In our previous article AI Chatbot for Customer Support: Benefits, Features, and Requirements, we’ve explored AI chatbot relation to other support channels in customer support and help desk software. However, before its effective utilization, the enterprise knowledge must be systematically organized, manageable and functional. In this article, we discuss the concept of enterprise knowledge management. We also identify key challenges and issues that arise in the knowledge integration into modern systems based on the RAG architecture.
The exponential growth in internal data push organization to seek for advanced solutions in their utilization. In order for this data to remain usable, it is necessary to transform it into accessible information and applicable knowledge. Modern software architectures offer solutions for their dynamic search and utilization. However, the successful implementation in business environment depends on understanding the nature, structure, sources, and distribution of knowledge and the data that make it up.
Categorization of Knowledge
Enterprise knowledge consists of vast amounts of diverse information, including: technical documentation, internal wikis, corporate policies, customer support records, historical reports, and more. Organizations are under constant pressure to organize, find, and integrate knowledge to accelerate innovation, improve customer satisfaction, and meet regulatory requirements.
Enterprise knowledge also has a cumulative nature, representing a collection of previous experiences and the results of procedures used. Eventually, it becomes an important business resource that is used daily to make decisions, execute operational processes, and support learning within the company.
The comprehensive knowledge generated within a company can be divided into two main categories:
- Explicit. Represents a knowledge that is easy to articulate, formalize, record, and share. It is found in clear formats such as manuals, databases, and official documents. Computer systems usually easily access this type of knowledge.
- Tacit. Knowledge rooted in the personal experience, practice, skills, and intuition of employees. It is difficult to formalize, so it often remains unused or “locked up” in informal communications, archives, or previous projects.
Regardless of the category, the primary condition for its effective use requires careful management, including planning, organization, and control. The main goal is to increase the overall productivity of employees, facilitating their access to the necessary information and reducing the risk of its loss.
The Knowledge Management (KM) represents:
- A formal discipline that deals with the collection, structuring and retrieval of organizational (enterprise) knowledge to support continuous learning and decision-making in the company.
- The process of identifying, organizing, storing, sharing, and using knowledge to ensure that the right information reaches the right people at the right time.
The overall goal of the technological foundations of the KM system is to enable easy and accurate retrieval of information, sharing knowledge within teams and departments, and preserving enterprise knowledge for future generations of employees.
Traditional knowledge management systems have relied heavily on rigid taxonomies, keyword-based searches, and manual searches by people who maintain knowledge bases. Common solutions have relied on systems that store articles organized into categories. This approach is unable to keep up with the rapid growth of information, though. Knowledge bases often become incomplete and difficult to manage, frustrating users who cannot quickly find answers to their questions. To identify ways for KM improvement, it is necessary to recognize the various structures of data and information.
Structures of Data, Information, and Knowledge
Enterprise knowledge comes in a variety of forms and structures. According to the way the data is organized, it can be divided into three basic categories:
- Structured: Highly organized data that follows a strict, predefined schema. Data is most often found in relational databases (e.g., PostgreSQL or Oracle) following structured tables, where rows and columns clearly define attributes and values. This category also includes Knowledge Graphs that structure entities and their explicit relationships in the form of nodes and edges.
- Unstructured: Represents the majority of all enterprise knowledge. In this form, data does not have a consistent structure, does not follow any schema, and varies greatly in format. Examples include free text, PDF documents, technical manuals, corporate reports, interview transcripts, and multimedia records. Due to its inherent variability and complexity of content, its straightforward search and interpretation present a major challenge.
- Semi-structured: Occupies a middle ground between structured and unstructured formats, possessing certain organizational elements and hierarchy, but not following a fixed relational schema. Examples include JSON and XML files, and email messages. A specific example includes HTML that forms a basic element of web pages, mixing structured components (tags and attributes) with free text. These data present a specific challenge for extraction and integration into knowledge bases.
Management of enterprise knowledge with data stored in different structures is a very complex task, accompanied by numerous infrastructural and operational problems. A particular problem is that organizations are usually flooded with massive amounts of unstructured data, which traditional management methods have difficulty managing.
Knowledge Sources and Distribution
As mentioned above, enterprise knowledge is heterogeneous and largely decentralized across different formats and applications. An additional challenge is that knowledge is not stored in a single unified format, but encompasses a wide range of unstructured and semi-structured data expressed in explicit or implicit form.
Additionally, enterprise knowledge is often not centralized, but is deeply fragmented across multiple and diverse sources within the company that are distributed through different channels. Information can be scattered across wikis, APIs, management systems, various heterogeneous databases, and more.
Common knowledge sources include:
- General business documents, project requirements, and corporate procedures, typically stored in Content Management Systems.
- Operational issues, fault histories, and architectural decisions are recorded in support ticketing systems and customer support platforms, such as SaaS Help Desk.
- Technical knowledge, which usually contains API documentation written in Markdown format and data definitions within a relational database.
- Implicit data generated through informal collaboration channels. Examples include email messages, Slack or Teams messages, multimedia records, and files stored in local or cloud storage systems.
In enterprise organizations, different teams and departments maintain their own, specific systems, creating fragmented information across the organization that is offered through different distribution channels. For example:
- IT and engineering departments may use support ticketing system, specific technology documentation, and technical knowledge bases.
- Finance departments rely on highly confidential regulatory records, spreadsheets, vendor and budget data.
- Legal and HR departments often have sensitive contracts, corporate policies, compliance procedures, and many other sensitive information.
The reason for this distribution of knowledge across departments is that this data is domain-specific and separated by strict boundaries due to its purpose, context, and the specific organizational structure of the company.
Challenges in Knowledge Retrieval and Acquisition
In a modern corporate environment, knowledge is an extremely dynamic, extensive, and complex resource. Enterprise systems that support corporate governance are also characterized by a high degree of complexity. Data is constantly generated, stored and updated independently within different technological and domain frameworks.
The process of finding information in such an environment is made difficult by the fact that the data is large and diverse. Different teams and departments within the same company often maintain their own, separate knowledge bases and applications. The result is the emergence of redundant information stacks, following inconsistencies between versions of the same content. Users are therefore forced to manually search and navigate through multiple completely unrelated systems to compile a complete answer and consolidate the accurate information needed for their work.
A common example is when a software engineer who has just joined a customer support team discovers discrepancies in technical instructions and documentation. Due to a delay in updating, the knowledge presented in the development environment differs from that valid for the production environment. In addition, there is no record that explains these differences and connects them.
Alongside the problem of finding relevant content, there is also the issue of assessing the credibility of the information found. Due to the aforementioned problems, finding relevant information in a large set of dispersed data becomes difficult and demanding. Traditional tools do not have the capacity to aggregate multi-layered knowledge into a coherent whole. Therefore, the introduction of advanced solutions is required, such as semantic search including RAG solutions that enable modern AI chatbots.
