Knowledge Management Project – Engaging in Knowledge Lifecycle in an Appropriate Way

Let us learn how an organization can engage in knowledge management and knowledge lifecycle management in an appropriate way. For this, I am assuming a role of a consultant who is designing and discussing these approaches for a hypothetical organization.


AnalyticsNinja is a startup that offers analytics and data science consulting. This project assumes that AnalyticsNinja is willing to implement knowledge management and has hired me for this. In this report, I am discussing different stages of the knowledge management lifecycle.

Model Used: Zack Knowledge Management Model

In this project, I will discuss the knowledge management life cycle as discussed in the Zack Knowledge Management Model.

The Meyer and Zack KM cycle is derived from work on designing and developing information products (Meyer & Zack, 1996). A number of lessons learned from the cycle that physical products follow within an organization can be applied to managing knowledge assets. (Dalkir, K., 2017). In this cycle, the major developmental stages of a knowledge repository are analyzed and mapped to the stages of a KM cycle.

I recommend using the stages mentioned in this model and continually developing a KM model. I recommend building a KM model using the steps below:

  • Acquisition
  • Refinement
  • Storage/retrieval
  • Distribution
  • Presentation/use.

This cycle is also known as the “refinery.”

Zack Model


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Organization Culture

Corporate culture is the crucial factor that helps in proper knowledge and information flow within an organization. If an organization does not have a culture that enables and rewards knowledge sharing, employees will not be motivated to do the needful and participate in knowledge sharing activities.

support from the higher management is extremely important. This can make or break KM initiatives that an organization is willing to engage in. A line of communication should remain open in both upward and downward directions. The KM initiatives will need time and money investment for successful implementation. Therefore, having buy-in from the higher management can prove to be critical in transforming organizational culture to knowledge management culture

What is Knowledge Management?

Knowledge management is a recurring, multidisciplinary, collaborative, and integrated approach to identifying, creating, capturing, organizing, accessing, and disseminating an enterprise’s tangible and non-tangible knowledge assets that enhance organizational memory and productivity.

Today, organizations adopt the KM model to improve organizational memory and efficiency and to save knowledge within the organization. Every organization has policies that standardize the KM process across different verticals and departments. The primary aim is to enable learning within the organization while building a learning culture. In such organizations, sharing knowledge is always encouraged, and those willing to learn to find it easy to do so. Adopting the KM model ensures that specialized knowledge does not leave the organization with employees and remains available within organizational memory.

Implementing and adopting the suitable KM model will help build an efficient workspace. A workspace that enables faster and better decision-making with increased collaboration. Since KM is an ongoing, reiterative process, it helps build an organizational knowledge pool. However, KM requires extensive planning and expertise. Therefore, it is important for an organization to adopt a KM model that suits its size, culture, and requirements. Considering the nature of work, size, geography, and expected end results, I am proposing AnalyticsNinja to use Zack Knowledge Management Model.

AnalyticsNinja is a boutique firm that has the majority of its employees working as consultants at different client locations, with key managerial employees working from their Dallas office. The organization offers consulting services in niche technology that uses hard-to-find skillsets like Adobe Experience Manager, Azure, and RDBS services. Therefore, it is very important for organizations to codify all the knowledge and learnings that employees learn during their interaction with the customer and while they solve unique problems presented to them. The learning of a consultant must be passed on to other consultants and managerial employees. For a consulting firm of its size, it is extremely important to codify the tacit knowledge before it is lost due to time or the departure of the employee from the organization for any reason.

Acquisition of Knowledge

Codification of Tacit Knowledge

Knowledge is not only what the existing documents within AnalyticsNinja hold. Every consultant and employee of AnalyticsNinja holds a niche skillset and they interact with customers each day to solve unique issues which, most of the time, are never discussed in any product documents. Therefore, interacting with employees and encouraging interaction between different teams, and groups is extremely important. The knowledge needs to flow among these. This knowledge flow needs to be documented (codified). This is the acquisition stage in which knowledge is gathered from different sources. Attention should be given to widening the net and capturing as much information and knowledge as possible in this stage. In the next stage, which is refinement, unnecessary pieces of information can be filtered out.

Before we start discussing the challenges faced in the codification of tacit knowledge, let us revisit and understand what tacit knowledge is. Tacit knowledge is knowledge that is difficult to transfer from one person to another through written or verbal mode. It’s the information that, if asked, would be difficult to write down (document), articulate (convey), or present (visual transmission) in a tangible form—for example, swimming. One might know how to swim, but if asked to describe the process, then an individual might struggle. Swimming is far more than just swinging your arms and legs to stay afloat. To be treated as an economic good, knowledge must be put in a form that allows it to circulate and be exchanged. This transformation of knowledge into information is referred to as codification.

Acquisition of tacit knowledge at an individual or group level can be done using three major approaches. However, these approaches bring their challenges making the codification process complicated. Let us understand these three approaches and their associated complexities.

  1. Interview an expert: An expert will have knowledge that s/he has gained during years of working and experience. When one wants to codify this tacit knowledge via interviewing, it is easy to lose critical information because the questions were not thorough enough. This makes codification more complicated. This means people involved in codification should be as good as individuals or groups possessing tacit knowledge. Therefore, increasing the complexity.

This can be understood by looking at SMEs (Subject Matter Experts) at AnalyticsNinja. These are the people who are experts in their domain and have years of experience. Typically, when new recruits have doubts or are stuck in their job, they turn to SMEs for solutions. So, by interviewing SMEs, organizations can codify very important knowledge pieces which otherwise would leave the organization with the exit of SME.

A knowledge manager should often interact with the SMEs to

  1. Learn by doing: One can engage in doing a particular activity and codifying it as the action progresses. However, this is a time-consuming process and may lead to data loss. Moreover, doing is not equal to doing it efficiently and accurately, which makes the codification of tacit knowledge complex.
  2. Learn by observation: This is also a great option to codify tacit knowledge. However, it comes with challenges that make the process complicated. For example, work environment, work location, health hazards, expertise in the topic, availability of tools, budget constraints, etc., make the codification of tacit knowledge complex using this approach.
  3. Story telling: For an organization, the story can be interactions that employees have, events communicated informally, both internal and external, and history of the management’s action shared formally and informally within the organization. The most basic way for an organization to use storytelling is in informal education and training of resources. Once the employee learns the low-level details and procedures, stories present finer points and share expertise in the proper context. Even though the application is pretty sophisticated, the stories shared by colleagues and subject matter experts make understanding far easier than technical product documentation.

It is important for AnalyticsNinja to understand that storytelling fosters the collaboration between different individuals, teams, and groups within an organization. People learn from each other’s experiences and pass on the knowledge and the experience, context, and learnings, which further makes the knowledge transfer process efficient.

Knowledge Manager and his/her own knowledge about subject matter can be a critical success criterion in this phase. If the knowledge manager is unable to understand different key sources of the knowledge, connect them with appropriate resources within an organization, as a result the effectiveness of the KM exercise can be reduced considerably. It is important that all the phases mentioned above are carried out explicitly and all the different individuals and teams communicate effectively with each other.

Refinement of the Knowledge

Knowledge gathered and codified in the acquisition phase needs to be refined, which is done in this phase. Refinement of knowledge will be physical and logical in nature. The physical refinement involves migrating data to electronic format. Converting data to a single data format like HTML pages or PDF files. Moving data from local storage to centralized cloud storage at a defined interval etc. While the logical restructuring involves restructuring of the data that is collected, relabeling, indexing and integrating data into one file from multiple data files.

This step also ensures that the data is cleaned up and standardized. It is important to have content sanitized so that anonymity of the sources and key individuals is maintained. As a part of data standardization, the data is written in a particular template that matches with the organization standards.

The critical success criteria for this phase are adding up to the value by creating easy to comprehend, reuse knowledge pieces which are flexible. Technology plays a critical role in the success of this and all the next steps mentioned here onwards. Having a proper Knowledge Management System (KMS) is extremely important for AnalyticsNinja.

Storage/Retrieval of the Knowledge

AnalyticsNinja must ensure that the curated knowledge is stored in a way that it remains safe as well as easy to retrieve. Because no KMS is useful if users can’t retrieve and consume the data. This phase is key between upstream system that feeds data into the system and downstream system that consumes the content. The storage must be selected in a way that it is universally available to the authenticated users, with guaranteed up-time and back up in place. I strongly recommend organization to opt for cloud based storage solutions like AWS Redshift, S3, Kinesis, and EC2 instances instead of using local storage. These cloud based services also ensures that the data is available 99.99% of the time with right back up solutions to avoid unfortunate incidents like data loss. Please note storage can be physical (files, folders, printed information) as well.

Choosing a scalable, up-to-date solution that is compatible with the other technology stack of the organization is a critical success factor for this phase of the project.

Distribution and Presentation of the Knowledge

I will be discussing two stages of the KM lifecycle together as they are closely associated to each other. The knowledge distribution and presentation is extremely important for the overall success of the KM lifecycle. Distribution refers to how knowledge will be delivered to the end users. Considering the nature of business, I am eliminating usage of legacy methods of distribution like print, fax etc. AnalyticsNinja needs to make use of digital channel of knowledge distribution.

Context plays an important role in the knowledge presentation. A user should not be bombarded with the different pieces of the knowledge unless that piece is relevant to that particular user and for the scenario in which s/he is looking for the information. Technlogy is vital to success of these and having a proper Knowledge Management System is critical for the organization. I am recommending using WordPress as KMS for AnalyticsNinja. KMS will take care of the distribution and presentation phase of this project.

Knowledge Management System (KMS)

To be considered a successful KM technology, I evaluated WordPress on these criteria: content management, content organization, content dissemination, content discovery, and social media amplification. WordPress is a globally leading content management system. It has the best yet easy-to-use content management tools available, which are not resource heavy. One can start with WP using a shared hosting environment and scale as the demand grows. The distribution part is taken care as content is easy to access over the web with the help of browsers and no specialized tools or softwares are needed to distribute the content.

Since the content is consumed in browsers the presentation is responsive and user friendly. KM managers can make use of multiple types of media formats like text, images, audio, video, animations etc.

  • AI and ML uses for content recommendation.
  • Data sharing using social aspects
  • Content syndication.


In this project we discussed how AnalyticsNinja can engage in Knowledge Lifecycle in an appropriate way. Beginning from org culture, management buy-in to stages mentioned in the model.


Dalkir, K., (2017). Knowledge management in theory and practice. Massachusetts Institute of Technology.

Models of KM Cycle

Meyer, M., & M. Zack. (1996). The design and implementation of information products. Sloan Management Review, 37(3), 43–59.

Kimble, C. (2013). Knowledge management, codification and tacit knowledge Information Research, 18(2) paper 1

Knowledge capture and codification. Available at:

Knowledge Management: Importance, Benefits, Examples [2021]

Knowledge Management Systems, The Ultimate Guide:


What is Tacit Knowledge? Available at:, accessed on 30 Jan 2023

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