What is data modeling in software engineering? Let me explain.
Every company has a lot of data in its databases. But don’t you think managing massive chunks of data can create confusion? We know for a fact that data becomes obsolete after some time if it’s unorganized. After that, it doesn’t matter how relevant it was. To clarify, without organization, data isn’t of much use. Moreover, you can’t use it to its full potential. In fact, messed-up data makes it tough to store, retrieve, and capture it efficiently.
Data modeling is a method that helps you avoid the cons that come with poorly designed data. It’s like a map that helps organize information for better use. In this post, we’re going to take a detailed look at data modeling and why it’s important. We’ll also check out the types of data models and the pros and cons. This post will also cover the steps of data modeling in detail. So, let’s dive right in!
What’s a Data Model?
In layman’s terms, a data model is a logical definition of a database’s structure. The data model defines how multiple data are connected to each other. Data modeling changes the complex design of a database into a simple structure. It can be a diagram using text, visuals, and symbols that shows the way information should flow.
Figure 1-3 on this site, showing the relationship between employees and a project, is a great example of a model for handling complex data. A company’s employee database is complex, with entities like name, hire date, and project details. The data model converts it into a simple diagram. Now that we know what a data model is, let’s understand why we need it.
Why Do We Need a Data Model?
You need a data model to ensure that the developer has a structure of data objects and their flow. Data models help in designing a database at a physical and logical level. A data model also defines primary keys, foreign keys, relational tables, and stored procedures. Once the developers have an idea of how the data flows, they can create a physical database.
When you sort data in the correct order, you can find out which data is unclear. You can also look for missing information and take action. In the long run, this helps in upgrading the base of your project or company. Not only that, maintenance costs go down with a proper data model in action.
Types of Data Model
From the definition above, we know that the data model defines the structure of a database. It does so at a physical, conceptual, and logical level. Let’s check out the three main types of data models.
The conceptual data model defines what the system is made of. Data architects and stakeholders create this model. The aim is to define, set the scope, and organize different rules and business concepts. This model aims to define entities, how those entities relate to each other, and their attributes.
For instance, let’s consider two entities, customer and product. The customer’s name and ID are the attributes of the customer. The price and name are the entities of the product. We can say that sales is the connection between the product and the customer.
A physical data model has more details than a conceptual one. Also, the physical model defines all the things that a developer needs to build a database. Apart from all those in the conceptual model, a physical data model has properties like column names, table structures, and foreign keys.
There are two ways of creating a physical data model. You can create it by modifying the existing logical model. Or, you can use the native DBMS language. Database admins use the physical data model to plan the database’s capacity.
The logical data model is responsible for defining the structure of different data elements. The design and development of this model don’t depend on DBMS. In this model, you need not define the primary and secondary keys. You need only verify the details and adjust the connector between the entity and attributes. This model is the same as the conceptual model, only a bit more detailed.
Data Modeling Steps
Because there’s ample data to cater to, data modeling happens step-wise. Let’s take a look at the steps of data modeling.
We first consider raw data into different sets, each modeled separately. Each set has some business objects or entities. The first step is to identify these entities.
Identify a Key Property
Every entity has a key property. This way, you can differentiate between entities in a data model. For instance, in an employee data model, employee ID is a unique property.
Create a Rough Draft
Entities in a data set form a connection with each other in some way or another. Make a rough draft to display how the entities connect with each other.
Identify Different Data Attributes
After creating a rough draft, identify different data attributes or properties. Incorporate these attributes into the data model.
Map Out the Attributes
Map the attributes to entities after identifying various attributes. This way, the Entity-Relationship model will show what the data means from a business point of view. For instance, let’s consider the employee data model we discussed earlier. There, the employee’s name, ID, and join date are different attributes that we can map to the employee table.
All clear about the what, why, and how? Then let’s discuss some pros and cons of using a data model.
Data Model Pros and Cons
Now that we understand what data modeling means and why it’s important, let’s check out the pros and cons.
Data models help in lowering the cost of development. Generally, companies spend a lot of funds on coding and testing. Data modeling reduces the company’s coding budget. Not only that, the best part is that data models don’t use a lot from the budget. A data model also catches errors at an earlier stage. Don’t you think that’s a lot better than correcting mistakes when coding is complete? Or worse, imagine if the customers use your app only to find unfixed errors! How would that affect your business’s reputation?
Better Management of Data as a Resource
You can normalize your data with data modeling. You can also define the data in terms of what it is. Not only that, you can even define data in terms of the properties it can have. Querying the database and generating reports are important processes of any business. Data modeling provides the tools for the same. As a result, you can manage the data as a resource in a better way. Just think of it this way—suppose you hire a developer to create your database. What will the developer find easier: developing from an Excel sheet with multiple entities, or from a diagram that explains how all the entities connect with each other?
Designing Repositories and Databases
Having a smooth-functioning database is a must for every firm. For that, data modeling is an important process. It also helps in driving better decisions about archives and data warehouses. Apart from that, a data model describes the data clearly. As a result, a company knows the business needs for storing data. When you have a clear view of data, it becomes easier to decide what you need. Do you need data marts or a global data warehouse?
Accurate Representation of Objects
Data modeling provides an accurate description of data objects by creating a flow or diagram. This diagram shows how the entities and their properties connect with each other or with other elements in the database. You can use the information to define the relationship between tables, primary, and foreign keys.
Most people think that a database runs slow because of some problem in the database design. But in reality, without a data model, the database development is poor. Also, data modeling ensures better performance by easing the database tuning. When the concepts in a data model are clear, the developer can design a database that runs faster.
Like all other new technologies, with so many pros, there’s bound to be some cons as well.
- You can’t perform data modeling without knowing the features of the physical data stored.
- Even if you make only a minor change in the structure later, you’ll need to modify the entire structure.
Unleash the True Potential of Company Data With Data Modeling
Every architect makes a blueprint before constructing a building. Similarly, before developing a product, it’s important to consider all the data that you’ll need. If you’ve created a data model before development begins, it saves costs in the long run. As a result, the software is less prone to bugs.
Even if there are errors, you can catch them early. Sometimes, a firm doesn’t realize the value of the information it collects. Firstly, the data just lies there, sitting idle. Secondly, information loses its value as time passes. In other words, without a data model, you can still have all the data you want, but you won’t have any means to use the data unless you go for a data quality audit. All in all, if you’re looking to build quality software that’s predictable, data modeling is the way to go.
This post was written by Arnab Roy Chowdhury. Arnab is a UI developer by profession and a blogging enthusiast. He has strong expertise in the latest UI/UX trends, project methodologies, testing, and scripting.