Semantic search has long proven to be a powerful tool in creating intelligent search experiences. By leveraging a semantic data model, it can effectively understand the searcher’s intent and the contextual meaning of the terms to improve search accuracy. In this session, Majumder and Powell presented case studies for three different organizations across three different industries (finance, pharmaceuticals, and federal research) that started their semantic search journey at three very different maturity levels. For each case study, they described the business use case, solution architecture, implementation approach, and outcomes. Finally, Majumder and Powell rounded out the presentation with a practical guide to getting started with semantic search projects using the organization’s current maturity in the space as a starting point.

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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!

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An important precursor to developing any solution is our KM element of People, which focuses on the individual team members and the flow of knowledge within an organization. We define this element as the overarching characteristics and behaviors of team members as they pertain to knowledge management, which we investigate through human-centric discovery work like focus groups, interviews, and workshops. Oftentimes, the needs of certain users or specific KM roles are identified in the early stages of our discovery work with an organization, prompting us to determine where KM can have the greatest impact, help the organization understand why certain groups may be prioritized in the KM initiative, and define a roadmap that frontloads that which is most impactful to prove the value of KM. 

While KM is crucial to each part of an organization, there are cases when KM efforts should be focused on a specific department, role, or persona. EK recognizes that KM roles and responsibilities differ based on an employee’s business function, tenure, and seniority, which in turn affects how KM impacts various business groups (cohorts of individuals at the same seniority and tenure). In this blog, I will walk you through an essential element of communicating the value and focus of a KM initiative: how KM impacts business groups differently. 

To clarify some of the terms I’ll be using, seniority is about rank in an organization. Tenure, on the other hand, refers to someone’s length of employment. An employee can be high-ranking, but if he or she is newer to the company, they may not have the operational knowledge or experience of someone less senior. Familiarity with and use of an organization’s current KM processes will likely come with tenure, not seniority. I’ll now differentiate between Knowledge Consumers and Knowledge Creators in an organization, using a bell curve graphic to display how KM’s impact is not linear, but distributed based on seniority and tenure.

Knowledge Consumers

As seen on the bottom left and right sides of the bell curve, knowledge consumers are often entry-level employees or executives/leadership figures. Entry-level employees (low tenure and low seniority) often do not possess the knowledge or experience to create and share information, meaning that KM efforts and processes for them will focus on consuming knowledge, requiring an increased ability to find the knowledge and experts they need to do their jobs. On the other side of the spectrum, organizational executives and leadership (high tenure and high seniority) don’t often focus on everyday knowledge creation and sharing, though they may direct it or encourage it. These individuals rely on their employees to get them the information they need and produce new knowledge based on their guidance, vision, and overall strategic direction. KM metrics and Return on Investment (ROI) are primarily important to this group, as they depend on the overall business outcomes and economic value of KM initiatives, rather than the smaller and more detailed phases of a KM project.

Knowledge Creators  

At the top of the bell curve lies knowledge creators, those “middlemen” and senior-level employees who have been with the organization for a good amount of time and/or possess subject matter expertise. Their everyday work generates the majority of knowledge and information within an organization, as they have the experience and wherewithal to recognize gaps in knowledge and fill them. Those that are well-tenured within this group also know how to generate valuable information and are familiar with the processes and systems that best support the flow of knowledge, whether that be capturing and transferring tacit knowledge or sharing and distributing explicit knowledge. Knowledge consumers depend on this group to supply them with expert information and guidance. 

It should be obvious at this point how and why KM impacts different business groups. KM matters greatly to each of these groups, but during countless KM strategy and roadmapping engagements, we have found that knowledge creators both contribute to and benefit from improved KM processes the most. It is this type of employee that usually provides us with the best picture of an organization’s highest needs, day-to-day operations, and how things should be done because of their expertise and experience in creating and managing effective content, technologies, and workflows. Oftentimes, the people in the middle of the organization (the group at the top of the bell curve) are the most prolific creators of knowledge, though of course a successful KM program will empower all users to become knowledge creators. There are cases where knowledge consumers may be the focus and priority of a KM engagement, such as efforts related to training and onboarding, but oftentimes, those processes and best practices will still be directed and facilitated by knowledge creators.

While we do not “play favorites” with certain business groups in a KM effort, this knowledge is important when identifying with whom and how to validate KM solutions based on how said solutions will impact their everyday work. Keep the KM impact bell curve in mind when considering impacts and buy-in for your next KM initiative.

If you are interested in KM’s impact on your organization, contact us to learn more!

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

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

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

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

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

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

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

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

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

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

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

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

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