What is Data Modeling and Why is it So Important?
Today’s topic: data modeling.
Feeling intimidated by the prospect of delving into databases, mapping out objects, entities, classes, relationships and more? Don’t worry – you’re in the right place.
Figuring out what data modeling is, how it relates to your current systems and how it benefits your business isn’t the easiest thing to get your head round. Database management is a huge and complex discipline in itself, so not feeling like an expert is completely natural.
Ultimately, data models are designed so that a wide range of decision makers within a business can follow them. Whilst you might not be the one creating data models, you will need to understand them to make informed decisions about your data strategy.
Below, we take an introductory look at what data modeling is, when it’s required, what data models look like and what benefits the process offers your business.
What is Data Modeling?
Enterprise data modeling is a chunky topic – so bear with us as we start simple and gradually get more complex!
If you’re looking for a top-level definition, data modeling is the technique of creating a visual representation of how your data is connected within (or within part of) a particular system.
This helps you understand the different types of data used and stored within the system, what the relationship between different data types is and how your data requirements support your business processes.
Is it a particularly technical process? Well, it’s not one to attempt if you don’t have experience or knowledge in this area – the actual process itself is best left to your IT wizards, or perhaps an outsourced team if you don’t have the resources in house.
You should be able to follow the data models themselves even if you’re not on the technical side of the business. They open up data engineering as a concept to strategic teams as well as technical, so that a much wider group of people can work with data effectively.
When Do You Use Data Models?
Data models are used to map out data contained in relational databases.
A relational database is simply a database that stores data points that are related to one another. You extract and organize this data using a programming language called SQL. The relationships contained in relational databases can be represented visually by the data models you produce.
Data models are an absolute requirement for designing and building relational databases. You won’t be able to create one without a data model – or at least not one that works.
You might also use data models to map out your existing data processes and structures to see where improvements could be made. This is particularly useful when you’re investigating how to get more from your data, or considering replacing legacy software.
What are the Different Types of Data Modeling?
Your next question is likely ‘…so what does a data model look like?’. To answer that, we’ll need to explore the different types of data modeling infrastructures and the different types of models themselves.
We’ve included a visual example for each of them, to make it easier to understand the differences between them.
The Three Main Types of Data Model
The three main types of data model are conceptual data models, logical data models and physical data models.
Conceptual data models (also known as domain models) are an overarching view of what data a system needs to contain, how this data should be organized, which business rules should govern it and which entity classes you need to define. Conceptual data models are often used in the discovery stages of a project when mapping out project requirements, and are fairly basic in structure:
Logical data models build on the pattern laid out in conceptual data models. In logical data models, you’ll see your basic framework laid out, but with more relational factors included. Annotation is generally limited to overall data attributes rather than individual units of data. Logical data models are often left out of Agile or DevOps projects, but are useful for procedural, data-oriented tasks like data warehousing.
Physical data models are the most detailed type of data model, and usually the final step before building your database. Physical data models should include in-depth information on where and how your data will be physically stored and data points’ relationships with each other. As they are a blueprint for relational database creation, they should also contain associative tables that illustrate the relationships among entities as well as the primary keys and foreign keys that will be used to maintain those relationships.
Different data modeling infrastructures
Alongside the three types of database, there are several different ways of structuring your data models visually. These are:
- Hierarchical data model: a ‘family tree’ layout where entities are connected as parents and children.
- Relational data model: similar in style to a hierarchical model, but maps out connections between tables of data rather than entities.
- Entity relationship data model: emphasizes individual entities and how they connect. Often used alongside a relational data model for greater depth of understanding.
- Object-oriented data model: groups entities into class hierarchies. Useful for complex real-world data points and early-stage multimedia development.
Why Is Data Modeling Beneficial to Your Business?
Data modeling requires multiple facets of your business to work together to understand your key processes, what data these generate and how they connect to each other.
Whilst gaining this level of insight takes time, it’s absolutely worth it for the advantages it offers your business. If you invest your time and resources correctly, you’ll see major technical and strategic benefits from data modeling. These include:
Data modeling slashes your programming costs considerably.
This is because data modeling makes it much easier for your project team to catch potential errors before the build phase. Data modeling maps out key data structures, allowing your team to avoid problems, rather than building errors into a project then amassing delays trying to identify issues and fix them.
Your project requires fewer iterations as a result, and you’re significantly less likely to release error-ridden projects to your customers.
A better time to value
As a result of the above, data modeling also helps you cut time to market – and therefore the time it takes for your projects to start making money.
Fewer errors mean fewer iterations. Fewer iterations mean that you can release new products quicker, and that they can start generating revenue. As fewer errors also broadly translates into a better user experience, it’s likely that revenues will be higher too.
Overall, data models cut development costs whilst increasing revenue at the same time. With data modeling, you’ll get your product to a better place technically, whilst making it commercially viable more quickly.
Reduced data complexity and risk
Businesses generate, process and store more data than ever before. Over the coming decade, that amount is only going to increase. Knowing how you use that data is vital in avoiding costly mistakes in how you govern it.
With this explosion of data comes two key risks:
- Cybercrime: criminals stealing data by exploiting unidentified weaknesses in your data architecture.
- Regulatory: regulatory bodies imposing large fines for not adhering to data management and storage protocols (for example, GDPR).
In short, you really, really need to know how your business uses data, so that you can identify any security risks and keep in line with ever-tighter regulatory controls about storage and security.
Mapping out the data architecture of your business is vital here, particularly as you grow and generate more data. Without an understanding of your existing architecture, you risk your data becoming messy, unmanageable and vulnerable to risk.
On the other hand, having a single source of truth for existing data architecture – which data models provide – are essential in reducing these risks. You will still need to invest time in data management, but data models allow you to manage data risk much more efficiently.
Improved internal processes
Let’s say your customers’ first purchase tends to be the most money they spend with you. You want to figure out why, and take action to increase customer lifetime value.
Whilst there could be a number of reasons for this, it’s always worth delving into your data architecture for insights. For example, your data models might flag up that potentially useful data sets for personalization (like social media engagement with your brand) are siloed into an entirely separate system than basic customer information like contact information.
Data silos are a huge issue for businesses. Being able to identify where they are and how best to integrate them is a huge advantage when it comes to improving key processes, whether internal or external.
Creating Your Data Models – Next Steps
As we mentioned at the start of this article, data modeling isn’t really a DIY task.
If you don’t have that expertise in house, it’s really worth considering outsourcing to third-party experts. Your data models may end up defining how your internal IT systems are structured, or becoming the backbone of an external facing product.
At Tivix, we’ve been building data models for a range of clients for over a decade. We’ve helped startups, Fortune 500s and NGOs alike create cohesive data strategies, build custom data management software and create data architecture that helps unlock the true value of the data they collect and hold.
If you need data modeling expertise, why not get in touch for a quick chat? We’re happy to talk through all things technical – and it’s completely up to you whether you want to take things any further.