Understanding the Differences Between Data Modeling and Data Architecture

When exploring the main differences between data modeling and data architecture, the easiest way of expressing how they differ is by documenting the distinct purposes, deployment, and usages that each commands. Data architecture and architects serve a specific purpose in the world of business, as does its counterpart of data modelings and data modelers.

While both data architecture and data modeling are essential to any business and their daily practices, these fields are applied in distinct ways, complementing and feeding into one another. To outline the differences between these forms of data management, we’ll explain exactly what each is, when it’s used, and the main differences between them.

What is Data Modeling?

To put it simply, data modeling is how a data engineer will represent the flow of data within a diagram or data model. These act as the blueprints for a data system, demonstrating the flow of data, what databases are connected to what sources, and documents of the relationship between different entities.

A data model is a flowchart, which then allows businesses to understand exactly how their data comes about, how it’s distributed, how it’s collected, and how it can be used or changed. Equally, data models are fed to the data management team in order to give them a way to identify errors or create plans for future developments.

Data modeling is a very formal subset of data engineering, with standardized techniques being applied in order to construct a map of how data flows through a business. There are three main types of data models, each of which complements a different process:

• Logical Data Models – These provide detail about relationships between different data systems. They would include information about the format the data is in, as well as any links between different data sets. You’ll most likely find these in a data warehouse.
• Physical Data Models – In this scenario, ‘physical’ relates to how data will be contained within a specific database. They are often a range of different tables with clear lines of relation between them, helping anyone that needs to manage an entire data ecosystem understand exactly where data is coming from.
• Conceptual Data Models – The least specific form of data modeling is known as conceptual modeling. This is the practice of giving an overview or big picture of a whole organization. This is the simplest form of data model and will have only the bare-bones information. Think of a conceptual data model as the framework that project managers can refer to in order to understand how their business is generating and processing data.

In short, data modeling is transcribing the complex movement of data into a simplified and easy-to-read document or flowchart, which individuals in business can then apply further analysis on.

When and Why is Data Modeling Used?

A data model is constructed any time someone wants to know more about how the data a business is using works, comes to arrive at a company, or is connected to a certain output. As it is a representation of complicated data flows within a business, a data model allows this complex idea to be broken down into an easy-to-understand framework.

Typically, data modeling is used as an organizational force, allowing business and IT teams to understand what they’re dealing with. With this level of baseline understanding, businesses are able to work and produce new products much faster, as you can catch errors quickly, go through fewer rounds of changes, and streamline business processes.

Considering that a staggering amount of data is produced every minute, a data model ensures that no one becomes overwhelmed by the sheer amount of data out there. Instead, you’re able to manage huge amounts of data and see clear relationships between them.

What is Data Architecture?

Data architecture within a business is the foundation upon which all data processes are built. This field of data processing and management is all about constructing the data pipelines and pathways that data will be able to flow through. An organization that has a good level of data architecture will be able to pass data from one warehouse to another, seamlessly creating a flow from data storage to data processing and onwards to analytical functions.

Data architecture supports all other data processes, being the first step in creating a working data-driven organization. Part of what makes this practice so fundamental is that data architects can scale and modify data processes.

If another data set needs to be introduced, it would fall to a data architect to construct the pipeline that introduces this data into the current system. Equally, if the pathways need to be modified, the flexibility of data architecture allows them to be changed.

A huge part of this circles back to the appropriate storage and maintenance of data. Data architects will often be proficient in SQL, ensuring they can construct and integrate data warehouses into a business. The most common SQL definition is that it’s a programming language that allows data engineers to create relational databases. With this skill, they can effectively construct a warehouse and then connect them to the other data architecture they have already created.

With construction, modification, and scalability at its core, data architecture is fundamental for modern business. It is the main way that businesses interact with, collect, and process their data for business analytics – being a huge part of the $200 billion + that is spent on data processes each year.

When and Why is Data Architecture Used?

Due to the fact that data is simply everywhere in our modern age, data architecture is used practically all the time. Any time that someone needs to access a database or find out where data is coming from, they will turn to the data architecture that’s in place.

Considering that bad data costs  $3.1 trillion each year, , having stable and appropriate data architecture in place is vital for a company’s success. If their data architecture is badly structured, it will create duplicates of data, slow down processes, or make some important pieces of data inaccessible.

From collecting base data to transforming it into a useful format and even relaying it to platforms and tools that will then conduct analysis, data architecture is vital.

How is Data Architecture Different From Data Modeling?

While data modeling is about representing the connections and flows of data within a company, data architecture is about the actual construction of those frameworks. Instead of just representing relationships, data architecture builds the data pipelines that connect different data sets.

• Data architecture is about building and connecting data.
• Data modeling is about representing those pathways and connections.

While they deal with similar things, they focus on different principles, and each has their own benefits and challenges.

What Are The Benefits of Data Modeling vs. Data Architecture?

While you can place data modeling vs. data architecture head to head, the simple fact is that both of these processes are vital for any business that wants to ensure they are receiving good data, alongside understanding where that data comes from.

For example, the benefits of each are as follows:

• Data Modeling: Helps people to understand how data flows in a business, helps when tracing where business intelligence comes from, supports project management, and speedily deployment of new systems.
• Data Architecture: Ensures there is no bad data within a business, helps collect and distribute data within a business, and supports the transformation and delivery of data across many formats and sources.

While both serve the business and its output of data, they do so in different ways, one constructing data pathways and the other tracing them.

So, What’s The Difference Between Data Modeling and Data Architecture?

To wrap up, the core differences between data modeling and data architecture come back to three main differences:

• Purpose – The main purpose of data architecture is to build pathways and data pipelines between business tools and datasets. On the other hand, data modeling is relied upon to help people easily understand the relationships between different parts of the data a business uses.
• Deliverable – Data architecture is delivered at the start of the project and will be the first thing you turn to. A data model will be constructed after the architecture is in place to create a map of what has been made by the data architects.

Across these two main differences, you can quickly see that data modeling and data architecture, while dealing with similar things, take different approaches. While data modeling is about representing a data framework, data architecture is about actually making that framework and ensuring it works properly within a business.

By combining these two fields, you’ll be able to ensure that your data engineers can construct a well-structured system and that anyone that’s interested in how this data works or is delivered can clearly trace the relationships on a data model.