers to the impending wave of retirements as a significant portion of the workforce—particularly Baby Boomers—reaches retirement age. According to labor market trends, millions of experienced professionals are set to retire in the coming years, posing a critical challenge for organizations. The departure of seasoned employees risks the loss of institutional knowledge, technical expertise, and key relationships, leading to operational disruptions and costly efforts to regain lost expertise.

One of the most immediate financial consequences Enterprise Knowledge has seen on several of our engagements is the growing reliance on retirees returning as contractors to fill knowledge and capability gaps, often at significantly higher costs than their original salaries. While this can provide a short-term fix, it also creates a long-term liability. Research from Harvard Business Review and other labor market analyses shows that rehiring former employees without structured knowledge transfer can perpetuate a cycle of dependency, inflate workforce costs, and suppress the development of internal talent. Organizations may pay premium contract rates while still losing institutional knowledge over time, especially if critical expertise remains undocumented or siloed. Without proactive strategies, such as structured succession planning, mentoring, and systematic knowledge capture, organizations risk operational disruption, weakened continuity, and increased turnover-related costs that can amount to billions of dollars annually.

The Role of Knowledge Management in Succession Planning

Knowledge management plays a vital role in succession planning by implementing systems and practices that ensure critical expertise is systematically captured and transferred across generations of employees. Documenting key insights, best practices, and institutional knowledge is essential for mitigating the risk of knowledge loss. This process helps to strengthen organizational continuity and ensures that employees have the knowledge they need to perform their roles effectively and make informed decisions.

The Retirement Cliff: Challenges & Solutions

While the table highlights immediate challenges and corresponding solutions, organizations benefit from a deeper set of strategies that address both near-term risks and long-term sustainability. The following sections expand on these themes, outlining actionable approaches that help organizations capture critical knowledge today, while laying the foundation for resilient succession planning tomorrow.

Near-term Strategies: Mitigating Immediate Risk

Engage Employees in Knowledge Capture Efforts

Long-tenured employees approaching retirement have accumulated invaluable institutional knowledge, and their sustained tenure itself demonstrates their consistent value to the organization. When a retirement cliff is looming, organizations should take action to engage those employees in efforts that help to capture and transfer key institutional knowledge before it is lost.

Cast a Wide, Inclusive Net

Organizations often lack visibility into actual retirement timelines. Rather than making assumptions about who might retire or inadvertently pressuring employees to reveal their plans, frame knowledge transfer efforts as part of comprehensive KM practices. By positioning these initiatives as valuable for all long-tenured employees—not just potential retirees—organizations create an inclusive environment that captures critical knowledge. This broader approach not only prepares for potential retirement-related knowledge gaps but also establishes ongoing knowledge transfer as a standard organizational practice.

Acknowledge and Thank Employees

Explicitly acknowledge the expertise and contributions of key knowledge holders participating in efforts. By recognizing their professional legacy and expressing the organization’s desire to preserve and share their wisdom with others, leaders can create a foundation for meaningful participation in knowledge transfer activities. This approach validates key members’ career impact while positioning them as mentors and knowledge stewards for the next generation. Consider setting aside some time from their normal responsibilities to encourage participation.

Reward Knowledge Sharing

Employees are far more likely to engage in knowledge transfer when it is seen as both valuable and valued. In EK’s experience, organizations that successfully foster a culture of knowledge sharing often embed these behaviors into their core talent practices, such as performance evaluations and internal recognition programs. For example, EK has helped to incorporate KM contributions into annual review processes or introduce peer-nominated awards like “Knowledge Champion” to highlight and celebrate individuals who model strong knowledge-sharing behaviors.

Enable Employees to Capture Knowledge

Effective knowledge transfer begins with capturing critical institutional knowledge. This includes both explicit knowledge, such as processes and workflows, and tacit knowledge, such as decision-making frameworks, strategic insights, and the rationale behind past choices. To guide organizations in successful knowledge capture and transfer practices, EK recommends implementing a variety of strategies that help build confidence and make the process manageable.

Provide Documentation Training and Support

Organizations should consider offering dedicated support through roles and teams that naturally align with KM efforts, such as technical documentation, organizational learning and development, or quality assurance. These groups can help introduce employees to the practice and facilitate more effective capture. For example, many organizations focus solely on documenting step-by-step processes, overlooking the tacit knowledge that explains the “why” behind key decisions to provide future employees with critical context. In EK’s experience, preserving and transmitting knowledge of past actions and opinions has given teams the confidence to make more informed decisions and ensure coherence in guidance. This approach is especially valuable from a legal perspective, where understanding the rationale behind decisions is crucial for consistency and compliance.

Help Prioritize the Knowledge to Capture

Organizations can help focus knowledge capture efforts, without overwhelming employees, by prioritizing the types of knowledge to capture. If knowledge falls into one of these categories, it is ideal to prioritize:

1. 1. Mission-Critical Knowledge – High-impact expertise that is not widely known (e.g., decision-making rationales, specialized processes) is at greatest risk for loss. Encourage employees to prioritize this knowledge first.

1. 1. Operational Knowledge – Day-to-day processes that can be captured progressively over time. Suggest to employees that they take advantage of workflows and cycles as they are completed to document knowledge in real time from beginning to end.

1. 1. Contextual Knowledge – Broader insights from specific projects and initiatives are best captured in collective discussions or team reflections from various participants. Aim to make arrangements to put team members in conversation with one another and capture insights.

Embed Knowledge Capture into Workflows

Rather than treating documentation as a separate task, organizations should embed it into the existing processes and workflows where the knowledge is already being used. Integrating documentation creation and review into regular processes helps normalize knowledge capture as a routine part of work. In practice, this may look like employees updating Standard Operating Procedures (SOPs) during routine tasks, recording leadership reflections during key decisions, or incorporating “lessons learned” or retrospective activities into project cycles. Additionally, structured after-action reviews and reflective learning exercises can further strengthen this practice by documenting key takeaways from major projects and initiatives. Beyond improving project and knowledge transfer outcomes, these habits also build durable knowledge assets that support AI-readiness.

Design Succession-Focused Knowledge Sharing Programs

Cultural silos and resistance to sharing knowledge often undermine succession planning. Employees may hesitate to share what they know due to fears about losing job security, feeling undervalued, or simply lacking the time to do so. To overcome these challenges, organizations must implement intentional knowledge transfer programs, as outlined below, that aim to prevent a forthcoming retirement cliff from leaving large gaps.

Create Knowledge Transfer Interview Programs

Pairing long-tenured staff with successors ensures that critical institutional knowledge is passed on before key departures. Create thoughtful interview programming that takes the burden off the experienced staff from initiating or handling administrative efforts. EK recently partnered with a global automotive manufacturing company to design and facilitate structured knowledge capture and transfer plans for high-risk roles that were eligible for retirement, including walkthroughs of core responsibilities, stakeholder maps, decision-making criteria, and context around ongoing initiatives. These sessions were tracked and archived, enabling smoother transitions and reducing institutional memory loss. EK also supported a federal agency in implementing a leadership knowledge transfer interview series with retiring senior leaders to capture institutional knowledge and critical insights from their tenure. These conversations focused on navigating the agency’s operations, lessons for successors, and role-specific takeaways. EK distilled these into concise, topical summaries that were tagged for findability and reuse, laying the foundation for a repeatable, agency-wide approach to preserving institutional knowledge.

Foster Communities of Practice

Encourage cross-functional collaboration and socialize knowledge sharing across the organization by establishing communities of practice.  The programs provide opportunities for employees to gather regularly and discuss a common professional interest, to learn from each other through sharing ideas, experiences, and best practices. Involve long-tenured staff in these efforts and encourage them to develop topics around their expertise. EK has seen firsthand how these practices promote ongoing knowledge exchange, helping employees stay connected and informed across departments, even during leadership transitions.

Offer Formal Knowledge Exchange Programs

Knowledge Exchange Programs, like job shadowing, expert-led cohorts, and mentorship initiatives, create clear pathways for employees to share and document expertise before transitions occur. Long-tenured employees are often excellent candidates to take the leading role in these efforts because of the vast knowledge they hold.

Ultimately, effective succession planning is not just about capturing what people know—it is about creating a culture where knowledge transfer is expected, supported, and celebrated. By addressing resistance and embedding knowledge-sharing into the rhythm of daily work, organizations can reduce risk, improve continuity, and build long-term resilience.

Long-term Strategies: Building Sustainable Knowledge Flow

While short-term efforts can help reduce immediate risk, organizations also need long-term strategies that embed knowledge management into daily operations and ensure continuity across future workforce transitions. That is why EK believes Artificial Intelligence (AI) and Knowledge Intelligence (KI) are essential tools in capturing, contextualizing, and preserving knowledge in a way that supports sustainable transitions and continuity.

Below are long-term, technology-enabled strategies that organizations can adopt to complement near-term efforts and future-proof institutional knowledge.

Structure and Contextualize Knowledge with a Semantic Foundation

EK sees contextual understanding as central to KM and succession planning, as adding business context to knowledge helps to illuminate and interpret meaning for users. By breaking down content into dynamic, structured components and enriching it with semantic metadata, organizations can preserve not only the knowledge itself, but also the meaning, rationale, and relationships behind it. EK has supported clients in building semantic layers and structured knowledge models that tag and categorize lessons learned, decisions made, and guidance provided, enabling content to be reused, assembled, and delivered at the point of need. This approach helps ensure continuity through leadership transitions, reduces duplication of effort, and allows institutional knowledge to evolve without losing its foundational context.

Leverage Knowledge Graphs and Intelligent Portals

Traditional knowledge repositories, while well-intentioned, often become static libraries that users struggle to navigate. EK has helped organizations move from these repositories to dynamic knowledge ecosystems by implementing knowledge graphs and a semantic layer. These approaches connect once disparate data, creating relationships between concepts, decisions, and people.

To leverage the power of the knowledge graph and semantic layer, EK has designed and deployed knowledge portals for several clients, providing a means for users to engage with the semantic layer. These portals consolidate information from multiple existing systems into a streamlined, user-friendly landing page. Each portal is designed to serve as a central hub for enterprise knowledge, connecting users to the right information, experts, and insights they need to do their jobs, while also supporting smoother transitions when staff move on or new team members step in. With intuitive navigation and contextualized search, the portal helps staff quickly find complete, relevant answers across multiple systems, explore related content, and access expertise—all within a single experience.

Augment Search and Discovery with Artificial Intelligence

To reduce the friction of finding and applying knowledge, EK has helped clients enhance knowledge portals with AI capabilities, integrating features like context-aware search, intelligent recommendations, and predictive content delivery.  These features anticipate user intent, guide employees to relevant insights, and surface related content that might otherwise be missed. When paired with a strong semantic foundation, these enhancements transform a portal from a basic search tool into an intelligent instrument that supports real-time learning, decision-making, and collaboration across the enterprise.

Automate and Scale Tagging with AI-Assisted Curation

Manual tagging is often cited as one of the more time-consuming and inconsistent aspects of content management. To improve both the speed and quality of metadata, EK has helped clients implement AI-assisted tagging solutions that automatically classify content based on a shared taxonomy.

We recommend a human-in-the-loop model, where AI performs the initial tagging, and subject matter experts validate results to preserve nuance and apply expertise. This approach allows organizations to scale content organization efforts while maintaining accuracy and alignment.

For example, we partnered with a leading development bank to build an AI-powered knowledge platform that processed data from eight enterprise systems. Using a multilingual taxonomy of over 4,000 terms, the platform automatically tagged content and proactively delivered contextual content recommendations across the enterprise. The solution dramatically improved enterprise search, reduced time spent locating information, and earned recognition from leadership as one of the organization’s most impactful knowledge initiatives.

Integrate Technology, People, and Process Within Succession Planning

The most successful organizations do not treat knowledge technologies as standalone tools. Instead, they integrate them into broader KM and succession planning strategies, ensuring these solutions support, rather than replace, human collaboration and expertise.

In EK’s experience, when AI, knowledge graphs, and semantic metadata are used to enhance existing processes—like onboarding, leadership transitions, or project handovers—they become powerful enablers of continuity. These tools help protect institutional knowledge, reduce bottlenecks, and enable repeatable practices for knowledge transfer across roles, teams, and time.

As part of a long-term KM strategy, this allows organizations to evolve from reactive knowledge capture to proactive, ongoing knowledge flow.

Measuring Knowledge Transfer Impact

As we have provided the tools and advice for ensuring impactful knowledge captures and transfers, measuring the effectiveness of knowledge transfer initiatives is the essential next step to ensure that succession planning goals are being met and that knowledge transfer efforts are producing meaningful outcomes. Key performance indicators (KPIs) and metrics can help track the success of these initiatives and provide insights into their impact on the organization’s leadership pipeline.

Closing

Navigating the retirement cliff requires both immediate action and long-term planning. By addressing resistance, dismantling silos, embedding knowledge-sharing into daily work, and leveraging technology, organizations can reduce risk, preserve critical expertise, and build long-term resilience. Need help developing a strategy that supports both near-term needs and long-term success? Let’s connect to explore tailored solutions for your organization.

The post Navigating the Retirement Cliff: Challenges and Strategies for Knowledge Capture and Succession Planning appeared first on Enterprise Knowledge.

Organizations can prevent the unnecessary loss of essential knowledge by having structured conversations with key personnel before they leave their organization. This is one example of a tacit knowledge transfer activity, capturing knowledge that resides in people’s heads. These conversations, also known as knowledge transfer interviews, can enable smoother transitions between leaders, ensure continuity in organizational performance, and reduce the risk of repeating past mistakes or missteps. 

Capturing knowledge is the first step in making the most out of tacit/institutional knowledge activities. The next step is taking that knowledge and making it findable, reusable, and machine-readable with semantics to add context and/or content structure for ease of application to add real value to an organization. Retaining knowledge in a findable, action-oriented, and/or AI-ready format will transform knowledge transfer interview outcomes from interesting tidbits into valuable, distributable knowledge assets for companies to benefit from. 

In the following sections, this blog will break down the steps to conducting successful knowledge transfer interviews so that anyone can employ this technique at their organization and retain critical knowledge from experienced personnel. It will conclude with a discussion about how to enhance the knowledge gathered so it can be applied in the future. In all, conducting knowledge transfer interviews and subsequently transforming interview outcomes into a machine-readable, reusable format is a crucial strategy organizations should seek to employ. 

Preparing For The Interview: Identifying Critical Knowledge for Retention

1. Define and Prioritize Outcomes

To prepare for the interview, determine clear outcomes to obtain from the conversations with the interviewee, such as ensuring continuity in leadership or mitigating risks associated with leadership turnover. Reflect on what this knowledge capture will enable or who it will benefit, and prioritize outcomes accordingly (i.e., “Given this interviewee’s position, these are the top 3 pieces of information to walk away from this conversation with”). A clear outcome will ensure that interview sessions are efficient, focused, and targeted. While determining interview outcomes, plan for how and where interview takeaways (“aha” moments, lessons learned, preventable mistakes) will be captured so the knowledge gathered can be leveraged by the organization and others who can benefit.

2. Set Up an Interview Schedule

In most cases, multiple interviews will be necessary to achieve all desired interview outcomes. A particular topic might spark anecdotes or branch off into different topics. These segways can lead to the interviewee sharing unexpected but relevant and critical takeaways (it is amazing what stories will surface given the right amount of time!) that might not otherwise surface in a one-time session. Having multiple meetings helps account for the unexpected. Similarly, allotting enough time per session can be the difference between an uncomfortable interviewee and someone who is ready to open up. Plan multiple sessions for no less than 45 minutes and no longer than 60 minutes each, giving the interviewee enough time to get comfortable with the format and to start digging deeper into their experiences and expertise. If possible, consider recording the interview sessions to ensure the knowledge shared is accurately captured. Be sure to ask for the consent of your interviewee before recording.

3. Prepare A Guide For the Interviewee

An interview guide is a document that outlines the major topic areas–not the questions themselves–that the interview session will cover. Create an interview guide as part of the interview invitation to allow participants to think through the chosen topics and organize their thoughts ahead of the interview. A prepared interviewee can get to the most important nuggets of their knowledge more readily, making the most out of the limited time together. In addition to being a valuable resource for the interviewee, the practice of creating the guide will aid the interviewer in developing focused, on-topic interview questions.

4. Develop the Interview Questions

It is important to align interview questions with prioritized interview outcomes to direct the conversation and ensure all topics are covered. The aforementioned interview guide will aid in the development of focused questions. Even with interview outcomes as the underlying logic for question creation, developed questions should not be viewed as a strict script for the interviewer to follow. Instead, use the interview questions and outcomes as a guide, leaving room for adjustments and the ability to be flexible as the conversation flows. 

To get started developing interview questions, consider the following helpful categories.

• Contextual Background – Consider the interviewee’s current role and associated responsibilities. Seek to understand the context for their transition out of their current role. This background information will help set the stage for lessons learned and takeaways for future leaders in their role. 
  
• Knowledge Specific to their Role – Determine the expectations for their position. Ask about key mission successes and what factors could contribute to the success of their successor. Find out about the surprises the interviewee faced in their role or expectations about their role, frustrations they dealt with, pressing challenges, and how they overcame or addressed them. These strategies could directly apply to a successor and prevent repeated missteps or mistakes. Consider asking questions about how the organization could have made fuller use of the interviewee’s capabilities and expertise. Explore the culture of the organization and the ways it might affect how the role is executed (internal politics, etc.).
  
• Task-Specific Information – Focus the interviewee on describing a specific, demanding task. Have them break down the steps of the task and address factors such as complexity, time, criticality, and knowledge needed to execute successfully. Honing in on one activity can assist the interviewee in digging deeper into their time in the role, rather than providing generalized, high-level descriptions or takeaways.
  
• Summary and Wrap-Up – Wrap up the interview by inquiring about things the interviewee wished they had known before starting the job, and any advice they would offer to a future team. The end of the interview also provides a great opportunity to reflect on the interview thus far, potentially prompting insights that the interviewee had not initially surfaced. Ask the interviewee to summarize the three most important things about the role and anything else the interview may not have covered.
  

Conducting The Interview: Capturing High-Value Knowledge For Future Use

Once you have prepared for the interviews (set your interview intentions, created and passed along an interview guide to your interviewee, and developed interview questions), it is time to conduct the interview. When carrying out the interview, keep in mind the following advice:

1. Step into the interview with an open mind, leaving bias and opinions at the door.

2. Build trust by establishing confidentiality. At a later stage, key messages will be identified and sent back to the interviewee for their agreement to publish.

3. Strike a balance between free-ranging conversation and digging into real stories by looking for specific answers.

4. Be alert to the focus of the interviewee’s energy, focus, and interests, following their lead to areas of interest or concern.

5. Develop the interviewee’s train of thought by asking follow-up questions.

6. Ensure the focus is on the interviewee by refraining from telling stories or drawing conclusions based on what was said.

7. Request any artifacts mentioned in the interview and plan to follow up on obtaining them.

Try to hit on the topics that will be most valuable to others, using interview questions as a guide, rather than a strict script to stick to.

After The Interview: Codifying and Distributing the Knowledge

Knowledge transfer is not complete until the knowledge is made accessible to others. Once the interview sessions have concluded, review what has been said, send copies of potentially useful quotes to the interviewee for approval,  and look for key learnings to include in a final knowledge asset. A knowledge asset is a shared resource within an organization that captures and codifies insights, lessons learned, know-how, guidance, and other useful knowledge to enable staff to better conduct their work and make informed decisions. A great first step for creating the final knowledge asset would be compiling written documentation divided into sections based on subject matter or topic, with key takeaways or interview quotes. 
To make the knowledge asset even more meaningful and reusable, consider taking steps to prepare the knowledge asset for future applications (such as an input for AI or large language models). Here are some other content-related tips to get the most out of a final knowledge asset:

1. Utilize a centralized authoring platform to manage content in one place, leverage content types to standardize the final knowledge asset, and break content into semantically meaningful sections so they can stand on their own apart from the asset as a whole. 

2. Apply metadata tagging or a dynamic content model as part of a semantic layer, for example, to structure and semantically enrich the knowledge asset. 

3. Beware, even the prettiest reports get lost in people’s inboxes! Make a plan for circulating the final knowledge asset to those who will benefit from it and store it in an accessible, searchable, and centralized location where future knowledge transfer interview outcomes can also live.

Knowledge transfer interviews can also prompt additional actions like updates to Standard Operating Procedures (SOPs) and policies. These actions can embed valuable knowledge into the organization’s day-to-day business–a significant knowledge management accomplishment. 

In addition to the creation of a knowledge asset, potential takeaways resulting from transition interview distillation and analysis can fall into two categories: (1) what was effective/what could be changed, and (2) strategic improvement opportunities. 

Closing

Knowledge transfer interviews with departing senior leadership can be a highly effective element of succession planning. The knowledge of the transitioning team member has immense value, which is especially relevant in roles where the team member has accumulated a significant amount of knowledge and personal connections. This practical guide can be a starting place for planning interview sessions, rather than waiting until it is too late to capture these invaluable insights. Adding in content and semantic strategies to prepare interview takeaways for AI can be the difference between knowledge simply captured and knowledge utilized and leveraged to benefit the organization, individuals, or business functions in the future. Effective knowledge capture and transfer results in knowledge that is findable, reusable, and AI-ready.  

Want to learn more about how EK can support knowledge capture and transfer efforts and transform your knowledge assets to be AI-ready? Contact us!

The post A Practical Guide To Knowledge Transfer Interviews appeared first on Enterprise Knowledge.

This infographic showcases practical strategies we’ve implemented to foster knowledge sharing, ensuring critical expertise is captured, collaboration flourishes, and teams are equipped for long-term success.

Organizations can improve collaboration, streamline workflows, and strengthen problem-solving by creating an environment that supports and rewards knowledge sharing. Investing in knowledge sharing today ensures a smarter, more connected workforce ready to tackle future challenges. EK can help your organization improve collaboration and knowledge sharing, contact us to learn more.

The post Fostering a Knowledge-Sharing Mindset: How to Get People to Share What They Know appeared first on Enterprise Knowledge.

In this presentation, DeMay and Anna explored the World Wildlife Fund’s (WWF) approach to developing its knowledge management strategy from the ground up. They focused on the organization’s initial challenges, such as disparate systems and siloed information. They highlighted WWF’s strategy for overcoming these obstacles, emphasizing the integration of people, processes, and technology to craft a roadmap aligned with WWF’s organizational goals.

They discussed WWF’s proactive efforts to foster a knowledge-sharing culture, define clear roles, and implement a governance structure that enhances content management across a distributed team of over 1,900 employees. They also addressed the vital role of change management, sharing techniques for navigating resistance and securing buy-in through executive sponsorship and grassroots advocacy.

Participants in this session gained insights into:

• Key challenges and strategies for building a KM program from scratch;
• The importance of aligning KM initiatives with organizational goals;
• Techniques for fostering a knowledge-sharing culture and managing content; and
• How to drive sustainable change with effective communication, training, and support.

The post Nurturing Knowledge – A Journey in Building a KM Program from Scratch: A Case Study appeared first on Enterprise Knowledge.

• Review what KM ‘is’ and ‘isn’t’
• Understand the value of KM and the benefits of engaging 
• Define and reflect on your “what’s in it for me?”  
• Share actionable ways you can participate in Knowledge Capture & Transfer 

Upon review of a comprehensive list of Knowledge Capture & Transfer techniques, Taylor noted that the common denominator is the act of storytelling and listening. In addition to providing a definition and best practices, she outlines the benefits: 

• Shares the organizational knowledge, wisdom, and insight often missed during more formalized knowledge sharing processes
• Offers opportunity for real-time dialogue (Q&A) 
• May be facilitated or occur organically
• Nurtures existing and budding expertise 
• Builds trust and interconnectivity between participants

Participants engaged in a live poll to determine the frequency in which they currently engage in telling and listening to stories in the workplace. Taylor facilitated a healthy dialogue around the importance of frequency, structure, span, and the individual outcomes for participating in knowledge capture and transfer techniques, even if it’s through the simple act of storytelling. 

The presentation concluded with 15 minutes for participant questions and the shared sentiment to “Tell Your Stories” and “Learn from Each Other.”

The post Presentation: Demystifying Knowledge Management through Storytelling appeared first on Enterprise Knowledge.

This first session was moderated by Lulit Tesfaye, EK’s very own Partner and Vice President of Knowledge & Data Services, whose primary focus is on employing practical AI and semantic capabilities for optimizing organizational knowledge, data, and information assets. Our three panelists were Polly Alexander, Director of Metadata and Taxonomy for WebMD Ignite, with expertise bridging the fields of Knowledge Management, AI, and Machine Learning; Malcolm Hawker, a former Chief Product Officer and Gartner analyst with over 25 years of experience across the fields of Data Strategy, Master Data Management (MDM), and Data Governance; and Mohammed Aaser, Chief Data Officer (CDO) of Domo and former CDO of McKinsey and Company.

Webinar Summary—What Is a Semantic Layer?

To set some context for this blog (which the truth is bound by, as you’ll read in a few minutes), a Semantic Layer is a standardized framework; it’s not one technology and it’s not an all-powerful product that you can click and buy. Rather, a true Semantic Layer is a combination of solutions that help organize and connect your organization’s knowledge and information, both structured and unstructured. It does so by shifting the focus from only physical data to descriptive metadata, allowing an organization to aggregate content from multiple sources without the overwhelming step of migrating all your data to one central location. As another powerful benefit, a Semantic Layer allows you to provide structured context to any type of data by leveraging schemas like taxonomies, ontologies, and knowledge graphs, ultimately enabling flexibility and interoperability between systems – the end goal on many organizations’ roadmaps.

The panelists discussed various topics revolving around the real-world applications of the Semantic Layer within their respective industries and organizations. In this article, we present the 6 primary themes that our experts discussed, showcasing the common value that Semantic Layers bring, no matter the experience or industry.

Overarching Themes / Key Findings & Takeaways

Enabling Truth to Be Bound by Context

The Semantic Layer bridges the gap between data management and true knowledge management by adding and connecting information with context. 

“We’ve always known that truth is bound by context.” – Malcom Hawker

What is true to a CEO may look completely different from what is true to a new business analyst, though both versions of that truth may, and usually are, present and valid within an organization. The goal of knowledge management is to allow the right kind of truth to be delivered to the right person at the right time they need it. Semantic Layers enable multiple versions of true information to exist at the same time, building context around who will need that information and in what form. For example, that CEO and that new business analyst may need different types and formats of information from the same data set; the CEO requires a high-level summary to make a strategic decision, while the analyst requires a more granular view to write a longer, more detailed report.

Many relationships, both within an organization and to the audiences they serve, depend on the trust that comes when people are delivered correct, meaningful information that helps them make an informed decision. These relationships range from medical professionals and the patients they advise, to product-based companies and the customers they advertise to, to business executives and the staff that they employ. The trust that comes with context is invaluable, as don’t we all just want to feel understood?

Understanding Specific Domains & Defined Business Entities

Another major benefit of the Semantic Layer is that it allows an organization’s technology solutions to truly represent the organization’s domain and specific business lines. In some cases, this can literally mean life or death when it comes to predicting the type of knowledge that people will need next. Polly provided some great anecdotes from a healthcare perspective, aptly noting that, with the rise of LLMs, people have come to expect immediate and easily accessible answers to their questions. As patients are seeking quick and personalized information about their medical journey, symptoms, treatment plans, etc., it’s imperative that content recommendations be reliable and generated from accurate and governed data. The power of a knowledge graph, and in turn, the Semantic Layer, is the idea of targeted recommendations that indirectly advises and supports users on what the best action is, relevant to the domain they’re in and the concepts they’re researching. It’s really exciting that decades of research and information can be operationalized in a new way to deliver end users with information before they knew they needed it, but it must be verifiable and based in truth, in case you don’t have the luxury of getting it wrong.

Approaching Technical Possibilities from the Layman’s Perspective

While the excitement around semantic technologies is palpable (at least in our field), it can be difficult to garner the right level of buy-in and understanding from business stakeholders and end users, as the Semantic Layer involves some highly technical concepts. The panelists recognized this challenge and discussed different ways to start these conversations and make the Semantic Layer more real. They discussed comparing the Semantic Layer to a digital twin (say, a virtual model of a jet plane before it is even built) or connective tissue (that super important stuff inside humans that provides cohesion and internal support). 

“A Semantic Layer is much like connective tissue.” – Polly Alexander

While the Semantic Layer can be daunting to bring to the discussion table, leveraging familiar metaphors and technologies can help make this highly technical concept much more palatable for business buy-in. The rise of LLMs and other forms of GenAI makes now the perfect time to start these conversations, as most people are familiar with those concepts. The message is simple: people want fast answers to their questions, and they want to be confident those answers are correct. But, without the right underpinning, you risk exposing the wrong information and data. Most companies will not be building and training their own LLMs, but they will be utilizing intelligent agents already available in the everyday applications they use, such as chatbots and Copilot, chasing the promise of hyperproductivity. 

Within any industry, there are well-known and defined facts, providing a great starting point for content that can be ingested, modeled, and delivered. It’s not necessary for an organization to leverage an LLM to look through other types of data or an entire corpus of information, as we know from our research that 70-80% of any organization’s data is incorrect, outdated, or duplicate. The compelling driver for the Semantic Layer is to provide LLMs with facts and entities that are already true and defined within a particular domain, rather than letting them come to their own conclusions. The real power of a LLM, or any customized semantic solution, is that it has the ability to make structured information humanized and understandable, a goal that will resonate with most stakeholders, no matter their background or expertise.

Navigating the Balance Between Exciting Technologies and Real-World Use Cases

Any conversations around these exciting technologies should be balanced with discussions of well-defined, real-world use cases. A use case, or a user’s story to tell, will resonate with any audience. The panelists discussed two schools of thought here: you can either focus heavily on the user story and avoid any technical concepts in initial conversations, or you can focus on the specific business value to paint a larger picture (the timing, what your organization wants to go after, and the rationale behind investing in semantic technologies). Both approaches are valid but should be considered for the particular audience.

It’s also important to conduct the aforementioned conversations with a skeptical mind. The Semantic Layer holds immense potential, but it’s not right for every organization. For organizations with data products that involve multiple complex data sources to be integrated, a Semantic Layer is invaluable and will warrant the investment costs. But for smaller organizations, the Semantic Layer may simply be a consideration to learn more about for future growth and scale. This skepticism can save a lot of time and money, setting expectations early that the right approach to a Semantic Layer is starting small, building on 1-2 integrations and a common data model, and staying grounded in a prioritized use case to more easily prove out and measure value.

Making Data Teams Clickable

A major challenge within many organizations is the fact that data and analytics teams are often siloed or disconnected from the rest of the business, and furthermore, data professionals may only make up 1-2% of a workforce. They hold the data and technical knowledge, but do they really understand the business and possess the ability to advise the rest of the organization? The panelists explored the idea that in order for an organization to make the jump from data to knowledge, in order for it to start addressing the prioritized use cases I mentioned above, it’s imperative that data teams truly understand the use case they are looking to solve and are viewed as strategic business consultants that can guide the organization on their Semantic Layer goals. 

“I think data and analytics teams need to move closer to the business and become strategic business consultants. The way we do that is through this focus on knowledge.” – Malcom Hawker

 Malcolm and Mohammed joked that it may be as simple as a name change that leads to a mindset shift: instead of Chief Data Officers, perhaps they should rebrand themselves to Chief Knowledge Officers or Chief Wisdom Officers, positioning themselves as thought leaders and partners by focusing on the types of knowledge that will bring the most value.

“We could rebrand ourselves as Chief Wisdom Officers.” – Mohammed Aaser

By making data teams ‘clickable’, that is, building a team who is attractive as a source of technical expertise as well as valuable business insights, it’s much easier to have fruitful conversations about how to leverage semantic technologies and when to do so. The Semantic Layer path can be fraught with peril, and it requires collaboration and mutual understanding to start down that path.

Baking the Four-Layer Cake

I give full credit to Malcolm for this analogy, but I think it’s helpful to end off this blog with a bigger, more digestible picture. He describes the Semantic Layer as a four-layer cake, composed of an Integration Layer, a Governance Layer, a Data Analysis Layer, and topped with a Recommendation Layer. 

“I see this as a 4 layer cake.” – Malcom Hawker 

The Integration Layer provides the foundation, the data pipelines and APIs that help connect disparate sources. The Governance Layer encompasses the guidelines and processes that help maintain and refine knowledge and data over time. The Data Analysis Layer includes developing, running, and tuning models to make sure that accurate conclusions are drawn from the data. Finally, the Recommendation Layer helps users understand how different pieces of information are connected by recommending them related content based on their personal attributes, location, demographic profile, search history, etc. 

Many organizations we’ve worked with have already taken the first step of building the base, the Integration Layer. It’s important to have that foundation and stack the additional layers on top, with proper time and consideration given to each. I’d like to call particular attention to the Governance Layer, as we’ve seen too many organizations overlook the “AI Governance Elephant in the Room”—look out for Malcolm’s upcoming article with this title—meaning that the same careful attention that is paid to structured data assets is not applied to the rest of an organization’s content, such as FAQs that marketing creates, bereavement policies, or employee handbooks. 

“The AI Governance Elephant in the Room” – Malcom Hawker

A properly baked Semantic Layer provides consideration and guardrails to this type of content, especially for organizations that are already using some form of GenAI, protecting users from receiving imprecise, outdated, or downright wrong information. In most cases, the allure of semantic technologies is rooted in text data; the Semantic Layer is the bridge that transforms structured data that is sitting in rows and columns into text data that can be easily read and understood. The combination of LLMs and the Semantic Layer is truly revolutionary, and it’s reigniting this field and conversations just like this webinar. I think Lulit said it best during the webinar: 

“If you are able to understand and map your data and encode the facts, then you can ask the questions of tomorrow.” – Lulit Tesfaye

Closing

By reshaping the way that we think about and interact with our knowledge and data, and by considering the individual roles within an organization that can benefit from this connectivity, the possibilities are truly endless. If you found this blog interesting or insightful, I encourage you to go listen to the recording of the full webinar or check out the rest of our Knowledge Base for many more resources on the Semantic Layer. If you are looking for help getting started on harnessing the power of Semantic Layers or discussing the value that these solutions can bring, contact us today!

The post Industry Panel: Different Applications of a Semantic Layer — Takeaways Blog appeared first on Enterprise Knowledge.

Kjerish, CC BY-SA 4.0, via Wikimedia Commons

Data proliferates. Whether you are a small team or a multinational enterprise, information grows at an accelerated rate over time. As that data proliferates, you can run into issues of interoperability, duplication, and inconsistency that slows down the speed with which actionable insights can be derived. In order to mitigate this natural tendency, we develop and enforce standardized data models. 

Developing enterprise data models introduces new concerns, such as how centralized ownership of the model should be. While it can be helpful to have a singular overarching data model team, centralization can also introduce its own challenges. Chief among these is the introduction of a modeling bottleneck. If only one team can produce or approve models, that slows down the speed with which models can be developed and improved. Even if that team is incorporating feedback and review from the data experts, centralization is typically a blocker to ensuring that deep domain knowledge is captured and kept updated. It is for that reason that frameworks such as data mesh and data fabric promote domain ownership of data models by the people closest to the data within a larger federated framework.

Before continuing, we should define a few terms:

Of course, implementing domains working within a federated data model brings its own challenges for data governance. Some–such as the need for global standardization to promote interoperability across data products–are common data challenges, while others–such as the federation of governance responsibilities–may be new to organizations embarking on a decentralized model journey. This article will walk through how to begin transitioning to federated data model governance.

Similar to a town hall or local government, success will rely on ensuring that many different stakeholders have a seat at the table and a sense of shared responsibility. Dietmar Rabich / Wikimedia Commons / “Dülmen, Rathaus, Ratssaal — 2017 — 9667-73” / CC BY-SA 4.0

Moving away from a centralized model: Balancing standardization and autonomy

For organizations that have already implemented centralized data governance, the thought that governance responsibilities can or should be federated out to different domains may seem strange. Data governance grows out of the need for standardization, interoperability, and regulatory standards, all of which are typically associated with centralized management. These needs are central to any large organization’s data governance, and they don’t go away when creating a federated governance model. However, within a federated data model, these standardization needs are balanced against the principles of domain autonomy that support data innovation and agile production. Time spent explaining field naming conventions and data structure to non-experts and waiting for approval can slow or even stymie the ability to make data internally available, resulting in increased cost, lost hours, and lower innovation.

To support domain autonomy and the ability to move quickly when iterating on or creating new data products, some of the responsibility for ensuring that data meets governance standards is shifted onto the domains as part of a shared governance model. Business-wide governance concerns like security and enterprise authorization remain with central teams, while specific governance implementations like authorization rules and data quality assurance are handled on a domain basis. Domains handle the domain-level governance checks and leave the centralized governance group to tackle more central issues like regulatory compliance, meaning that the data product teams spend less time waiting on centralized governance checks when iterating on a data product. 

The federated and central governance teams are not separate entities, working without knowledge of one another. Domain teams are able to weigh in on and guide global data product governance policies, through a cross-functional governance team.

 Global governance, local implementation

Within a federated governance model, it is still important to be able to create enterprise-wide governance policies. Individual domains need guidance on meeting regulatory requirements in areas of privacy and security, among others. Additionally, for standardization to be of the greatest benefit, all of the groups producing data need to align on the same standards. 

It is for these reasons that the federated governance model relies on a cross-functional governance team for policy decisions, as well as guidance on how to implement governance to meet those policies. This cross-functional team should be made up of domain representatives and representatives from Central IT, Compliance, Standards, and other experts in relevant governance areas. This ensures that policy decisions are not removed from the data producers, and that domains have a say in governance decisions while remaining connected to your organization’s central governance bodies. Policies that should be determined by this governance team can include PII requirements, API contracts, mappings, security policies, representation requirements, and more.

An example federated governance diagram

In order to ensure that domains are fully engaged in the governance process, it is best practice to involve the data product teams early in the governance process. For an organization new to federated data models, this should happen when the data product teams are being stood up, rather than waiting for product teams to be fully formed before grafting on later. When Enterprise Knowledge spearheaded the development of an enterprise ontology for data fabric at a major multinational bank, we worked with the major stakeholders to start defining a federated governance program and initial domains from the beginning of the engagement alongside the initial ontology modeling. This helped to ensure that there was early buy-in from the teams that would later define and be responsible for data products.

The data product teams are ultimately responsible for executing the governance policies of this group, so it is vital that they are involved in defining those policies. Lack of involvement can lead to friction between the governance and data product teams, especially if the data product teams feel that the policies are out of sync with their governance needs.

Shift left on standards

The idea of “shifting left” comes from software testing, where it means to evaluate early rather than later in project lifecycles. Similarly, shifting left on standards looks to incorporate data management practices early into data lifecycles, rather than trying to tack them on at the end. Data management frameworks prioritize working with data close to its source, and data governance should be no different. Standards need to be embedded as early within the data lifecycle as possible in order to promote greater usability downstream within data products. 

For data attributes, this could mean mapping data to standardized concept definitions and business rules as defined in an ontology. EK has worked with clients to shift left on standardization by using a semantic layer to connect standardized vocabulary to source data across disparate datasets, and map the data to a shared logical model. Applying standardization within the data products improves the user experience for data consumers and lessens the time lost when working with multiple data products. 

Zhamak Dehghani, the creator of the data mesh framework, suggests looking for places where standardization and governance can be applied programmatically as part of what she refers to as “computational governance.” Depending on an organization’s technical maturity (i.e. the availability and use of technical solutions internally), governance tasks such as the anonymization of PII, access controls, retention schedules, and more can be coded as a part of the data products. This is another instance of embedding standardization within domains to promote data quality and ease of use. Early standardization lessens the amount of later coordination that is required to publish data products, resulting in faster production, and it is one of the keys to enabling a federated data model. 

Conclusion

While federated data governance will be a new paradigm to many organizations, it has clear advantages for data environments that rely on expertise across different subject areas. The best practices discussed in this article will ensure that your organization’s data ecosystem is not only a powerful tool for standardization and insights, but also a robust and reliable one. Data product thinking can be an exciting new way to gain insights from your data, but the change in paradigm required can also leave new users feeling lost and unsure. If you want to learn more about the social and technical sides of setting up federated data governance, contact us and we can discuss your organization’s needs in detail. 

The post Governing a Federated Data Model appeared first on Enterprise Knowledge.

Laurie Gray, Principal Consultant on Enterprise Knowledge’s Strategy team, and EK client Kate Vilches, Knowledge Management Lead at Ulteig, presented on November 6, 2022 at the Taxonomy Boot Camp Conference, co-located with KMWorld, in Washington, D.C. The talk, “Taxonomy Roller Coasters: Techniques to Keep Stakeholders on the Ride,” focused on proven stakeholder management techniques during enterprise taxonomy development and launch activities. 

Gray and Vilches used their firsthand experience to relate advice, share practical tools, and provide real-life examples to ensure successful stakeholder involvement, reinforcing three key themes for attendees:

• How to select partners and build coalitions to ensure long term success;
• Overview of the steps, stages, challenges, and thrills of defining and implementing an enterprise taxonomy; and
• The importance and finesse of effective change management efforts to ensure that stakeholders begin and remain excited and involved throughout the project.

Are you ready to begin taxonomy efforts at your organization, or are you in the middle of a taxonomy effort that you need assistance with? Contact Enterprise Knowledge today!

The post Taxonomy Roller Coasters: Techniques to Keep Stakeholders on the Ride appeared first on Enterprise Knowledge.

• Describe the importance of focusing on the unique culture of an organization when building and iterating on a KM program
• Recognize organizational archetypes and know how to adapt their KM program to them
• Conduct a cultural assessment of their own organization to ensure their KM program is meeting them where they are

The post Presentation: Building for the KM Archetypes at Your Company appeared first on Enterprise Knowledge.

The two-day certification course is a staple in KMI’s Certified Knowledge Specialist (CKS) offerings. The course provides a background on key concepts including Agile, Design Thinking, Change Management, and User-Centered Communications strategies, detailing how elements of each may be harnessed to address common challenges in KM efforts. This will be highly interactive, involving participants in a series of facilitated exercises, including EK’s complete “Design Thinking for KM” workshop approach.

Mary Little, EK’s Division Director of Knowledge Management Strategy & Design, will serve as the lead instructor for the course. Little is a KM expert, experienced facilitator, and frequent speaker on KM, Design Thinking, and Agile methodologies. She is a certified Project Management Professional (PMP) and Scrum Product Owner (CSPO). Little focuses on applying agile and design-thinking principles to user-centric solutions, and she is determined to create actual, positive change in the way people engage in the work they do.

When asked about the course, Zach Wahl, CEO of Enterprise Knowledge, said, “This certification course is a perfect application for how we approach KM. It will teach attendees how we put the end user at the center of the KM strategy and design effort, and leverage an assortment of facilitation techniques to engage them in every step of the process.”

Additional information and registration can be found on KMI’s website.

About Enterprise Knowledge

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

About the International Knowledge Management Institute

Based in Washington, D.C., the KM Institute is a global leader in Knowledge Management certifications and training, with thousands certified since 2001 and classes delivered in up to 15 countries annually. KMI trains and certifies KM team members in the methods and tools that enable individuals and organizations to transform (and substantially improve) human performance in the current Knowledge Age.

The post EK Teaching Upcoming Agile & Design Thinking Certification for KMI appeared first on Enterprise Knowledge.