According to Webster’s Dictionary, knowledge is “the fact or condition of knowing something with familiarity gained through experience or association.” In the context of business enterprises, we see that knowledge tends to be interpreted as possession of experience (tacit knowledge) as well as possession of factual information (explicit knowledge) – or where to get it.
The difference between data, information, explicit and tacit knowledge
Nowadays, everyone is talking about the Internet of Things or the Industrial Internet of Things and Big Data. Common for both is that they are all about data and information but not so much about knowledge. So why is this so important? It’s crucial to understand the difference between data, information, and knowledge – especially when capturing knowledge from the experts in a computer system using Causal AI, and it’s essential to understand the kinds of knowledge we are trying to formalize.
Explicit knowledge can be expressed in words and numbers, and easily communicated and shared in the form of hard data, scientific formulae, codified procedures, or universal principles and general rules.
Data is unorganized and unprocessed facts, whereas information can be considered an aggregation of data-processed data. For example, the number 32 is data, but it means little to us before we can contextualize it. A thermometer might read 32 degrees – and suddenly, the number makes sense. However, we gain knowledge by interpreting this in a larger context and applying our pre-existing knowledge and information. In this case, we know that water will freeze at 32 degrees, which could be why our sprinkler system is not working correctly.
The kind of knowledge that cannot be expressed easily in words and numbers is referred to as tacit knowledge (Nonaka & Takeuchi). It is not easily visible and expressible, and it is highly personal and hard to formalize, making it difficult to communicate or share with others. Subjective insights, intuition, and hunches fall into this category.
Tacit knowledge is what most organizations call “know how” – skills and expertise gained over many years of experience.
Add Context
So why is this important for knowledge management systems? It’s crucial because if we don’t appreciate that we must apply our pre-existing knowledge and put all the data and information into a context to interpret it correctly using our know-how, we will end up with an Information Management System – scattered information where users are left to their own devices to search through endless manuals, flow-charts or wiki-type systems and each one left to interpret this the way they can. Contextualization is extremely important for transforming massive amounts of data collected through IoT into accurate structured information that we can learn from and generate knowledge that we want to capture and share using Causal AI.
Inherently difficult to transfer knowledge
Most knowledge management solutions fail to capture much more than the tip of the iceberg in that they capture only explicit knowledge using article-based approaches or simple workflows.
We need to recognize how difficult it is to transfer knowledge from one person to another, and we need to understand the importance of tacit knowledge when taking a more holistic approach to process all the data and information we have to truly call it a knowledge management system or – God forbid – an expert system.
Capturing knowledge in a computer is difficult, as is making it accessible and valuable to others. Still, it’s precisely in this transformation from tacit to explicit and formalized knowledge using Causal AI that we can realize our valuable organizational knowledge and improve efficiency and quality.
Working with enterprises, we see how organizations are beginning to treat their accumulated structured knowledge as an asset and how they are starting to build knowledge management strategies and applications and the massive value it provides throughout the organization – next time, we will focus on capturing and applying knowledge such that others can use it in a useful and meaningful way and such that it provides value to other services.
Frequently Asked Questions
How can organizations effectively capture tacit knowledge from employees before it is lost, especially considering the challenges of workforce turnover?
Capturing tacit knowledge from employees, especially in the face of turnover or retirement, is a complex challenge that organizations face. One effective approach involves creating a culture that values knowledge sharing, encouraging employees to share their insights, experiences, and expertise through formal and informal channels. This can be facilitated through community of practice programs, where experienced employees are paired with newer ones, and through collaborative tools and platforms that allow for sharing experiences and best practices. Storytelling sessions, where employees share their experiences in solving specific problems or dealing with specific error codes in various products, can also be a powerful way to transfer tacit knowledge. The most important aspect of these methods is to support them through a rigorous process that facilitates knowledge elicitation.
In Dezide, we support this process using our Knowledge Value Chain, where capturing knowledge is the essence of the first phase, named Acquisition. Read about the Acquisition phase here: https://www.dezide.com/knowledge-value-chain-acquisition-unlocking-knowledge-source/
What are the specific technologies or tools that can aid in the transformation of tacit knowledge into explicit knowledge?
Various technologies and tools can support the transformation of tacit knowledge into explicit knowledge. Knowledge management systems and troubleshooting platforms play a crucial role in this process, providing a structured way to document, store, and retrieve knowledge. These systems often include features like information search, static procedure management, databases for storing structured information, guide step-by-step troubleshooting experiences and collaboration tools that support the sharing and discussion of knowledge. Artificial intelligence and machine learning can also assist in identifying patterns and insights from large volumes of data, which can then be documented, structured, validated, and shared, adding tacit knowledge from experts. Social networking tools within an organization can facilitate informal knowledge sharing. At the same time, specialized software for capturing and analyzing decision-making and troubleshooting processes can help make tacit field service knowledge explicit, precise, and validated.
Dezide has 20 years of experience in capturing, organizing, and optimizing expert troubleshooting knowledge through patented AI troubleshooting technology driven by Bayesian Belief Networks, ensuring transparent and scalable troubleshooting that will reduce troubleshooting time, increase first-time fixes, transfer skills to new employees, and maximize skill-flexibility.
Learn more about Dezide and our troubleshooting technology in our white paper: https://pages.dezide.com/capture-troubleshooting-knowledge-baeysian-networks
Can you provide examples of organizations successfully implementing knowledge management and troubleshooting systems and what benefits they have realized?
We have worked with several organizations successfully implementing troubleshooting technology and have seen significant benefits. For example, Siemens Gamesa Renewable Energy proved that their technicians troubleshoot much faster using Dezide technology. In testing, common errors were deliberately introduced into a wind turbine. Junior technicians using Dezide troubleshooting software fixed the errors in under 15 minutes, while senior engineers took over an hour using traditional methods. Siemens Gamesa is seeing the software resolving complex issues around 70% faster and simpler problems 40% more efficiently. The benefits are enormous, with more than 4000 field service technicians in the field.
Heavy equipment manufacturer Liebherr has also leveraged advanced troubleshooting software to capture the insights and expertise of their field service experts, enhancing their ability to deliver value to clients through a comprehensive database of best practices and optimized procedures. Currently rolled out to four different product divisions, including earthmoving, cranes, and mining, Liebherr is retaining knowledge, improving customer satisfaction, and realizing new revenue streams by offering the solution to customers.
Particle Accelerator Technology Company IBA (Ion Beam Applications) has seen an average reduced troubleshooting time of 33%. Still, the technicians solving these issues more efficiently are not experts in this area. For IBA, improved skill flexibility is the real power of the troubleshooting software, as it enables more technicians to work on products from different product lines – all while appearing professional and competent to the customer.
These organizations have seen benefits, including improved decision-making processes, faster problem-solving, reduced redundancy in work, and enhanced innovation by leveraging the collective knowledge of their employees.
About Dezide
We have 20 years of experience helping businesses of all sizes capture, organize, and optimize expert knowledge using Causal AI. Our clients range from the world’s largest enterprises in the wind industry, mining sector, and air compressors to consumer printing and telecom.
Get in touch and see why they trust Dezide to build brilliant knowledge bases powering the world’s best service organizations.
