AI implementations are already showing signs of knowledge gaps: hallucinations, wrong answers, incomplete answers, and even “unanswerable” questions. There are multiple causes for AI hallucinations, but an important one is not having the right knowledge to answer a question in the first place. While LLMs may have been trained on massive amounts of data, it doesn’t mean that they know your business, your people, or your customers. This is a common problem when organizations make the leap from how they experience “Public AI” tools like ChatGPT, Gemini, or Copilot, to attempting their own organization’s AI solutions. LLMs and agentic solutions need knowledge—your organization’s unique knowledge— to produce results that are unique to your and your customers’ needs, and help employees navigate and solve challenges they encounter in their day-to-day work. 

In a recent article, EK outlined key strategies for preparing content and data for AI. This blog post builds on that foundation by providing a step-by-step process for identifying and closing knowledge gaps, ensuring a more robust AI implementation.

The Importance of Bridging Knowledge Gaps for AI Readiness

EK lays out a six-step path to getting your content, data, and other knowledge assets AI-ready, yielding assets that are correct, complete, consistent, contextual, and compliant. The diagram below provides an overview of these six steps:

Identifying and filling knowledge gaps, the third step of EK’s path towards AI readiness, is crucial in ensuring that AI solutions have optimized inputs. 

Prior to filling gaps, an organization will have defined its critical knowledge assets and conducted a content cleanup. A content cleanup not only ensures the correctness and reliability of the knowledge assets, but also reveals the specific topics, concepts, or capabilities that the organization cannot currently supply to AI solutions as inputs.

This scenario presupposes that the organization has a clear idea of the AI use cases and purposes for its knowledge assets. Given the organization knows the questions AI needs to answer, an assessment to identify the location and state of knowledge assets can be targeted based on the inputs required. This assessment would be followed by efforts to collect the identified knowledge and optimize it for AI solutions. 

A second, more complicated, scenario arises when an organization hasn’t formulated a prioritized list of questions for AI to answer. The previously described approach, relying on drawing up a traditional knowledge inventory will face setbacks because it may prove difficult to scale, and won’t always uncover the insights we need for AI readiness. Knowledge inventories may help us understand our known unknowns, but they will not be helpful in revealing our unknown unknowns. 

Identifying the Gap

How can we identify something that is missing? At this juncture, organizations will need to leverage analytics, introduce semantics, and if AI is already deployed in the organization, then use it as a resource as well. There are different techniques to identify these gaps, depending on whether your organization has already deployed an AI solution or is ramping up for one. Available options include:

Before and After AI Deployment

Leveraging Analytics from Existing Systems

Monitoring and assessing different tools’ analytics is an established practice to understand user behavior. In this instance, EK applies these same methods to understand critical questions about the availability of knowledge assets. We are particularly interested in analytics that reveal answers to the following questions:

• When are our people giving up when navigating different sections of a tool or portal? 
• What sort of queries return no results?
• What queries are more likely to get abandoned? 
• What sort of content gets poor reviews, and by whom?
• What sort of material gets no engagement? What did the user do or search for before getting to it? 

These questions aim to identify instances of users trying, and failing, to get knowledge they need to do their work. Where appropriate, these questions can also be posed directly to users via surveys or focus groups to get a more rounded perspective. 

Semantics

Semantics involve modeling an organization’s knowledge landscape with taxonomies and ontologies. When taxonomies and ontologies have been properly designed, updated, and consistently applied to knowledge, they are invaluable as part of wider knowledge mapping efforts. In particular, semantic models can be used as an exemplar of what should be there, and can then be compared with what is actually present, thus revealing what is missing.

We recently worked with a professional association within the medical field, helping them define a semantic model for their expansive amount of content, and then defining an automated approach to tagging these knowledge assets. As part of the design process, EK taxonomists interviewed experts across all of the association’s organizational functional teams to define the terms that should be present in the organization’s knowledge assets. After the first few rounds of auto-tagging, we examined the taxonomy’s coverage, and found that a significant fraction of the terms in the taxonomy went unused. We validated our findings with our clients’ experts, and, to their surprise, our engagement revealed an imbalance of knowledge asset production: while some topics were covered by their content, others were entirely lacking. 

Valid taxonomy terms or ontology concepts for which few to no knowledge assets exist reveal a knowledge gap where AI is likely to struggle.

After AI Deployment

User Engagement & Feedback

To ensure a solution can scale, evolve, and remain effective over time, it is important to establish formal feedback mechanisms for users to engage with system owners and governance bodies on an ongoing basis. Ideally, users should have a frictionless way to report an unsatisfactory answer immediately after they receive it, whether it is because the answer is incomplete or just plain wrong. A thumbs-up or thumbs-down icon has traditionally been used to solicit this kind of feedback, but organizations should also consider dedicated chat channels, conversations within forums, or other approaches for communicating feedback to which their users are accustomed.

AI Design and Governance 

Out-of-the-box, pre-trained language models are designed to prioritize providing a fluid response, often leading them to confidently generate answers even when their underlying knowledge is uncertain or incomplete. This core behavior increases the risk of delivering wrong information to users. However, this flaw can be preempted by thoughtful design in enterprise AI solutions: the key is to transform them from a simple answer generator into a sophisticated instrument that can also detect knowledge gaps. Enterprise AI solutions can be engineered to proactively identify questions which they do not have adequate information to answer and immediately flag these requests. This approach effectively creates a mandate for AI governance bodies to capture the needed knowledge. 

AI can move beyond just alerting the relevant teams about missing knowledge. As we will soon discuss, AI holds additional capabilities to close knowledge gaps by inferring new insights from disparate, already-known information, and connecting users directly with relevant human experts. This allows enterprise AI to not only identify knowledge voids, but also begin the process of bridging them.

Closing the Gap

It is important, at this point, to make the distinction between knowledge that is truly missing from the organization and knowledge that is simply unavailable to the organization’s AI solution. The approach to close the knowledge gap will hinge on this key distinction. 

If the ‘missing’ knowledge is documented or recorded somewhere… but the knowledge is not in a format that AI can use it, then:

Transform and migrate the present knowledge asset into a format that AI can more readily ingest. 

If the ‘missing’ knowledge is documented or recorded somewhere… but the knowledge exists in private spaces like email or closed forums, then:

Establish workflows and guidelines to promote, elevate, and institutionalize knowledge that had been previously informal.

If the ‘missing’ knowledge is documented or recorded somewhere… but the knowledge exists in different fragments across disjointed repositories, then: 

Unify the disparate fragments of knowledge by designing and applying a semantic model to associate and contextualize them. 

If the ‘missing’ knowledge is not recorded anywhere… but the company’s experts hold this knowledge with them, then: 

Choose the appropriate techniques to elicit knowledge from experts during high-value moments of knowledge capture. It is important to note that we can begin incorporating agentic solutions to help the organization capture institutional knowledge, especially when agents can know or infer expertise held by the organization’s people. 

If the ‘missing’ knowledge is not recorded anywhere… and the knowledge cannot be found, then:

Make sure to clearly define the knowledge gap and its impact on the AI solution as it supports the business. When it has substantial effects on the solution’s ability to provide critical responses, then it will be up to subject matter experts within the organization to devise a strategy to create, acquire, and institutionalize the missing knowledge. 

Conclusion

Identifying and closing knowledge gaps is no small feat, and predicting knowledge needs was nearly impossible before the advent of AI. Now, AI acts as both a driver and a solution, helping modern enterprises maintain their competitive edge.

Whether your critical knowledge is in people’s heads or buried in documents, Enterprise Knowledge can help. We’ll show you how to capture, connect, and leverage your company’s knowledge assets to their full potential to solve complex problems and obtain the results you expect out of your AI investments. Contact us today to learn how to bridge your knowledge gaps with AI.

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In Greek mythology, the character Sysiphus is condemned to spend eternity pushing a boulder up a hill, only for the boulder to roll back down as soon as he nears the top. 

When organizations lack capability to manage and preserve their institutional knowledge, in the form of documented procedures, expertise, and know-how, their staff may experience a similar sentiment: doomed to repeat tasks that have already been accomplished, and solve for issues that have already been solved for by others. This needless repetition of work results in wasted time and resources, and represents a tangible opportunity cost: teams become unable to dedicate their attention and efforts to improving and innovating. 

Conversely, organizations that harness innovation can edge out advantages over their competitors. Research has found evidence that “perceived innovativeness improves the attractiveness of firms to consumers” (Keiningham et al., 2023). The Norwegian Innovation Index furthermore argues that increased innovation correlates to increased customer loyalty (NII, 2021). The Drucker Institute goes a step further, explaining how innovative companies “deftly use data and technology to ferret out evolving customer wants and needs—and respond by devising new products and ways to deliver them” (Wartzman & Tang, 2021). 

In this blog article, we’ll discuss the opportunity cost to innovation represented by the loss of institutional knowledge, and what can be done to solve this challenge. 

Poor Knowledge Management as a Barrier to Innovation

A few years ago, we had the opportunity to partner with a Silicon Valley firm. They initially sought our help to establish consistent approaches to share knowledge more effectively. This engagement started with an assessment of their current Knowledge Management (KM) maturity. As our discovery efforts progressed, we uncovered a fundamental challenge facing the organization: limited innovation due to immature KM practices.

Periodic reductions in their workforce had exacerbated these problems. With these layoffs, the cracks in their foundational knowledge management practices illuminated additional burdens. Remaining employees not only had to pick up where their departing colleagues left off, but in many cases had to recreate their work altogether because it wasn’t properly captured in the first place.

Looking closer at the root causes of hampered innovation, we found:

1. Institutional knowledge was not being consistently captured. Teams lacked established approaches to capture knowledge across the flow of day-to-day work, diverged in how they documented that knowledge, and often stored it in different knowledge bases. 
2. Knowledge bases were poorly curated and hard to navigate – instead employees became the most reliable source of information.
3. Staff had limited access and visibility into the work that others were doing. Knowledge bases were siloed across the organization, and staff were unable to search for things across multiple repositories.
4. Staff had limited opportunities for cross-functional collaboration and exchange of ideas. While the organization had several Employee Resource Groups (ERGs), these weren’t geared towards sharing expertise and strengthening shared business capabilities. 

Maximizing Knowledge Management Capabilities to Unlock Innovation

Established knowledge management practices within an organization contribute to both promoting innovation and removing barriers to it. We can summarize this into three overarching outcomes: 

Ability to reuse institutional knowledge. When organizations are able to retain and reuse institutional knowledge, their people can dedicate their efforts towards bringing new ideas to life, enhancing existing products and services, and improving processes. While there are no single practices nor tools that guarantee this outcome, there are several things that organizations can do to encourage knowledge capture and reuse: having designated places for different types of knowledge, making the most of high-value moments of knowledge capture by collecting key documentation and lessons learned on projects, and enabling staff to search and discover knowledge resources that have been produced in the past. At a large Sovereign Wealth Fund, we successfully incorporated these concepts and practices to deliver a Knowledge Portal. This solution and supporting practices provided a 360-degree view of critical business processes, enabling staff and executives to identify synergies and opportunities for collaboration in their work. 

Ability to cross-pollinate ideas and increase awareness across traditional organizational silos. This capability can be enabled through a diverse set of practices and tools. Traditionally, Communities of Practice (CoPs), have been a common tool for organizations to create spaces to nurture knowledge sharing. Other approaches can include Knowledge Cafes, such as the one we instituted at the Green Climate Fund (GCF), providing a repeatable opportunity to bring together people from all areas of the organization to discuss topics relevant to their work and encourage them to learn from each other. 

Ability to deploy advanced KM technologies to accelerate innovation. The proper application of advanced technologies can enable organizations to leverage its institutional knowledge as a springboard to unlock and accelerate business capabilities. For instance, a Semantic Layer can unlock knowledge that has previously been siloed within individual systems and organizational units, enabling executives to make faster decisions using more complete data available throughout the organization. Similarly, a semantic layer can enable individual contributors to gain broader awareness of expertise throughout the organization, creating pathways to collaboration. AI applications can further increase the organization’s capabilities to create and share insights, and take faster action. 

Closing

Innovation is a key business capability. All too often though, we find that innovation efforts are hampered because staff spend a lot of time fixing issues that have been fixed in the past rather than on ensuring that the organization is evolving to continually be able to meet its objectives. 

Enterprise Knowledge brings a holistic set of solutions that involve human-centered approaches and cutting edge technology to enable organizations to accelerate their innovation processes. Contact us at info@enterprise-knowledge.com if you would like assistance in maximizing the ability to leverage your institutional knowledge for innovation.

Institutional knowledge is the sum of experiences, skills, and knowledge resources available to an organization’s employees. It includes the insights, best practices, know-how, know-why, and know-who that enable teams to perform. This knowledge is the life blood of work happening in modern organizations. However, not all organizations are capable of preserving, maintaining, and mobilizing their institutional knowledge—much to their detriment. This blog is one in a series of articles exploring the costs of lost institutional knowledge and different approaches to overcoming challenges faced by organizations in being able to mobilize their knowledge resources. 

References

Keiningham, T., Aksoy, L., Buoye, A., Yan, A., Morgeson, F. V., Woodall, G., & Larivière, B. (2023). Customer perceptions of firm Innovativeness and Market Performance: A Nation-level, longitudinal, cross-industry examination. Journal of Service Research, 27(4), 475–489. doi:10.1177/10946705231220463

NII (2021), Technical Description: The Norwegian Innovation Index Research Model. Norwegian School of Economics (NHH). Available at: https://www.nhh.no/en/norwegian-innovation-index/about-nii/technical-description/

Wartzman, R., & Tang, K. (2021). Which industry excels at innovation? you’ll be surprised; consumer-staples companies stand out in the management top 250 ranking. New York, N.Y.: Dow Jones & Company Inc. Retrieved from https://www.proquest.com/blogs-podcasts-websites/which-industry-excels-at-innovation-youll-be/docview/2490681747/se-2

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This, however, is not the full extent of the impact of their departure. Operational efficiency and continuity are at stake. When organizations fail to capture and transfer seasoned leaders’ knowledge and expertise throughout their tenure, it becomes more difficult for up-and-coming employees to step up to fill in gaps when an individual decides to retire or leave the organization. In leadership positions, this problem is exacerbated when new leaders lack the historical context of previous decisions and strategic efforts. All of this translates to increased risks in succession. 

An Interim Solution to Departing Executives

We recently had the opportunity to partner with a regulatory agency in modernizing one of their knowledge management programs. As part of this effort, we began with one of their most pressing concerns: several of their senior leaders were due to retire in the next few months. They had built their careers within the agency, establishing and leading some of its largest, most impactful projects and programs. Understandably, many of their colleagues were concerned that with their departure, the organization would lose critical institutional knowledge and the ability to effectively sustain and expand on the work that they had been performing. We held a series of knowledge transfer activities to capture prioritized institutional knowledge and maintain it within the organization. Knowledge was prioritized by consulting senior leaders’ peers, their direct reports, and previous collaborators, focusing on the types of questions that they would like answers to. From the sessions, our team synthesized responses and produced searchable knowledge reports that, for instance, detailed the process of establishing hiring and training procedures for critical staff in niche fields. 

After going through several rounds of this approach for knowledge capture, it becomes evident that manually conducting end-of-career interviews requires a great deal of time and effort from both KM teams and interviewees. 

Sustainable Solutions to Knowledge Loss

While targeted knowledge capture activities may work as an interim solution to resolve an immediate need, activities like knowledge transfer interviews at the time of departure are not a sustainable, long-term solution. If your organization waits until an employee’s imminent exit to capture their knowledge, it does so under less-than-ideal conditions. Interviewees may face difficulty recalling the details of work that may have transpired several years in the past. Furthermore, the process can be very resource-intensive: a KM specialist needs to plan and facilitate each session, and afterwards they then need to conduct follow-ups, and synthesize and prepare the knowledge for reuse.

Ideally, organizations will establish a repeatable and consistent process to capture relevant knowledge from their employees as part of regular business. These processes should be paired with established and emerging technologies to reduce the effort required from individual experts to capture and contribute their knowledge. 

Technology remains a critical tool for implementing and scaling knowledge capture and transfer processes to prevent organizations from losing institutional knowledge. Often, we can leverage tools that are already in use within the organization. In recent years, teams have increasingly adopted digital channels and tools to collaborate and communicate, enabling new opportunities to embed captured knowledge into existing processes, and generating data and content that can be used by AI. For example: 

1. Mining meeting data, communication exchanges, and document authorship to identify hidden networks and pockets of expertise. Once these ‘hidden’ networks have been identified, they could be supported into the formation of Communities of Practice (CoPs) to nurture and transfer their knowledge across the organization. Real-world Application: At an international organization, we developed a recommender system that connected individuals to experts, which was especially useful during their project planning phase, creating the opportunity to bring in institutional knowledge to project teams from the very beginning of an effort. By creating a community and spaces for their members to share their knowledge, organizations can help remove single points of failure in the future. 
2. Establishing standards to collect key artifacts at high value moments of content capture and make them findable. The artifacts should be structured and tagged with metadata representing business context. To the extent possible, organizations should go beyond saving files and documents, and creating semantically-rich content so that knowledge is more readily found and reused. Real-world Application: EK helped a federal R&D center standardize their metadata in their project document repository systems, and leveraged an auto-tagging routine to apply the metadata at a large scale. This approach significantly improved their ability to find and understand historical research documents, preventing their institutional knowledge from being lost. 
3. Leveraging emerging technologies such as Generative AI agents to expand and automate the efforts of KM teams. Real-world Application: Recently, a large financial institution EK works with had an incident where a critical legacy system failed. They struggled to reach the person or the knowledge to get the system back up and running in time because their primary expert was on vacation. This incident also illuminated a larger problem leadership was facing: many of their tenured experts and senior management were nearing retirement and planning on leaving the organization within the year. They needed to quickly stand up a knowledge capture process that could be repeated for all of these individuals. EK designed an AI-powered agent to conduct asynchronous interviews in order to collect and synthesize knowledge from the departing experts and managers. Similar to the manual approach previously described, EK worked with stakeholders to define the success criteria of the interviews, and these were passed along to the AI agent. The AI agent then opens up a chat with each departing individual, asking questions until the desired outcomes are met. Interviewees respond to the questions at any time, enabling participants to contribute their knowledge at the most convenient times for them. 

Organizations can, and should, apply different combinations of process improvements and technology to not only capture knowledge from leaders within the organization, but also effectively scale them throughout the organization. 

Closing

The ability to retain institutional knowledge from departing leaders is essential for organizations—essential for maintaining operational efficiency and continuity and enabling informed decision-making.

Minimizing the impacts of departing leaders requires longer term approaches to identify, capture, and preserve institutional knowledge. The approach will depend on a variety of factors, including the time availability of leaders, whether their direct successors have been identified, the existence of knowledge bases within the organization, and the organization’s knowledge transfer preferences. Ultimately, this may result in establishing formal mentorship programs, communities of practice, knowledge summits, or other KM solutions. It will be important that this knowledge, through whichever means it is captured, is also intentionally disseminated across the organization.

Enterprise Knowledge helps clients across the globe in defining knowledge management strategies and leveraging knowledge capture and transfer techniques to preserve their institutional knowledge. If your organization needs assistance in this area, you can reach us at info@enterprise-knowledge.com.

Institutional knowledge is the sum of experiences, skills, and knowledge resources available to an organization’s employees. It includes the insights, best practices, know-how, know-why, and know-who that enable teams to perform. This knowledge is the life blood of work happening in modern organizations. However, not all organizations are capable of preserving, maintaining, and mobilizing their institutional knowledge—much to their detriment. This blog is one in a series of articles exploring the costs of lost institutional knowledge and different approaches to overcoming challenges faced by organizations in being able to mobilize their knowledge resources. 

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Indeed, effective AI solutions, much like other technologies, require quality inputs. AI needs data embedded with rich context derived from an organization’s institutional knowledge. Institutional knowledge is the collection of experiences, skills, and knowledge resources that are available to an organization. It includes the insights, best practices, know-how, know-why, and know-who that enable teams to perform. It not only resides in documentation, but it can be part of processes, and it lives in people’s heads. Extracting this institutional knowledge and injecting it into data and content being fed to technology systems is key to achieving Knowledge Intelligence (KI). One of the biggest gaps that we have observed is that this rich contextual knowledge is missing or inaccessible, and therefore AI deployments will not easily live up to their promises. 

Vast Deposits of Knowledge, but Limited Capabilities to Extract and Apply It

A while back we had the opportunity to work with a storied research institution. This institution has been around for over a century, working on cutting-edge research in multiple fields. They boast a monumental library with thousands (if not millions) of carefully produced and peer-reviewed manuscripts going back through their whole existence. However, when they tried to use AI to answer questions about their past experience, AI was unable to deliver the value that the organization and its researchers expected.

As we performed our discovery we noticed a couple of things that were working against our client: first, while they had a tremendous amount of content in their library, it was not optimized for leveraging as an input for AI or other advanced technologies. It lacked a significant amount of institutional knowledge, as evidenced by the absence of rich metadata and a consistent structure that allows AI and Large language Models (LLMs) to produce optimal answers. Second, not all the answers people sought from AI were captured as part of the final manuscripts that made it to the library. A significant amount of institutional knowledge remained constrained to the research team, inaccessible to AI in the first place: failures and lessons learned, relationships with external entities, project roles and responsibilities, know-why’s, and other critical knowledge were never deliberately captured.

Achieving Knowledge Intelligence (KI) to Improve AI Performance

As EK’s CEO wrote, there are three main practices that advance knowledge intelligence, which could be applied to organizations facing similar challenges in rolling out their AI solutions:

Expert Knowledge Capture & Transfer 

This refers to encoding expert knowledge and business context in an organization’s knowledge assets and tools, identifying high-value moments of knowledge creation and transfer, and establishing procedures to capture the key information needed to answer the questions AI seeks to provide. For our client in the previous example, this translated to standardizing approaches to project start-up and project closeout to make sure that knowledge was intentionally handed over and made available to the rest of the organization and its supporting systems. 

Real-World Application: At an international development bank, EK captured and embedded expert knowledge onto a knowledge graph and different repositories to enable a chatbot to deliver accurate and context-rich institutional knowledge to its stakeholders. 

Business Context Embedding 

Taking the previous practice one step further, this ensures that business context is embedded into content and other knowledge assets through consistent, structured metadata. This includes representing business, technical, and operational context so that it is understandable by AI and human users alike. It is important to leverage taxonomies to consistently describe this context. In the case of our client above, this included making sure to capture information about the duration and cost of their research projects, the people involved, clients and providers, and the different methodologies and techniques employed as part of the project. 

Real-World Application: At a global investment firm, we applied a custom generative AI solution to be able to develop a taxonomy to describe and classify risks so that they could enable data-driven decision-making. The use of generative AI not only reduced the level of effort required to classify the risks, since it took experts many hours to read and understand the source content, but it also increased the consistency in their classification.

Knowledge Extraction

This makes sure that AI and other solutions have access to rich knowledge resources through connections and aggregation. A semantic layer can represent an ideal tool to ensure that AI systems have knowledge from around the organization easily available. 

Real-World Application: For example, we recently assisted a large pharmaceutical company in extracting critical knowledge from thousands of its research documents so that researchers, compliance teams, and advanced semantic and AI tools could better ‘understand’ the company’s research activities, experiments and methods, and their products. 

It is important to note that these three practices also need to be grounded in clearly defined and prioritized use cases. The knowledge that is captured, embedded, and extracted by AI systems needs to be determined by actual business needs and aligned with business objectives. It may sound redundant to say, but in our experience we find that teams within organizations are often capturing knowledge that only serves their immediate needs, or capturing knowledge that they assume others need, if at all. 

Closing

Organizations are increasingly turning to AI to gain advantages over their competitors and unlock previously inaccessible capabilities. To truly take advantage of this, organizations need to make their institutional knowledge available to human and machine users alike. 

Enterprise Knowledge’s multidisciplinary team of experts helps clients across the globe maximize the effectiveness of their AI deployments through optimizing the data, content, and other knowledge resources at their disposal. If your organization needs assistance in these areas, you can reach us at info@enterprise-knowledge.com.

Institutional knowledge is the sum of experiences, skills, and knowledge resources available to an organization’s employees. It includes the insights, best practices, know-how, know-why, and know-who that enable teams to perform. This knowledge is the lifeblood of work happening in modern organizations. However, not all organizations are capable of preserving, maintaining, and mobilizing their institutional knowledge—much to their detriment. This blog is one in a series of articles exploring the costs of lost institutional knowledge and different approaches to overcoming challenges faced by organizations in being able to mobilize their knowledge resources. 

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The LavaCon Conference on Content Strategy and Technical Communication Management took place October 27-30 in Portland, Oregon. The theme of this year’s event was Content as a Business Asset: Reducing Costs, Generating Revenue, and Improving the Customer Experience Through Better Content.

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The following article highlights five key factors that design and implementation teams should consider when building a Knowledge Portal for their organizations.

Sources of Truth

Guillermo Galdamez

We define ‘sources of truth’ as the various systems responsible for generating data, recording transactions, or storing key documents about the vital business processes of an organization. These systems are fundamental to the day-to-day operations and long-term strategic objectives of the business. 

In a modern enterprise, the systems supporting diverse business processes can number in the dozens, if not hundreds, depending on the organization’s size. However, from the business perspective of a Knowledge Portal implementation, it is critical to prioritize integrations with each source based on appropriate criteria. Drawing from our experience, we’ve identified three key factors that Knowledge Portal leaders should consider:

• Business value. The source must contain data that is fundamental to both the business and to the Knowledge Portal’s objectives, aligning with the users’ expectations.
• Data readiness. The data within the source should be in a state ready for ingestion and aggregation (more on this in the next section). 
• Technical readiness. It may sound obvious, but the source systems need to be capable of providing data to the Knowledge Portal. In some cases, a source system might be under active development (and not yet operational), or it might have limited functionality for exporting the necessary data for the Knowledge Portal’s use cases.
  

Kate Erfle

A Knowledge Portal should draw from well-defined information sources that are recognized as being authoritative and trusted. A Knowledge Portal isn’t intended to act as the “source of truth” itself, but rather to aggregate and meaningfully connect data sources and repositories, providing a cohesive “view of truth.” 

As Guillermo pointed out, there are several key data and technical readiness factors to consider when integrating source systems within a Knowledge Portal ecosystem. For a successful implementation, the source systems should meet the following technical criteria:

• The data must be clean, consistent, and standardized (more on this in the next section).
• The data should be accessible in a standard, compatible format, either via an API or manual export.
• The data must be protected by necessary and appropriate security measures, or it should provide data points that can be used to implement and enforce these security measures.

Once a data source meets the established criteria for quality, import/export capabilities, and security, it becomes eligible for integration with the Knowledge Portal. Within the portal, it may be possible to create or update content, but the data source remains its own “source of truth”. All changes made within the Knowledge Portal should be reflected in the corresponding source system to maintain consistency, accuracy, and integrity of the source system data. During the design and implementation of a Knowledge Portal, it is critical to consider the impact of user actions and to ensure that any changes are accurately reflected in the source data. This approach ensures the continued accuracy and reliability of data from the source systems.

Information Quality

Guillermo Galdamez

One of the most common issues I encounter when talking to our clients is the perception that their data and unstructured content is unreliable. This could be due to various issues: the data might be incomplete, duplicative, outdated, or just plain wrong. As a result, employees can spend hours not only searching for information and data but also tracking down people who can confirm its reliability and usability.

In discussing content and data quality, one of the foundational steps is taking inventory of the ‘stuff’ contained within your prioritized sources of truth. Though the maxim “You can’t manage what you can’t measure,” has often sparked debate about its merits, this is one occasion where it is notably relevant. It is important for the implementation team, as well as the business to have visibility of the data and content it means to ingest and display through the Knowledge Portal. Performing a content analysis is key in providing the Knowledge Portal team with the information they need to ensure that information provided by the Knowledge Portal is consistent, reliable, and timely. 

A content inventory and audit often reveals areas where data and content needs to be remediated, migrated, archived, or disposed of. The Knowledge Portal team should take this opportunity to perform data and content cleanup. During development, the Knowledge Portal Team can collaborate with various teams to improve data and content quality. Even following its launch, the Portal, by aggregating and presenting information in new ways, can reveal gaps or inconsistencies across its sources. It will be helpful to define feedback mechanisms between users, the Knowledge Portal Team, and data and content owners to be able to address instances where data and content needs to be maintained. 

Gaining and sustaining user trust is crucial for Knowledge Portals. Users will continuously visit the Portal as long as they perceive that it solves their previous challenges. If the Portal becomes a new ‘junk drawer’ for data, engagement will decline rapidly. To avoid this, implement a strong change management and communications strategy to continually remind users about the Portal’s capabilities and value.

Kate Erfle

Maintaining high-quality data and content is crucial for a Knowledge Portal’s success. As Guillermo stated, the implementation phase of a Knowledge Portal offers the perfect opportunity for data cleanup.

To begin, it’s important for individual system owners and administrators to do what is feasible within their systems to ensure high-quality data. Before it’s provided to the Portal, several transformation and cleaning steps can be applied directly to the source system data. The Knowledge Portal implementation team should collaborate closely with the various data repository teams to ensure the required data fields are standardized, cleaned, and validated before being exported. By working together, these teams can assess the current state of the data, identify missing fields, spot discrepancies, and address inconsistencies.

If the data from source systems still contains imperfections, a few remediation strategies can be applied to prepare it for integration: 

• Removing Placeholder or Dummy Data: If the data source team is unable to remediate placeholder or dummy data, the Portal team can compile a list of these “dummy values” and remove them entirely. Displaying a field as “empty” is preferable to showing a fake or false value.
• Normalizing Terms with a Controlled Vocabulary: In cases where the source system lacks a controlled vocabulary, the Portal team can align certain data fields with the Knowledge Portal’s taxonomy and ontology. This involves using synonyms to match various representations of the same concept into one concise point.
• Enforcing Data Standards through APIs: The Portal team’s APIs can be configured to expect and enforce specific data standards, models, and types, ensuring that only accurately conforming data is ingested and displayed to the end user. Such enforcement can also highlight required fields and alert data teams when essential data is missing, which increases visibility into the underlying issues associated with bad data.

Guillermo emphasized the importance of remedying data issues to build and maintain user trust and buy-in. Effectively addressing bad data is also critical to avoid significant issues:

• Preventing Unauthorized Access to Information: Without proper security measures and clear definitions of user identities and access rights, there’s a high risk of sensitive information being exposed. The data needs to clearly indicate who should be granted access, and users need to be uniquely and consistently identifiable across systems.
• Ensuring Full Functionality of the Knowledge Portal: If data is incomplete or untrustworthy, it impedes the use of advanced capabilities and functionalities of the Knowledge Portal. Reliable data is foundational for seeing its full potential.

Business Meaning and Context

Guillermo Galdamez

As mentioned earlier, Knowledge Portals aggregate information from diverse sources and present it to users, introducing a new capability to the organization. It’s essential for the Knowledge Portal team to fully understand the data and information being presented to the users. This includes knowing its significance and business value, its origin, how it is generated, and its connection to other business processes. Keep in mind that this information is seldom presented to users all at once, so they will likely face a learning curve to utilize the Knowledge Portal effectively. This challenge can be mitigated through thoughtful design, change management, training, and communication. 

Designs for a Knowledge Portal need to strategically organize different information elements. This involves not only prioritizing these elements based on relative importance, but also ensuring they align with business logic and are linked to related data, information, and people. In other words, the design needs to be understandable to all intended users at a single glance. Achieving this requires clear, prioritized use cases tailored to the Knowledge Portal’s audiences, combined with thorough user research that informs user expectations. Knowing this, it becomes easier to design with user needs and objectives in mind and have it more seamlessly fit into their daily workflows and activities. 

Effective change management, training, and communications help reinforce the purpose and the value of a Knowledge Portal, which might not always be intuitive to everyone across the organization; some users may be resistant to change, preferring to stick to familiar routines. It’s crucial for the Knowledge Portal team to understand these users’ motivations, their hesitations, and what they value. Clearly articulating the individual benefits users will gain from the Portal, setting clear expectations, and providing guidance on using the Portal successfully are crucial for new users to adopt it and appreciate its value in their work.

Kate Erfle

It is essential to provide context to the information available on the portal, especially within a specific business or industry setting. This involves adding metadata, descriptions, and categorizations to data, allowing siloed, disconnected information to be associated and helping users discover content relevant to their needs quickly and efficiently. 

A robust metadata system and a well-defined taxonomy can aid in organizing and presenting content in a meaningful way. It’s important to evaluate the current state of existing taxonomies and controlled vocabularies across each source system, as well as to assess the prevalence and consistency of metadata applied to content within these systems. These evaluations help determine the level of effort required to standardize and connect content effectively. To obtain the full benefits of a Knowledge Portal–creating an Enterprise 360 view of the organization’s assets, knowledge, and data–this content needs to be well-defined, categorized, and described.

Security and Governance

Guillermo Galdamez

One of the most common motivations driving the implementation of Knowledge Portals is the user’s need to quickly find specific information required for their work. However, users often overlook the equally important aspect of securing this information. 

Often, information is shared through unsecured channels like email, chat, or other common communication methods at users’ disposal. This approach places the responsibility entirely on the sender to ascertain and decide if a recipient is authorized to receive the information. Sometimes senders mistakenly send information to the wrong person, or they may need additional time to verify the recipient’s access rights. Furthermore, senders may need to redact parts of the information that the recipient isn’t permitted to see, which adds another time-consuming step. 

The Knowledge Portal implementation must address this organizational challenge. At times, the Knowledge Portal team will need to guide the organization in establishing a clear framework for access control. This is especially necessary when the Knowledge Portal creates new types of information and data by aggregating, repackaging, and delivering them to users.

Kate Erfle

Security and governance are paramount in the construction of a Knowledge Portal. They profoundly influence various implementation details and are critical for ensuring the confidentiality, integrity, and availability of information within the portal.

The first major piece of security and governance is user authentication, which involves verifying a user’s identity. Several options for implementing user authentication include traditional username and password, Multi-Factor Authentication (MFA), and Single Sign-On (SSO). These choices will be influenced by the existing authentication and identity management systems in use within the client organization. Solidifying these design decisions early in the architecting process is critical as they affect many facets of the portal’s implementation.

The second major piece of security and governance is user authorization, which involves granting users permission to access specific resources based on their identity, as established through user authentication. Multiple authorization models may be necessary based on the level of fine-grained access control required. Popular models include: 

• Role-Based Access Control (RBAC): This model involves defining roles (e.g., admin, user, manager) and assigning specific permissions to each. Users are then assigned to these roles, which determine their access level.
• Attribute-Based Access Control (ABAC): In this model, access decisions are based on user attributes (e.g., department, location, job title), with policies that specify the conditions for access.

Depending on the organization’s use case, one or a combination of these may be used to manage user access and ensure sensitive data is secured. The difficulty and complexity of the implementation will be directly correlated with the current and target state of identity and security management across the organization, as well as the breadth and depth of data classification applied to the organization’s data.

Information Seeking and Action

Guillermo Galdamez

Knowledge Portal users will approach their quest for information in a variety of ways. Users may prefer to browse through content during exploratory sessions, or they may leverage search when they know precisely what they need. Often, users employ a combination of these approaches depending on their specific needs for data or content. 

For instance, in a recent Knowledge Portal project, our user research revealed that individuals rarely searched for documents directly. Instead, they searched for various business entities and then browsed through related documents. This prompted the team to reevaluate the prioritization of documents in search results and the necessary data points that should be displayed alongside these documents to provide meaningful context. 

In summary, having a strong user research strategy is essential to understand what type of data and information users are seeking, their reasons for needing it, their subsequent use of it, and how this supports the broader organization’s processes and objectives.

Kate Erfle

Knowledge Portals are designed to provide users with access to a broader range of information and resources than available in the various source systems, and they should facilitate users in both finding necessary information and taking meaningful actions based on that information. 

Information Seeking Involves:

• Search Functionality: A robust search engine matches user queries to the most relevant content. This involves keyword relevance, search and ranking algorithms, and user-specific parameters. Tailoring these parameters to the organization’s specific business use cases improves search accuracy. The incorporation of taxonomies and ontologies for content categorization, tagging, and filtering further refines search results, aligning them with organization-specific terminology, and enables users to sift through results using familiar business vernacular.
• Browsing and Navigation: Well-structured categories, facets, menus, and user-friendly navigation features help users discover not just the information they directly seek, but also related, relevant content they may not have anticipated. This can be done through various interfaces, including mobile applications, enhancing the portal’s accessibility and user interaction.
• Dynamic Content Aggregation and Personalization: A standout feature of Knowledge Portals is their ability to aggregate data from various sources into a single page, which can be highly personalized. For instance, a project aggregator page might include sections on related projects, prioritizing those relevant to the user’s department.

Action Involves:

• Integration with Source Systems or Applications: Providing seamless links to source systems within the Knowledge Portal allows users to easily find content and perform CRUD (Create, Read, Update, Delete) operations on the original content.
• Task Support: Tools for document generation, data visualization, workflow automation, and more, assist users in their daily tasks and enable them to make the most of source data and optimize business processes.
• Learning and Performance Support: Dynamic content and interactive features encourage users to actively engage with content which strengthens their comprehension and absorption of information. 
• Feedback Mechanism: Enabling users to contribute feedback on content and documents within the portal fosters continuous improvement and enhances the portal’s effectiveness over time.

Closing

The business and technical considerations outlined here are essential for creating a Knowledge Portal that intuitively delivers information to its users. Keep in mind that these considerations are interconnected, and a well-designed Knowledge Portal should strike a balance between them to provide users with a seamless and enriching experience. Should your organization aspire to implement a Knowledge Portal, our team of experts can guide you through these challenges and intricacies, ensuring a successful deployment.

The post Expert Analysis: Top 5 Considerations When Building a Modern Knowledge Portal appeared first on Enterprise Knowledge.

The mechanics of completing lessons learned efforts can be deceivingly simple: You get people in a room (physically or otherwise) and discuss opportunities for improvement based on what they experienced during a project. However, many teams and organizations experience difficulty in realizing the full business value they expect from their lessons learned efforts. 

First, it is helpful to define what a lesson learned is. In a broad sense, a lesson learned is knowledge created over the course of past work that is recalled and applied to improve present and future efforts. For example, a project team overcoming a challenge by adapting an existing process or tool can yield innovations towards future project approaches. Another team may dissect a failure to identify what has gone wrong and enact changes to avoid it in the future. In this blog, I discuss five tips on how to capture and apply lessons learned in your organization. 

Identify moments of high-value knowledge capture

Memory can be quite fragile. Details of what occurred as part of a project can get fuzzy quickly, and the more time goes by, the greater the mental effort any one person must spend in retrieving their memories. 

Many teams incorporate a lessons learned component at a project’s conclusion through retrospectives or after-action reviews. If your project or initiative spans multiple months, or even years, then waiting until its end to elicit lessons learned runs the risk of missing key details, simply because participants have already forgotten. 

The antidote for loss of key details over time? Identify moments of high-value knowledge capture and incorporate them into your project or sprint plans in advance. If you are working as part of the project, you may not need to wait until the end to begin capturing lessons learned, for every stage of the project represents an opportunity to discuss lessons learned. For instance,  if you have made important decisions, tried out something new, or experienced something that did not go as anticipated, it is important for team members’ memories to be as recent as possible so that they can recall valuable and meaningful details.

Include the right voices in the conversation

In increasingly complex and distributed work environments, it is rare that any single person can get (or give) a full view of what is happening in a project with all of the interdependencies and interactions that affect the outcome of an initiative. Therefore, discussions can benefit from the holistic perspective that a diverse group can bring. 

Consider the team members, stakeholders, and partners that can contribute to these conversations, and invite them to participate in the lessons learned discussion. Moreover, think broadly; these individuals are likely to span across multiple functions and up and down hierarchies. If relevant, bring people in who are external to the organization, such as partners, vendors, consultants, and others who may be able to contribute to a comprehensive understanding of the project. Make the most out of everybody’s time together by defining an agenda and prioritizing specific discussion points beforehand. 

Engage people meaningfully 

Acknowledge participants’ own preferences, habits, and agendas. These will differ widely, especially when dealing with a diverse group. Establish ground rules, expectations, and objectives for a lessons learned session early on, and enforce them. If necessary, do a quick touchpoint with select participants prior to the session to ensure alignment on the objectives and prevent any surprises. 

Keep in mind there may be individuals who dominate conversations and others who are naturally quiet. There will be some participants who need to talk out their ideas aloud, while others may need quiet time to reflect. In order to elicit the most powerful nuggets of knowledge out of participants, you may want to leverage a variety of channels: some verbal, some asynchronous, some written. The key here is to adapt your approach to maximize peoples’ contributions. In addition, while a live session may be the best method to maximize engagement, the reality of work is that some people will need to join remotely, and others in different time zones may not be able to join at all. Having a diversity of options for engagement will enable a wider segment of individuals to participate. 

This being said, the purpose of engaging stakeholders in lessons learned is to get a holistic account of how the work was done, what factors influenced the project’s results (whether expected or unexpected), and what can be done so that future projects can perform better. Here is a list of key knowledge nuggets that you should try to elicit:

1. Business context. Things seldom remain the same on a project, as assumptions are proven wrong, new constraints arise, and competing priorities emerge in other parts of the business. You may ask participants, “What are things that future projects should look out for and mitigate earlier on in the project?”
2. Key people, partnerships, and other relationships. Interactions that proved instrumental throughout the project. 
3. Skills and expertise. Whose skills and expertise came in handy at different points in the project? Did the project’s work help develop new skills? What emergent needs will we need to address in the future?
4. Introduced innovations. Often, teams will adapt existing processes, practices, or technologies to overcome specific challenges that arose during the project. These may be helpful to collect and disseminate throughout the organization so that other projects can leverage these innovations. 

Finally, conversation should be focused on producing actionable insights. These should consider modifications to the way work is conducted before, during, and after the project concludes. It is helpful if these tasks have an owner, a deadline, and someone to provide guidance and oversight in case people run into delays or roadblocks. 

Save captured lessons learned for easy retrieval and reuse

People may contribute as many ideas, insights, and learnings as they can, but if there is no way to effectively capture lessons learned in a manner that is consistent and retrievable, then these learnings will be difficult to find and apply in the future. 

To capture lessons learned effectively, you need two things:

1. A designated repository for storing lessons learned. Organizations often have a patchwork of solutions: Confluence, Google Drive, SharePoint, shared network drives, and sometimes individual spreadsheets. Different teams may have decided to use any one of these as knowledge bases for their needs, but if everyone is using something different, then lessons learned become difficult to find and understand. Having a designated place where teams store their lessons learned sets the foundation for making them retrievable and reusable. 
2. A consistent structure and tags to organize and classify lessons learned so that they can be easily searched for and found later. A consistent structure provides a set of attributes that defines what a “lesson learned” looks like, such as a title, lesson details, the type of project, the phase of the project that it applies to, the lesson’s topic, and others that would be relevant to describe and categorize the lesson learned. Having a standard structure makes lessons learned easy to write, and it also makes them easy to filter when trying to find them.

Loop lessons learned back into your business processes

In my client experiences, I’ve often seen lessons learned become an item in a to-do list; as long as they have a meeting at the end of the project or fill out a simple report, they can claim they do “lessons learned,” but the organization is not meaningfully learning.  

An organization doesn’t actually learn unless it applies the lessons and knowledge it has captured to present and future ways of working. For example, lessons learned may lead to changes in the way projects are staffed, introducing checks and safeguards at different stages of the project, bringing in experts or stakeholders when certain types of decisions are made, or including certain knowledge as part of new employee onboarding. 

There may be several challenges to organizations being able to apply lessons learned. There may be technical considerations in capturing, retrieving, and curating lessons learned within a knowledge base, or the lessons learned may require individuals to change their behaviors and habits. 

From a technical standpoint, building on having a knowledge base and consistently-tagged information within it, lessons learned can be indexed by a search tool to make them increasingly findable. More advanced applications would include integrating lessons learned into a knowledge graph so that they can be associated with different data and artifacts across the organization, then incorporated into a recommender system that proactively delivers relevant lessons learned to project leaders at key moments throughout the project.

Behavioral challenges can be tricky to overcome as well, and you can take both a top-down and a bottom-up approach to address them. Top-down strategies may include embedding lessons learned retrieval throughout different project stages or prior to key project activities. However, these will need to be complemented through a bottom-up approach: The people receiving new knowledge in the form of lessons learned must be convinced that the knowledge is valuable and that applying it will bring benefits to their work. A holistic change management and communications approach may be necessary to reinforce the expectations around lessons learned, share success stories, and reiterate the value that lessons learned bring to individuals and their teams. 

Closing

Lessons learned, captured and applied effectively to future projects, can bring a bounty of benefits to organizations. The knowledge that is generated as part of everyday work can, and should be, leveraged to improve the results of future work—enhancing the experience for organizations’ customers by learning to better anticipate their needs or prevent mistakes, their employees by better delivering the resources they need to succeed, and their bottom line by achieving efficiencies and introducing innovations. 

At EK, we specialize in all things Knowledge Management, from strategy, to design, onto implementation and maintenance. If you need any help setting up or improving lessons learned, we will be glad to help, no matter where you are on your journey. Contact us today!

The post Five Tips for Improving Lessons Learned in Project-Based Organizations appeared first on Enterprise Knowledge.

At the project’s onset, I was presented with the opportunity to act as the KM Lead to help guide the team and our client in KM best practices and align the solution to business needs. My role later evolved to include SCRUM Project Owner responsibilities as well, working with multiple stakeholders to establish priorities for development and paving a path for our development team to release valuable iterations of our solution. 

The journey of developing a Knowledge Portal has had its ups and downs. There were also many hidden dangers, latent mistakes, and sudden risks along the way. It could have been very easy to go down the wrong path and waste our team’s efforts and resources. In this blog, I will share 5 of the lessons I’ve collected over the past year in the implementation of a Knowledge Portal – hopefully these will serve you well in your own Knowledge Portal journey. 

1. Pragmatism Over Idealism

When we first begin talking about Knowledge Portals with our stakeholders, you can see their eyes illuminate with thrust open doors of opportunities. Business leaders and their staff have so many needs and ideas that could potentially be addressed through a Knowledge Portal. 

However, Knowledge Portals are highly complex solutions that incorporate multiple technologies and data from upstream systems that must all work in concert to achieve stakeholders’ needs and expectations. Throughout the development of the Knowledge Portal, we ran into a multitude of blockers, some outside our control, and many of which prevented us from achieving our initial priorities.

In the spirit of Agile, our team’s focus was to deliver value and results as early as possible, so we got creative. Features that were once fourth on our backlog rose to the top. If we had stayed the course and worked to remove immediate roadblocks, we may have delayed our releases by half a year. Showing early business value was critical to sustain engagement with our key stakeholders and maintain the project’s momentum. Our first release incorporated all of our underlying technologies, giving us a very solid foundation to release further functionality quicker in the future. 

2. Content Quality and Governance Takes a Front Seat

A Knowledge Portal is a powerful tool to surface content and data that was previously “locked away” in information silos, guarded by bureaucracy and obscure procedures. We knew from the beginning that displaying accurate and reliable content and data would be a key to the Knowledge Portal’s success.

Throughout the development process, we found that some of the data was not ready to be displayed on the Portal. This slowed us down, but we worked with different data owners to help fix the erroneous data. To be fair, prior to the Portal, it was cumbersome for them to get a holistic view of their data, especially if it was spread across multiple repositories. The success of a Knowledge Portal is dependent on the quality, consistency, and accessibility of the underlying content and data. It is critical to plan for this, and to expect time and effort for cleanup and enhancements. To an extent, our development of the Knowledge Portal encouraged our client to find solutions to some of their data quality issues, as well as create new processes and guidance on data management. This process ensures that data is maintained and users perceive it as being reliable.

Expect issues with data and content quality. It will be helpful for you to know who data owners are and to have a protocol in place for raising and escalating issues when they occur. This is something I would recommend doing early on in the project – at the kickoff, or before, if possible. Later, incorporate data checks throughout the entire development lifecycle; use actual sample data in design wireframes, get previews of data during development, and dedicate time during testing to validate whether the incoming data meets user wants and expectations.

3. Technical Partnerships Are Key

Given the complexity of Knowledge Portals, it can be a humbling experience to tackle their enterprise development. It is difficult for any one person to have the depth of expertise in the range of knowledge domains it covers: UX, taxonomy and ontology, search, strategic business alignment, data management, content modeling, systems infrastructure and architecture, and many more.

A successful Knowledge Portal implementation requires a combination of design, business, and technology skills. While I could focus on some of the definition and prioritization of use cases and features, my technical partners could focus on the nuts-and-bolts of how these features would come together. This being said, the idiom “it takes a village” really applies to the development of a Knowledge Portal. Being part of the EK Team was a real advantage because of the quick access to a depth of expertise in every necessary aspect to build a Knowledge Portal: UX designers, engineers and developers, data scientists, ontologists and taxonomists, technical analysts, and Agilists to support our work. 

There were a few additional technical partnerships which were key to the Knowledge Portal’s success. Our relationship with infrastructure and security teams facilitated our adherence to internal standards and requirements that the Portal needed to meet. Although we are technology-agnostic, our partnership technology vendors facilitated our work as well.They aided in both maximizing the use of the features of each individual component of the Knowledge Portal and the expedited resolution of any unforeseen issue or question that our development team had.  

Just as important as it was to bring data and content together in the Portal, we brought together disparate teams and individuals to make the Knowledge Portal a reality. 

4. Early Technology Choices Are Critical

When we started the project, the organization lacked the full suite of technologies needed to implement the Knowledge Portal, so one of the first steps we took was defining and prioritizing business and technical requirements. We then took a close look at the leaders in each of the spaces to evaluate and choose the technologies that would satisfy these requirements.

Even though early iterations of the Knowledge Portal did not make full use of each of the technical solutions’ capabilities, smart choices early on future-proofed our Knowledge Portal. The technologies, coupled with our design approach (focused on solving business problems as opposed to delivering features), resulted in an easily extensible Knowledge Portal. As new requirements come up, we have been able to fulfill them without the need for further technology investments. 

As we move forward, I am sure new requirements and priorities will surface that will necessitate the incorporation of the latest technologies; however, I am confident our experts will provide guidance to stakeholders on emergent technologies that can continue to extend the overall solution.

5. If You Build It… They May Not Come

Being a KM practitioner, I find the concept of Knowledge Portals thrilling. Most people in an organization will not share this sentiment, and it will lead to disappointment if your target users are not adopting and frequently using the solution you’ve been working on. I’ve found users mostly want to get on with their work, and changing habits is hard. Even if you are providing them with new capabilities for their teams, they may not have the additional attention and energy to embrace a new way of doing things. Deploying any new KM system requires a robust change management and communications strategy to drive adoption of tools and healthy KM practices.

For success on any initiative of this scale, change management and communications is critical. We worked together with our stakeholders and users to establish a virtuous cycle of feedback, where our development is now more responsive to users’ needs, while at the same time able to comprehensively communicate the value and expectations of the Knowledge Portal.

We assembled a cross-functional group of stakeholders to inform our design and refine our messaging and promotion around the Knowledge Portal. We also leveraged different data and knowledge management stakeholders to champion our cause and help articulate to their colleagues the value that the Knowledge Portal brought to their team work. Ultimately, we need to demonstrate how the Knowledge Portal is not only aligned to the overall organization’s strategic objectives, but how it will make each individual user’s life easier. 

Closing

The development of a Knowledge Portal is quite the endeavor. It requires the effort of a multidisciplinary team to get all of its components working in harmony and aligned to tangible business objectives. If you are considering implementing a Knowledge Portal in your organization, please contact us! We have quite a few more lessons to share. 

The post Five Lessons in Developing and Deploying a Modern Knowledge Portal appeared first on Enterprise Knowledge.