How do you use data modeling in real-world scenarios?

1 Business Intelligence

Business intelligence (BI) is the use of data analysis tools and techniques to transform raw data into meaningful insights that support decision making and strategic planning. Data modeling is essential for BI, as it helps you organize, integrate, and standardize data from various sources and formats, and create a logical and consistent data warehouse or data mart that serves as the foundation for BI reporting and analytics. Data modeling for BI typically involves three levels: conceptual, logical, and physical. Conceptual data modeling defines the high-level business concepts and requirements, logical data modeling describes the detailed attributes and relationships of the data entities, and physical data modeling specifies the technical implementation and optimization of the data structures in the database or system.

2 Data Engineering

Data engineering is the discipline of building and maintaining the data infrastructure and pipelines that enable data collection, processing, storage, and distribution. Data modeling is a key skill for data engineers, as it helps them design, develop, and test scalable, reliable, and efficient data solutions that meet the needs and expectations of the data consumers and stakeholders. Data modeling for data engineering often involves using tools and frameworks such as ER diagrams, UML diagrams, SQL, NoSQL, ETL, Hadoop, Spark, and Kafka to create and manage data models that support various data types, formats, sources, and use cases.

3 Data Science

Data science is the field of applying scientific methods, algorithms, and techniques to extract knowledge and insights from data. Data modeling is a crucial step for data science, as it helps you prepare, clean, and transform data into a suitable format and structure for analysis and modeling. Data modeling for data science also helps you explore, understand, and visualize the data, identify patterns and anomalies, and select the appropriate features and variables for building and evaluating predictive or prescriptive models. Data modeling for data science often involves using tools and languages such as Python, R, pandas, numpy, scikit-learn, TensorFlow, and PyTorch to manipulate and model data.

4 Data Governance

Data governance is the process of establishing and enforcing policies, standards, and best practices for managing the quality, security, and availability of data across an organization or system. Data modeling is a vital component of data governance, as it helps you define and document the data elements, metadata, lineage, ownership, and lifecycle of the data assets, as well as the rules and constraints that govern their usage and access. Data modeling for data governance also helps you monitor and audit the data quality and compliance, identify and resolve data issues, and ensure data consistency and integrity. Data modeling for data governance often involves using tools and methodologies such as data dictionaries, data catalogs, data quality frameworks, data lineage diagrams, and data stewardship roles to create and maintain data models that support data governance objectives.

5 Data Visualization

Data visualization is the art and science of presenting data in a graphical or interactive form that makes it easy to understand, explore, and communicate. Data modeling is a prerequisite for data visualization, as it helps you organize, aggregate, and filter data into a format and structure that can be effectively visualized using charts, graphs, maps, dashboards, or other visual elements. Data modeling for data visualization also helps you choose the right type and level of data to display, as well as the appropriate visual encoding and design principles to convey the message and story behind the data. Data modeling for data visualization often involves using tools and platforms such as Excel, Tableau, Power BI, QlikView, D3.js, and Plotly to create and customize data models and visualizations.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?