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At TimeXtender, we love a lot of things, but one thing we love quite a bit is destroying absurd and far-too-common misconceptions about the world of data. Today’s candidate is one of our favorites: data modeling. It’s likely you’ve heard many of these misconceptions before in your daily life, but may not have been sure how to debunk, discredit, or outright ignore them. In this post, we’ll look at three common misconceptions about data modeling and how you can deal with them the next time they come up. Let's dive right in, shall we?
misconception #1: DATA MODELING IS SIMPLY CREATING A DATABASE SCHEMA
Oh boy, where do we even start with this one? Let’s start with a simple definition of each. Think of this like a blueprint for a huge library where we will organize all the information we need. Once the walls are constructed for the library, there needs to be a plan for how all the books and information will be organized inside. In the same way, a database schema is used to decide what kind of information will be stored and how to organize it.
For example, if we want to keep track of all the pets in our neighborhood, we might decide that we need to store their names, types of pets, and their owners' names. Then we will create a plan, or a schema, to organize this information. We might create a table for each type of information, like one for pets' names, another for pet types, and a third for their owners' names. This helps us keep all the information organized so that we can find it easily when we need it.
On the other hand, data modeling is like building with LEGO®. Just like it takes different colored and shaped pieces to build that sweet Death Star replica you put together, people use data modeling to create a picture of information. Instead of using LEGO®, data teams use symbols and shapes to represent different types of information such as sales numbers or people’s names and ages. By putting all these shapes together in the right way, they can create a map of the information that helps them understand it better (kind of like finding that ventilation shaft that led to the heart of the Death Star).
A database schema contains a list of attributes and instructions that tell the database how the data is organized. On the other hand, data modeling is about understanding the relationships between different data elements, creating a semantic representation of the data, and defining how data will be stored and used in the future.
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misconception #2: data modeling is only for big data projects
Excuse us while we suppress our laughter. This misconception is so classic, it needs a vintage license plate. Data modeling is important for projects of all sizes, whether you're working with a handful of data elements or a massive data lake. In fact, data modeling is especially important for small projects, as there may be limited resources and time available to manage the data effectively.
Let’s look at another simple real-world example of data modeling – a business analyst working on a project to implement a new customer relationship management (CRM) system for a company. The business analyst may use data modeling techniques to develop a conceptual, logical, and physical data model for the new system.
The conceptual data model would describe the high-level entities and relationships between them, such as customers, products, and orders. The logical data model would provide more detail about the attributes and relationships of each entity, such as the specific data elements that would be captured for each customer, product, and order. The physical data model would specify how the data would be stored in the database, including the tables, fields, and relationships.
By creating these data models, the business analyst can ensure that the new CRM system accurately represents the data that the company needs to manage its customer relationships. The data models can also help ensure that the system is designed efficiently and that it can easily be integrated with other systems and applications.
No matter the size of the project, having a well-built data model can be a valuable tool to help you manage your data effectively and achieve your goals. Skip it and you’re bound to end up with a mess.
Misconception #3: Data modeling is just for the IT Team
Pfft! Data modeling is not just for the IT team – it’s for everyone! Data modeling is about understanding how your data will be used, so it's important for all stakeholders to have a solid understanding of the data model. Whether you're a business analyst, a data scientist, or a developer, having a good understanding of the data model will help you to make better decisions and work more effectively.
Here are a few examples of how top-performing teams are using data modeling today:
An E-commerce Website: A company that sells products online can use data modeling to manage product information, customer data, and order information. By creating a data model that includes entities such as products, customers, and orders, the company can better understand how these different elements are related and organize them in a way that makes sense for the business. This can help the company manage inventory, track customer orders, and analyze sales data to make better business decisions.
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A Healthcare System: A hospital or healthcare system can use data modeling to manage patient data, including medical history, diagnoses, and treatment plans. By creating a data model that includes entities such as patients, doctors, and medical procedures, the healthcare system can better understand how different elements of patient care are related and organize them in a way that ensures patient safety and efficient care delivery. This can help doctors make more informed treatment decisions and improve patient outcomes.
A Financial Institution: A bank or financial institution can use data modeling to manage financial data, including account information, transactions, and customer profiles. By creating a data model that includes entities such as accounts, transactions, and customers, the financial institution can better understand how money flows through the organization and organize data in a way that meets regulatory requirements and protects customer privacy. This can help the bank manage risk, prevent fraud, and offer more personalized financial services to its customers.
With TimeXtender’s holistic data estate solution, it’s easier than ever for your entire organization to understand and learn more about data modeling.
How to Debunk
In the end, these misconceptions about data modeling may be ridiculous, but they’re still out there. Data modeling is a critical step in the data process, and it's important for everyone to have a solid understanding of what it is, why it's important, and how it's used. So, how do you debunk these common misconceptions? Simple – you set yourself up with a holistic data estate builder, instead of fragmented tools or differing resources that can’t communicate with each other, or worse, that create redundancies in your modeling process so you’re essentially doing the same tasks over and over again.
The next time someone (your boss, your coworker, the person who makes your morning latte) tries to tell you data modeling is just creating a database schema or it's only for big data projects, kindly shake your head and remind them of the facts.
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Summary
There are a lot of misconceptions around data modeling. This post looked at three of the most common:
1. Data Modeling Is Simply Creating a Database Schema - Data modeling is not just creating a database schema; it involves understanding the relationships between different data elements and creating a semantic representation of the data.
2. Data Modeling Is Only for Big Data Projects - Data modeling is not only for big data projects; it is important for projects of all sizes, including small ones, to manage data effectively.
3. Data Modeling Is Just for the IT Team - Data modeling is for everyone, as it helps stakeholders make better decisions and work more effectively in various industries, such as e-commerce, healthcare, and financial institutions.
How To Debunk - Set yourself up with a holistic data estate builder, instead of fragmented tools or differing resources that can’t communicate with each other.