Data science is based on the idea that raw data can be turned into information that can be used for decision-making. However, before machine learning algorithms can predict things or visualizations can show trends, data must be able to be understood, structured, and connected. This fundamental process is called data modeling and serves as the foundation of meaningful data science.
Without data modeling, even the best analytics or AI can result in misleading conclusions. Let’s discuss what data modeling is, why it is important, and how it drives every level of modern data-based decision-making.
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What Is Data Modeling?
Data modeling is the method of structuring data to be organized, stored and accessed. It provides the logical relationships between any data point, essentially being a design document that allows the data to accurately and efficiently flow through the system.
A data model is the bridge between technical implementation and business needs. It allows data scientists, analysts, or engineers to speak the same language when defining what the data is and how it will be leveraged.
Simply put, a data model answers questions like:
- What entities (objects) do we need to capture?
- How are those entities related?
- What attributes must we describe each entity by?
- How should this data be structured for analytics and storage?
The Different Types of Data Models
To understand the role of data models in data science, it’s helpful to understand the three main layers of data models:
1. Conceptual Data Model
The conceptual model provides an overview of what data is available and how it is related. The conceptual model includes only business entities without regard for the implementation of databases.
Example: Customer -> orders -> Product.
2. Logical Data Model
The logical model goes beyond the conceptual model by defining attributes, relationships, and constraints. The logical model acts as the blueprint for building databases and protecting the integrity of data.
Example: Each Order must have one Customer ID and can have multiple Product IDs.
3. Physical Data Model
The physical model puts everything into actual database structures, tables, primary keys, indexes, and paths for storage. This is what engineers implement in SQL, NoSQL, or data warehouse environments.
Each layer builds on the previous layer so that the end database is an accurate reflection of the business logic and can be used for any type of analysis.
Why Data Modeling Is the Backbone of Data Science
1. Ensures Data Quality and Consistency
If the data is poor, then the insights are poor. Data modeling organizes data, specifies validation rules and data relationships, so there are no duplicates and no broken relationships in datasets. For data scientists, having a clean and reliable dataset means time savings is because they get to analyze the data instead of spend time getting it ready to analyze.
2. Enables Efficient Data Analysis
Data that is modeled well saves time as queries or algorithms will run faster and more efficiently. Once the relationships in the data are clear, very fast and accurate insights can be gleaned.
3. Provides a Link Between Business and Technology
Data modeling can link data to a business operation. If insights are solely based on data, they may not always represent what is actually happening in business processes.
4. Provides the Fuel for Machine Learning and AI
Machine learning models are only as good as the data model that aligns with the data. Good, solid data models fit with the input of structured data to feature engineer it best and build predictive machine learning models with great data.
5. Enhances Maintainability and Scalability
Unstructured systems become unusable as data expands, and using a data model provides a set architecture that eases the integration of new sources, provides room for data scale, and aids performance improvements.
6. Assists with Data Governance and Compliance
The growing number of data privacy regulations like HIPAA and GDPR requires an understanding of how data moves and where it resides – and data models permit tracking of data lineage and governance policy enforcement.
Why You Need to Learn Data Modeling
Whether you are a data analyst, data scientist, or future AI professional, if you want to quickly improve your effectiveness, consider learning data modeling. Here’s why:
- It Reduces Your Data Preparation Time – If you consider that up to 80% of the time spent in the data science process is applied to cleaning and structuring data, enhancing data modeling capabilities effectively reduces this effort.
- It Improves Your Analytical Thinking – Building models requires you to analyze the significance of dependencies among data, hierarchies, and ultimately relationships that can sometimes be complicated.
- You Become A More Valuable Professional – Employers today are looking for candidates who understand how to perform the analysis, but they are also eager to find individuals who can help design, manage, and understand data.
- It Enhances Your Ability to Collaborate – When you can communicate (to some degree) using conceptual and logical models, it makes working cross-functionally much easier.
- It Creates Opportunities to Expand Your Role – In data modeling, we found a skill that opens up many opportunities as you move into roles like data architect, data engineer, and analytics designer.
Learning data modeling is not just an additional skill, it is an essential data science competency that impacts all areas of your analytical capacity.
How Data Modeling Fits into the Data Science Workflow
Data modeling occurs early in the data science cycle, most often between data collection and analysis. Here is how it relates to these different aspects of the workflow:
- Data Collection: Specify sources and relations.
- Data Cleaning: Use the model to identify missing or mismatched values.
- Data Integration: Bring together many sources based on a common schema.
- Feature Engineering: Develop meaningful features from the relationships in your model.
- Analysis and Machine Learning: Execute accurate models on verified and organized datasets.
- Visualizations: Build dashboards and reports based on entities and relations in the business.
Basically, data modeling ensures that the entire workflow is built on a strong foundation.
Common Data Modeling Techniques in Data Science
There are several data modeling techniques that are widely used in data science, including:
- Entity-relationship (ER) modeling – Common in relational databases to depict entities and relationships visually.
- Dimensional modeling – Used in data warehouses to manage data and separates data into facts and dimensions for analytics.
- Hierarchical and network models – Useful when you want to depict non-linear relationships; for example, business or social relationships, and the relationship of a supply chain.
- Object-Oriented Data Models – Aligns with modern programming, integrating data with methods and behavior.
- Graph-Based Models – Powers modern applications such as recommendation systems or fraud detection through interconnections.
Depending on the goal of the project, the volume of data, and the system architecture, a specific approach is selected.
The Role of Data Modeling in AI and Big Data
In this generation of AI, big data, and real-time analytics, data modeling is undergoing a transformation from the use of static schemas to the use of semantic models, knowledge graphs, and schema-on-read architectures to manage either unstructured or streaming data.
Yet the concept remains the same, that is, structure drives insight. Even if data is semi-structured (e.g., data stored in JSON or XML), knowing how the data relates or what it means enables machine learning systems to learn better and faster.
Career and Future Outlook
Data modeling is rapidly emerging as one of the most demanded skills in data science and analytics. LinkedIn and IBM have stated that roles requiring expertise in data architecture and modelling have increased 25% in the last 2 years.
Job Types that Involve Data Modeling:
- Data Scientist: To create data pipelines and define a feature set..
- Data Engineer: To design and enhance data architecture.
- Business Intelligence Developer: To create data models that are analytics-ready.
- Data Architect: to manage data assets across the enterprise.
Those who practice strong data modelling skills often move to more senior-level practices in data governance, AI engineering, or enterprise architecture.
How to Learn Data Modeling for Data Science
To become proficient in data modeling requires both theory and practice. Normally, a practitioner will begin with the principles of designing databases, survey entity-relationship modeling first, and then consider dimensional modeling and the architecture of a data warehouse.
If you have an interest in a professional role as a data scientist, the knowledge around data modeling could be your differentiator. It is one of the most overlooked, but a vital part of the toolkit in the design of scalable, reliable, and, most importantly, insight-ready systems of data.
(You may also be interested in our Data Science course, in which you would work on real-world, authentic projects that involve data modeling, data warehousing, and a pipeline of analytics.)
Conclusion
Although data modeling is given less emphasis compared to AI and machine learning, data modeling underpins both of these disciplines. Data modeling establishes data structures, meaning, and context to help restore order out of chaos.
For every successful data-driven organization, data modeling sits at the center to support everything from forecasting to business intelligence dashboarding. For anyone who is serious about data science, the skills in designing, modeling, and managing data are not optional as it is of extreme importance.