Data and Knowledge Management
Information Management is a regulatory or organizational technique that aims to make information easily searchable and reusable by users. The key processes involved in information management include information gathering, processing, repackaging, and retrieval.
On the other hand, Knowledge Management is a technique for creating a learning environment in which people continually learn, use existing information, and are willing to share their new knowledge. Knowledge management processes include learning (individual, organizational, and collaborative) and knowledge sharing.
The foundation of knowledge management is the idea that pure knowledge exists in every human mind and thought. Therefore, it is essential to establish mechanisms to disseminate information and experience from existing human resources to increase the knowledge of each actor in the organization.
Now, let’s delve into Data Management:
A. Data Management
a. Data Importance: Data is increasingly recognized as a corporate asset that can be used to inform business decisions, enhance marketing campaigns, optimize operations, and reduce costs. Proper data management is crucial, especially in the face of regulatory requirements like GDPR and privacy laws such as the California Consumer Privacy Act.
b. Data Management Function: The data management process encompasses data processing and storage, controlling data formatting, and its use in operational and analytical systems. Databases, particularly relational databases, are commonly used for storing corporate data. Database management is central to data management, including setting up, monitoring, tuning, and maintaining databases. Other fundamental data management disciplines include data modeling, data integration, data governance, data quality management, and master data management.
c. Data Management Techniques:
i) Database Management Systems (DBMS): Relational DBMS is the most typical type, utilizing primary and foreign keys to link related records across tables. Non-relational databases (NoSQL) are emerging options for various data workloads.
ii) Big Data Management: NoSQL databases are often used in big data environments due to their ability to handle diverse data types.
iii) Data Warehouse and Data Marts: Data warehouses and data marts are used for managing analytics data. Data warehouses are typically based on relational or columnar databases and store structured data. Data marts are smaller subsets of data warehouses catering to specific departments or user groups.
iv) Data Governance: Data governance is primarily an organizational process that involves setting policies and procedures for data consistency and quality. Data management professionals may oversee governance programs, but senior management typically makes decisions about data policies. Data governance also involves monitoring data and ensuring compliance with data policies.
B. Knowledge Management
Knowledge management is a set of tools, methods, and strategies used to analyze, maintain, share, improve, and organize a company’s information. Its purpose is to enhance company efficiency and store knowledge that can benefit all parts of the organization regarding operational and business activities.
a. Knowledge Management Cycle: The knowledge management cycle consists of creating, capturing, refining, storing, managing, and disseminating knowledge.
b. Pros of Knowledge Management:
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- More Efficient Business Decision Making: Knowledge management enables more efficient and informed decision-making by providing access to different perspectives and experiences.
- Easier Access to Knowledge and Information: It facilitates easier access to information and individuals with the required knowledge.
- Efficiency in Each Operational Unit: It streamlines knowledge access, making it faster for all parts of the organization.
- Quickly Create Change and Innovation: Knowledge management supports innovation and change by providing insights from various elements of the company.
Both information and knowledge management are crucial for organizations seeking to leverage their data and knowledge assets effectively.
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