Why Do You Need Data Modeling?

You need data modeling to save yourself or your organization lots of money, hours, and issues. Read on to find out how data models do its magic.

Data modeling is the process of creating a conceptual view of the information a database contains or should contain. As a result of this process, a data model is created, giving form to data objects (all those entities for which information is to be stored), the associations or relationships among them, and rules or restrictions that govern the information that enters the database.

Very nice, but is it really necessary to work with data models? Can't we just skip this step, save some time, and go straight to creating objects in the database? A course in database modeling will answer these questions, but if you want a summary, I’ll give you enough reasons to have a data model at hand whenever you need to work with information stored in a database. By the time you finish reading this article, you will agree with me that working with a database without a proper model is equivalent to building a house – or even a skyscraper – without a proper foundation.

Let’s start by considering two contexts in which data modeling is mainly done:

• Strategic modeling, which is carried out as part of the general information systems strategy in an organization.
• Database design, which is a part of the design phase in the software development process.

In both situations, there are plenty of reasons to do data modeling. First, we will see those that have to do with information systems strategy, then those related to software development.

Higher Information Quality

A data model is essential in providing clarity and consistency in the metadata, the definitions of the objects that make up a database. This contributes to increasing the information quality. For example, a data model can ensure that the correct formats are used for data elements such as phone numbers and ZIP codes, and in a database where customer data is stored, it can ensure that each customer has at least one address.

You can also ensure the quality of the information stored in a database by imposing rules so that only valid data enters the tables. To do this when designing the data model, you set the value domain for each field and differentiate the fields that must have values from those that can be left empty.

Data model definitions ensure data compliance to business rules. For example, you may want to enforce each client to have an address with the correct ZIP code format, or each address to be associated with a city and each city with a state.

Information quality is also improved by imposing restrictions that ensure referential integrity and maintain the intended cardinality in the relationships among entities. Those restrictions can be derived only from a proper data model.

Data Asset Reuse

When developing a new system or adding new functionality to an existing system, it is common that some of the data entities required by the new development already exist in a database and therefore can be reused. The only way to find out which entities already exist is to browse up-to-date data models that adequately describe the structures of the databases in use by the organization.

Conceptual, logical, and physical data models should be maintained to provide views with different levels of abstraction to let you detect reusable data assets easily. You can leverage a specialized design tool, such as the Vertabelo platform, to facilitate the creation of different types of data models and even to derive one from another.

This good practice avoids generating redundant data in different schemas, which leads to inconsistent information sooner or later (more on this below).

Migration to Cloud Environments

With DaaS (Data as a Service) infrastructures or databases in the cloud, certain requirements, such as database privacy, dynamic scalability, and efficiency in managing multiple tenants, become more critical.

Data models are an invaluable tool to meet these requirements, since they facilitate verifying that a schema design conforms to them. In turn, they allow you to define the partitions of the schemas and their storage requirements, which is essential to properly dimension the service level required and the expected storage growth when databases reside in private or public clouds.

Database design artifacts such as ER diagrams are the tools of choice when preparing for a migration to a cloud environment. A guide on how to use ER diagrams can give you a glimpse of their usefulness in database migration.

Database Modeling for Big Data and NoSQL

Non-relational databases, such as NoSQL and dimensional schemas, may force us to put aside (at least for a moment) our traditional relational mindset. But that does not mean we can do without data models. On the contrary, data modeling becomes even more important.

When you need to work with Big Data, you commonly face huge silos of information that must be broken down, refined, and structured in such a way that you or a data analyst can get strategic insights from it. A careful schema design is required, both for refined information repositories or data warehouses and for staging repositories used for data cleansing and data structuring processes.

There is a misconception, mainly by programmers, that NoSQL databases do not use schemas and therefore they do not require data models. Nothing could be farther from the truth. Since NoSQL technologies don’t provide a standardized way to view the metadata (something every RDBMS does), data models become essential in letting people use and share the information stored in the database.

Mergers and Acquisitions

Any merger between two organizations poses a gigantic challenge for their respective IT departments. A significant part of this challenge is in database consolidation. If both organizations have up-to-date data models, this consolidation can be done in the models instead of directly in the databases, substantially reducing the effort devoted to the task.

So far, we have seen the benefits of data modeling associated with IT strategic planning of an organization. If these reasons aren’t enough to convince you of the importance of data modeling, let's also look at the benefits it brings to software development.

Reduced Development Costs

In the early stages of a development project when the budget is being analyzed, the need to put effort into building a data model may be questioned. If the project leaders and managers are smart enough, they will compare what it costs to build and maintain a data model with the costs that will be saved and decide in favor of building the model.

Data modeling is a mere 10% of a development project budget and has the potential to reduce the actual project costs to less than a third.

Just consider the following. In most cases, the cost of data modeling (that is, the cost of the effort required to build and maintain the model) is less than 10% of the total budget for a software project. In comparison, the cost savings associated with using data models is up to 70%, all from the reductions in the hours for coding and maintenance.

So, in software development, the first and the most important reason to do data modeling is the unquestionable ROI (return on investment), which project leaders must consider in the early stages of every project.

Better Definitions of Requirements

In software development, you can guarantee a greater understanding of the system to be developed if data modeling activities are carried out in parallel with requirements gathering. The requirements will be more complete and more correct.

Data modeling helps uncover business rules and ask questions during requirements engineering, while ensuring data integrity. It is more effective than process modeling activities such as use case design or workflow design, and obviously more expressive and less verbose than the prose description of the business rules.

Faster Development

When developers have proper data models at hand, they can do their jobs with fewer errors. Data modeling tools automatically generate and maintain database schemas, creating data definition language (DDL) scripts that are often too long, complex, and messy for developers to generate manually.

In turn, those tools foster collaboration by allowing models to be shared among developers. When changes are needed, you can make them in the data model, ensuring that all developers will be informed and that they will be applied to the databases without breaking anything.

All of this allows the systems to be delivered sooner and with fewer bugs.

Boosting Agile Methodologies

Agile methodologies aim to speed up the development process by focusing efforts on delivering working software and avoiding bureaucracy, excessive documentation, and phases executed one after another.

Database modeling faces a significant challenge when working in agile environments, as the designer needs to be able to work on the “big picture,” while developers need only the data objects required for each user story. To reach a consensus between data modelers and developers, agile methodologies use techniques such as sandboxing and branching.

A sandbox is the working environment of each developer. The designer can work with the branches of the main data model in the sandbox of each developer, who will provide feedback to refine it. At the end of each stage (or sprint), the database designer merges the different branches to keep the complete model updated.

You might think that data modeling slows down agile teams and that developers must wait until models are ready to begin their work. But in reality, using techniques like sandboxing and branching maintains the principles of agility and achieves the speed improvements mentioned above at the same time.

What if I Don’t Use Data Models?

You might think that you can still survive without the benefits of data models mentioned so far to save time. But if you decide against data modeling, you risk running into serious issues such as:

• Unnecessary redundancy: Since there is no model to see the data objects clearly, different versions of the same objects will appear with different information. For example, an inventory system may report that 500 units of an item were sold in the last month, while a logistics system may report that 1000 units of the same item were shipped in the same period. Which is right? Who knows.
• Sluggish apps: The absence of a data model makes optimization tasks difficult, which reduces the responsiveness of the applications.
• Inability to meet quality standards: If there is no data model, your databases won’t be documented, which is mandatory in scenarios such as database migrations.
• Poor software quality: The software development requirements will be poor, and users will not have the applications they need or desire.
• Higher development costs: I’ve already mentioned the significant cost savings that can be achieved in a development project by using data models. If you choose not to use them, you will have to decide who pays for the extra development and maintenance costs. And who will make excuses when the deadlines are not met.

Still Not Convinced?

If what you’ve read so far is not enough to convince you of the importance of data modeling, remember that data is becoming an increasingly valuable asset for all kinds of organizations. Modeling the structures for taking advantage of information has unprecedented relevance today.

Consider this: during the gold rush, the guys who made the most money were not those digging for gold nuggets but rather those who provided the tools to extract the gold. In 2021, gold nuggets come in the form of insightful information, and the miners who extract such precious material need to be provided with data models.