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

Isometric illustration of data modeling with data lakes, OLTP systems, data marts, OLAP cubes, structured and unstructured data, and a BI professional working at a laptop

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.

Source systems include:

  • Data lakes: store large amounts of raw data in its original format.
  • OLTP databases: optimized for fast data processing and transactions.

Target systems include:

  • Data marts: subject‑oriented subsets of data warehouses.
  • OLAP databases: designed for analysis and multi‑source querying.

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 support:

  • ETL/ELT pipelines
  • Data governance
  • Reporting and dashboards
  • Advanced analytics

A solid understanding of data modeling is essential for BI professionals.

Section 4: Differentiating Structured and Unstructured Data

Structured data is organized in rows, columns, and predefined schemas, making it easy to query and analyze.

Unstructured data lacks a predefined structure and includes formats such as text, images, audio, and video.

Recognizing the distinction between these data types is vital when designing data models to ensure efficient storage and retrieval.

Section 5: Techniques and Tools for Data Modeling

Various techniques and tools are available to create data models and support the data modeling process.

Popular modeling techniques include:

  • Hierarchical modeling: tree‑like structures for parent‑child relationships.
  • Entity‑relationship modeling: entities, attributes, relationships, and domains.

Common tools include:

  • erwin Data Modeler
  • Power BI (semantic models)
  • SQL Server Management Studio (SSMS)

These tools help design, visualize, and maintain data models.

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