The Role of a BI Professional: Understanding Data Modeling,Types of Data Systems,Differentiating Structured and Unstructured Data

 Section 1: Understanding Data Modeling

Data modeling is the process of creating a visual representation of an entire information system or its components, establishing connections between data points and structures. Its goal is to illustrate the types of data used and stored within the system, their relationships, grouping and organization methods, as well as their formats and attributes. By modeling data, businesses can gain valuable insights and facilitate effective decision-making processes.

Section 2: Types of Data Systems Data systems consist of source systems, where data is imported and exposed, and target databases, where data is acted upon. Examples of source systems include data lakes, which store large amounts of raw data in their original format until needed, and OLTP (Online Transaction Processing) databases optimized for data processing. Target systems, on the other hand, may include data marts (subject-oriented databases that can be subsets of larger data warehouses) and OLAP (Online Analytical Processing) databases, designed for analysis and capable of querying data from multiple sources. Understanding these systems is vital for effective data modeling.

Section 3: The Role of a BI Professional In the realm of Business Intelligence (BI), professionals are responsible for creating the target database model. Their role encompasses organizing systems, tools, and storage, including the design of how data is structured and stored. These foundational systems play a crucial role in key BI processes and serve as a basis for building subsequent tools and functionalities. As such, a solid understanding of data modeling is essential for BI professionals.

Section 4: Differentiating Structured and Unstructured Data

Data can be broadly categorized as structured or unstructured. Structured data is organized in easily identifiable formats, such as rows and columns, making it readily accessible and analyzable. Unstructured data, on the other hand, lacks a predefined structure, making it challenging to organize and analyze. Recognizing the distinction between these data types is vital when designing data models to ensure efficient storage and retrieval of information.

Section 5: Techniques and Tools for Data Modeling Various techniques and tools are available to create data models and support the data modeling process. These have evolved over time with advancements in database concepts and data management. Some popular data modeling techniques include hierarchical modeling, which represents data relationships in a tree-like structure, and entity-relationship modeling, which focuses on entities, relationships, attributes, and domains. Additionally, tools like erwin Data Modeler and Power BI can provide valuable support in designing and visualizing data models