In an era where data is often called the “new oil,” it’s imperative to understand the tools and techniques harnessing its potential. Data visualization and modeling stand out as twin titans. However, their distinct roles often lead to confusion. This blog will demystify the realms of data visualization and data modeling.
Data visualization is the graphical representation of information and data. On the other hand, data modeling involves the creation of abstract representations of the data structure.
At the crossroads of these two concepts, a million-dollar question arises–how do they differ, and how can they work together to bring out the best in data? Continue reading to find out.
What Is Data Modeling?
Data modeling is like creating a blueprint of an information system, or parts of it, to illustrate the connections between different data points and structures. It’s all about identifying the kinds of data in the system, their relations, formats, and features, and how they can be neatly grouped and organized.
This process is based on the business’ needs. The initial steps involve gathering business needs directly from stakeholders and end-users. These needs, along with specific rules and requirements, are then transformed into data structures, which pave the way for designing a solid database. In today’s world, data modeling is used everywhere, from banks to hospitals.
Types of Data Models
The three most common types of data models are relational, dimensional, and entity-relationship (E-R). There are two advanced models—Object-Oriented Data Model and Dimensional Data Model. Besides these, there are a few others, like hierarchical, network, object-oriented, and multi-value, but they aren’t as widely used.
The type of data model you choose sets the stage for how data is stored and organized and how you can retrieve it.
- Relational
Think of the relational model as an old-school but popular approach. It organizes data into tables with rows and columns. Imagine you have data on mobile phones. Your basic elements are the ‘type of phones’ and ‘number of phones.’ ‘Type’ is a dimension—like the description or location, and ‘number’ is a measure used for math stuff like adding or averaging.
These tables are called ‘relations,’ the columns are ‘attributes,’ the rows are ‘tuples,’ and the ‘domain’ is the group of values a column can have. You can link tables using common data elements (called keys). Like, you can relate phone types to their prices or suppliers.
- Dimensional
This model adapts to how a business uses data. It’s designed for faster online queries and is excellent for data warehouses. Imagine you’re keeping track of apple sales. The number of apples sold is a ‘fact,’ and the info around it (like apple type, price, and sale date) are ‘dimensions.’
A fact table is the main table in a dimensional model. It’s fast to get data for a specific activity, but analyzing the data might need some extra work since it doesn’t link everything together like the relational model.
- Entity-Relationship (E-R)
E-R models are like visual diagrams showing how different business data are connected. They’ve got boxes for activities or ‘entities’ and lines, which show how these boxes are linked, or the ‘relationships.’ This diagram helps in building a relational database. For example, every row might represent a phone, and the fields in that row have attributes like type and color. Key data elements tie tables together.
- Object-Oriented Data Model
This more advanced model uses objects as the fundamental structure for defining and describing data, as opposed to the entity-relationship model, which represents data in terms of entities and their relationships.
Objects mirror real-world entities and comprise various attributes. For instance, as an object, a customer would have attributes like name, address, phone number, and email address. In an entity-relationship model, these attributes are stored in different tables to establish relationships between these tables.
- Dimensional Data Model
Dimensional data models are the backbone of business intelligence (BI) and online analytical processing (OLAP) systems. Primarily used in data warehouses for maintaining historical transactional data, you can also utilize these models for smaller datasets.
The dimensional data models include several structures, like fact tables, dimension tables, and lookup tables, which together form the foundation for creating enterprise data warehouses and online transaction processing systems.
The principal goal of a dimensional model is to facilitate rapid responses to business-related queries about forecasts, trends, and more. By providing an organized approach for business intelligence reporting, dimensional modeling enhances cross-departmental information sharing, fostering efficient collaboration and decision-making within an organization.
Role of Data Modeling in Data Analytics
Data modeling is key for effective data analytics. It ensures that data is structured, organized, and understood, making extracting meaningful insights from the data easier. For example, data modeling helps in:
- Designing Databases: Data modeling helps create the structure of databases, including the development of schemas, which outline the organization of data in a database, the types of data you can store, and how these different data elements are related. This forms the foundation for efficient data storage and retrieval, essential for data analytics.
- Understanding Data Relationships: Data modeling involves defining relationships between various data entities. Understanding these relationships is key in data analytics, as it can reveal patterns and dependencies that might otherwise be overlooked.
- Integrating Data: Data modeling aids in integrating data from various sources by ensuring you can merge and cohesively analyze data from different databases. This is especially important when dealing with large data sets or conducting complex analyses involving multiple databases.
- Predictive Modeling: Data models are often used in predictive modeling, a key aspect of data analytics. Use these models to predict future trends, identify risks, and find opportunities based on historical data and trends.
- Ensuring Data Consistency: By defining the rules for data, data modeling can help ensure the quality and consistency of data. It helps prevent errors in data analysis and can make the results of data analysis more reliable and accurate.
Benefits of Data Modeling
Data modeling plays an integral role in organizing and structuring data. It allows for a visual representation of data and facilitates better understanding and utilization of this data. Here are five key benefits of data modeling:
- Ensures Consistency
Data modeling enforces standards ensuring data consistency across the organization so everyone uses and interprets the data in the same way, reducing confusion and misinterpretation when conducting analysis.
- Facilitates Data Integration
Data modeling is crucial when integrating data from various sources. It helps map relationships between different data types and sources, ensuring data can be accurately combined and analyzed collectively.
- Improves Data Quality
Through data modeling, inconsistencies, duplications, and errors can be identified and addressed early in the data preparation process. It leads to higher-quality data, resulting in more accurate and reliable analytics.
What is Data Visualization?
In today’s data-driven world, there is an unprecedented rise in data volume daily. Making sense of these massive amounts of data, often quantified in quintillions of bytes, would be arduous without tools and strategies for data proliferation, such as data visualization.
Data visualization enables businesses to spot data trends that would otherwise be challenging to decipher. This graphical representation of data empowers analysts to visualize concepts and detect emerging patterns more intuitively. These tools provide a clear and accessible way to interpret complex data patterns and insights, facilitating informed decision-making.
Common Data Visualization Techniques
Here are some standard data visualization techniques and their benefits:
- Dashboards: These provide a quick overview of the key performance indicators (KPIs) relevant to a particular objective or business process.
- Graphs: Graphs are versatile and can represent complex data relationships effectively.
- Infographics: These are visually appealing presentations of data or information, typically used for simplifying complex information.
- Maps: Represent geographic data using maps to understand location-based trends and patterns.
- Charts: Charts are highly effective in comparing data sets and understanding their relationships.
- Videos: These can dynamically represent data changes over time, providing a more interactive experience.
- Slides: Slides offer a sequential representation of data, useful in presentations or reports.
Benefits of Data Visualization
- Enhanced Decision-Making: By visualizing data, decision-makers can better understand intricate data relationships, improving insights and decision-making processes.
- Improved Data Interpretation: Data visualization simplifies complex data sets, making them more accessible and easier to interpret.
- Discovering Patterns: Visualization helps identify emerging patterns and trends that might go unnoticed in text-based data.
Choosing Between Data Modeling and Visualization: A Detailed Comparison
Here’s a quick overview of the differences between Data Modeling and Visualization:
Data Modeling vs Data Visualization
| Feature | Data Modeling | Data Visualization |
|---|---|---|
| Definition | Data modeling involves creating an Entity-Relationship model for database tables to elucidate the links between them. It includes designing schemas for Data Warehouses and representing the interconnections between tables. | Data Visualization, on the other hand, is the practice of presenting data in a graphical or pictorial format to reveal hidden data trends and patterns. It translates complex data sets into an easy-to-understand format. |
| Techniques | Techniques utilized in data modeling include Entity-Relationship Diagrams (ERDs), which demonstrate the relationships among different database tables. It also employs data dictionaries and Unified Modeling Language (UML). | Data visualization uses various graphical elements such as graphs, charts, and tables to visually present data, showing the interrelations between diverse data attributes. |
| Used For | Data modeling aims to ensure accurate data representation and storage within a database. It unveils the inherent data structure by identifying data entities, their attributes, and their interrelationships. | Data Visualization helps efficiently communicate information to users by employing graphical elements for data representation. |
| Benefits | Data Modeling allows quicker data access throughout an organization, facilitates correct data structuring, and helps enforce data compliance standards. | Data Visualization aids businesses in gaining a deeper understanding of their customers, products, and processes, enhancing decision-making capabilities and predictive abilities. |
| Tools | Various tools for data modeling include Erwin Data Modeler, ER/Studio, DbSchema, ERBuilder, HeidiSQL, Navicat Data Modeler, Toad Data Modeler, and Archi, among others. | Tools used for data visualization include Knowi, Tableau, Dygraphs, QlikView, DataHero, ZingChart, and Domo, along with programming languages like Python and R. |
| Performed By | Data Architects and Data Modelers typically carry out data modeling tasks. | Data Engineers usually perform Data Visualization tasks. |