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J.E. Dice Regulatory Solutions

Building a Knowledge Management Platform - Simple Scales, Complex Fails

Many organizations are reactive rather than proactive when it comes to knowledge management (KM). The firehose of information created and collected on a daily basis frequently has nowhere to go but an employee's personal file inventory. As a result a lot of information is effective lost and/or duplicated throughout the organization. Time is money and it is constantly being lost when employees have nowhere to share collective resources.

Effective knowledge management in an organization creates a framework and institutional knowledge baseline that provides actionable information (for my needs it's regulatory, scientific and competitive intelligence) for all stakeholders. KM is a collective knowledge base that informs strategy and generally produces higher-quality submissions to regulatory authorities. This is accomplished by meticulously curating and organizing data, providing the collateral for a comprehensive database that empowers stakeholders to access critical information with ease. Here is a 5-point plan to begin the process of building a knowledge management platform:

1️⃣ Assessment and Goal Definition

Identify the specific KM needs and goals of the business. What types of information and documents do you need to manage? What are the key objectives, such as improving decision-making, enhancing collaboration, or streamlining operations? Understanding the requirements is crucial. Arguably this is the most important step. Make sure you know what the problem is to-be-solved.

2️⃣ Document Cataloging and Taxonomy

Develop a structured document cataloging system. Create a well-defined taxonomy that categorizes documents based on their content, purpose, and relevance. Consider using metadata and tags to enhance searchability. Ensure consistency in naming conventions.

3️⃣ Selecting the Right KM Application

Evaluate KM applications based on your needs. Consider factors such as scalability, security, user-friendliness, integration capabilities, and cost. Scalability is key, especially in certain industries where the guidelines often shift, requiring considerable rework.

4️⃣ Implementation and User Training

Implement the chosen knowledge management system, ensuring it aligns with your cataloging and taxonomy framework. Provide comprehensive training to your team on how to use the platform effectively. Encourage active participation and contributions from employees.

5️⃣ Continuous Improvement and Maintenance

Establish a system for ongoing maintenance and improvement. Regularly review and update your taxonomy and document cataloging methods to accommodate changing business needs. Monitor user feedback and analytics to identify areas for enhancement.

KM Examples in the Nicotine and Tobacco Industry

There are several examples of KM platforms created in the tobacco and nicotine industry space. These databases include millions of documents and required a significant amount of time and effort to build and maintain. They offer insights into how large amounts of disparate data can be presented and searched. There's perhaps no other industry in the world with as much information publicly-accessible.

  • PMDocs provides the public access to documents produced by Philip Morris USA Inc. and Altria Group Inc.


  • Truth Tobacco Industry Documents

  • An archive of 14 million documents created by tobacco companies about their advertising, manufacturing, marketing, scientific research and political activities, hosted by the UCSF Library.


  • R.J. Reynolds Tobacco Company Online Litigation Document Archive


  • Stanford Research Into the Impact of Tobacco Advertising (SRITA) analysis of tobacco industry advertising campaigns


Conclusions

Many organizations have become remote-first and hybrid workplaces and knowledge transfer must become more intentional. Having a robust KM platform to share knowledge is more important than ever when the institution exists mostly virtually. It's also important to consider the implications of a future where artificial intelligence (AI) applications become more prevalent. AI is now top-of-mind and many organizations have just started to realize the potential that exists with this type of technology. It seems obvious to me that organizations with a process in place to catalogue and index on a KM platform will be in a much better position to leverage large language model (LLM) training on their datasets. Building a KM platform isn't something that can happen overnight so there is value in iterating KM solutions early and often, concurrently with other work.


Additional Reading

  • The Knowledge-Creating Company (1991) by Ikujiro Nonaka and Hirotaka Takeuchi

In an economy where the only certainty is uncertainty, the one sure source of lasting competitive advantage is knowledge. Yet, few managers understand the true nature of the knowledge-creating company—let alone know how to manage it.

  • Working Knowledge: How Organizations Manage What They Know (1998) by Thomas H. Davenport and Laurence Prusak

We've observed and analyzed over a hundred attempts to manage knowledge in organizations. To the managers of most of them we've posed the question, "How do you make the distinction between data, information, and knowledge?" Many make no hard distinction in practice, and most of these initiatives involve a mixture of knowledge and information, if not some data as well. Many pointed out that they just tried to add value to what they had -- to move it up the scale from data toward knowledge.


jon@diceregulatory.com

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