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  • Writer's pictureAzhar Kassim Mustapha

Embracing Assertive Knowledge Management (KM): A Shift from Passive Reference-Only KM

Assertive Knowledge Management refers to a proactive and dynamic approach to knowledge management within an organization. It signifies that knowledge is actively harnessed and utilized to drive real-time decision-making, enhance operational efficiency, and contribute to the achievement of strategic goals. This is in contrast to passive or reference-only knowledge management, where knowledge is primarily stored for later reference but not actively applied in operational processes or decision-making. Assertive Knowledge Management emphasizes the active use and application of knowledge as a valuable asset within an organization.

Assertive KM has transitioned from being a static reference tool to an active, real-time approach integrated into operational processes. It captures documents in the process flow, ensuring that knowledge management is seamlessly integrated into existing workflows, rather than creating new processes. This proactive integration harmoniously embeds KM into running systems, actively contributing to decision-making and problem-solving.

One of the key innovations of this assertive KM is building knowledge models from operating documents. These knowledge models are not static references but active resources that can be pushed to be used in similar or related cases by seamlessly integrating them into running systems. This dynamic integration ensures that knowledge is actively leveraged to support real-time decision-making and operational excellence.

Additionally, the incorporation of automated reasoning has become a cornerstone of assertive KM. These automated reasoning capabilities are born from the knowledge models and are used to autonomously execute processes that involve reasoning, such as impact analysis, assessment, compliance checks, and recommendations. This not only enhances the efficiency of decision-making but also reduces the margin of error and promotes consistency across operations.

Assertive KM actively harnesses the intellectual capital and information resources within an organization to fuel innovation, enhance decision-making, and boost overall productivity. It goes beyond being a repository of knowledge and becomes a dynamic, integral part of an organization's daily operations, actively contributing to real-time decision-making and operational excellence. This evolution aligns KM with the fast-paced, data-driven, and digitally transformed environment of modern businesses, where knowledge isn't just stored; it's a driving force behind strategic decisions and operational efficiency.

The traditional understanding of KM, highlights its key characteristics such as digital transformation, collaboration, data analytics, and personalization. However, the contemporary landscape emphasizes that the true power of assertive KM lies in its ability to actively contribute to real-time decision-making and operational efficiency.

Zygy Knowledge has been pioneering Assertive Knowledge Management from the beginning with its Automated Reasoning AI Technology.

The Transformative Role of Technology in Achieving Assertive Knowledge Management

Technology plays a pivotal role in achieving Assertive Knowledge Management (KM) within organizations by enabling the efficient and effective management of knowledge assets and their active utilization in real-time decision-making, enhancing operational efficiency, and incorporating automatic reasoning to derive insights and recommendations from data.

Here's how technology supports Assertive KM:

1. Knowledge Transformation from Any Source

  • Technology now has the capability to gather knowledge from diverse sources and organize it into structured or semi-structured formats. For example, we can convert PDFs and Word documents into HTML, RDF, or OWL formats. We can also transform spoken audio into written text, which can then be structured in HTML, RDF, or OWL.

  • Additionally, we use Robotic Process Automation (RPA) to act as a bridge that helps access knowledge from various sources.

  • Additionally, for efficient storage and retrieval, technologies like MongoDB (a database) and Content Delivery Networks (CDN) come in handy.

2. Building Foundational Knowledge from Knowledge Transformation

  • Technology now can accumulate essential knowledge from a variety of sources, such as standards, compliance documents, Key Performance Indicators (KPIs), risk guidelines, policies, best practices, and more. This knowledge serves as the foundation upon which we can build our understanding and capabilities.

  • The foundation knowledge is represented by advanced technologies such as Natural Language Processing, Large Language Models, and AI-based Knowledge Graphs to learn crucial knowledge from sources like standards, compliance documents, KPIs, risk guidelines, and best practices.

3. Automated Reasoning from Foundational Knowledge for Analysis, Assessment and Prediction

  • The next step is to embark on automated reasoning technology to analyze, evaluate, and make projections. This technology employs AI algorithms, data models, and computational resources to interpret the foundational knowledge we've gathered. It provides valuable insights and forecasts based on this knowledge.

4. Integrating Reasoning into Current Systems

  • Beyond analysis and prediction, the technology has the ability to seamlessly integrate with existing systems, becoming an active, real-time component within your operational processes. This integration ensures that reasoning and insights are not isolated but actively contribute to your day-to-day operations.

5. Natural Language Query and Application into new cases

  • Empower users to query the gathered knowledge in natural language. Furthermore, users can apply this knowledge to new cases based on the reasoning provided, making the knowledge dynamic and actionable.

Ensuring Assertive Knowledge Management practices remain adaptable to evolving business needs

Evidence-Based Decision-Making:

Make informed decisions by conducting structured experiments. For example, create 100 queries related to critical tasks and assess the quality of responses from your Knowledge Management (KM) system

Continuous Learning and Enhancement:

Foster a culture of continuous improvement. Regularly assess and refine your KM processes, akin to a skilled driver refining their driving skills. Evaluate the effectiveness of your technology and its alignment with evolving organizational needs.

Effective Change Management:

Implement robust change management strategies to ease the adoption of new technologies and practices. Clearly communicate the benefits and reasons for change to gain employee support.

User-Centric Approach:

Actively gather user and stakeholder feedback to understand evolving needs and preferences. Employ surveys, focus groups, and direct communication to collect valuable input.

Scalability as a Priority:

Deploy KM solutions that easily accommodate growth and changing requirements without the need for a complete overhaul.

Integration and Interoperability:

Ensure that KM systems are designed for seamless integration with other enterprise tools. Promote interoperability to harness the full potential of diverse technologies.

Catalyzing Assertive Knowledge Management: A Case Study with Zygy

Automated ESG Assessment:

In the realm of Environmental, Social, and Governance (ESG) assessments, Zygy excels by incorporating ESG standards from regulatory bodies such as Bank Negara and the Association of Islamic Banking and Financial Institutions Malaysia (AIBIM) into the Knowledge Tree. With each new Sustainability report submitted by a company, the system efficiently captures and synthesizes information aligned with specific ESG criteria. It then proceeds to perform ESG assessments, ensuring that organizations can proactively meet and exceed ESG standards with clarity and precision.

Contract Compliance Intelligence:

In the realm of contract compliance, we've harnessed cutting-edge technology for Advanced Data Extraction and Knowledge Correlation, effectively distilling essential information from existing contracts. This knowledge is seamlessly integrated into our Knowledge Tree, which embodies compliance criteria derived from contract terms. With every new progress report on a contract, our AI-driven system generates compliance reports, ensuring that actions align with contractual commitments for enhanced governance and risk management

Impact Analysis of Research Reports:

Zygy addresses the pivotal task of evaluating the impact of research reports within organizations. We initiate this process by establishing a bespoke impact taxonomy, finely tuned to the organization's unique objectives. Members of the research team contribute by uploading their research write-ups and findings. The system then produces insightful analytics charts, offering a comprehensive view of the impact of research activities, thus enabling organizations to make informed, data-driven decisions and optimize their research initiatives.

HR Reskilling Excellence:

In the domain of Human Resources (HR) reskilling, we've automated the construction of a comprehensive Job Capabilities and Skills Knowledge Tree, drawing from an array of capabilities and skills materials. This robust foundation then underpins our Skills Gap Analysis, enriched by the insights gleaned from the Job Capabilities and Skills Knowledge Tree. Subsequently, our system tailors learning paths and assessments for each team member, optimizing the reskilling process for organizational effectiveness.

Knowledge Extraction from Software Development

In the realm of extracting knowledge from software development, where coders craft intricate source code using languages such as Java, Python, and Vue.js, we've introduced a streamlined process.

  • Efficient Documentation Integration:

We've designed and implemented scripts that seamlessly integrate the documentation generated by coders into Zygy system. This process ensures that every piece of code tells a story, and we've made it accessible for inquiries in natural language. Users can effortlessly pose questions about the narrative behind the code and receive human-like responses.

  • Impact Taxonomy for Software Development:

Alongside the documentation integration, we've introduced an Impact Taxonomy that brings forth the power of analytics. This taxonomy is finely tuned to provide insights into various facets of software development issues. It allows organizations to delve deep into the impact of software development activities on their projects, helping them make informed decisions and optimize their development strategies.

In this manner, Zygy bridges the gap between code and comprehension, enabling users to explore the intricacies of software development and harness the insights required to excel in this ever-evolving domain.

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