Data modeling is a way of mapping out and visualizing all the different places that a software or application stores information, and how these sources of data will fit together and flow into one another.
This is a hugely important stage in the design process for any business-critical IT system. When developers are figuring out how a new system will work, they establish what the most pressing needs of a business are, what kind of data they’ll need to access in order to meet those needs, and how the data will be used.
From there, they can start to create a diagram (or model) of how each pocket of data will flow into each other, and how they’ll interact.
Types of Data Modelling
There are many different ways you can approach data modeling, but generally you’ll want to work through three of the most common as you perfect your design:
Conceptual Data Modeling (or Enterprise Data Modeling): This starts by looking at the main needs of the business and working out how the most important entities relate to one another. Think of this as the big picture of how you want your data to interact across the company.
Logical Data Modelling: A little more complicated than conceptual data modeling, this drills down to how each piece of the puzzle works within each specific business function. You’re starting to look at how the technical details of the model will support the aims of the business.
Physical Data Modeling: This is your actual blueprint for the data model design. By this stage, you’re laying out precisely how each database will be implanted and how the databases, applications, and features will interact in forensic detail.
Data Modeling vs Data Analysis
Data modeling and data analysis are terms that are often bandied about together. Actually, they’re very different things, requiring entirely different skill sets.
Data analysis is what you do with the information at your fingertips. It’s about filtering through data to draw out the most important insights, whether that’s in the form of reports, graphs and other visualizations, predictions for the future or assessments of how and why things in your business are working the way they are.
See it in action:
Data modeling, on the other hand, is purely about creating the conditions to make this analysis possible. It’s about figuring out what types of data you’ll bring together and how, to get the answers you need.
Picture it like a restaurant. The data modeler is responsible for designing the kitchen, making sure all the right appliances are in place and the ingredients are stored in the correct way. They have nothing to do with preparing the food, but they need to talk with the chefs and restaurant manager in depth, to make sure they understand what’s required to meet the demands of the business. (Learn more about data modeling techniques).
A data analyst is like the chef. They don’t need to know how to install the kitchen, but they do need to know how to use it. It’s up to them to use the tools the data modeler has provided to select the right ingredients, to make something digestible and presentable that helps the business to thrive.
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