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Data modeling is a key tool for organizations to accelerate their application development and unlock the value of their data. However, perhaps the exact definition of data modeling has eluded you.
So, to help shine a light, here’s our definition of data modeling:
Data modeling is the process of creating a visual way to describe your business through understanding and clarifying your data requirements and how they underpin business processes.
Data models are technical in nature but also designed to be simple, and (mostly, but not limited to) visual in nature. This means they hit the sweet spot between hard to digest tech-speak and easy to understand everyday terminology. Thanks to data models, everyone in your organization can understand and collaborate with your data more effectively.
Below is an example of a data model from database.answers.org
So, we’ve looked at a definition of data modeling, but a definition only tells part of the story.
To really understand what data modeling is, you have to dig down and look at the specific benefits it delivers. Naturally, these benefits are only achieved when you deploy data models effectively, and when business and IT teams work in harmony.
Let’s explore the benefits of effective data modeling now.
With data modeling, business users can have a direct hand in defining core business rules which means fewer revisions are needed at implementation time. The integration of requirements and development results in a significant improvement in overall development time. This means you’ll deliver projects and new products to market more quickly. We helped one client use data modeling to reduce their time-to-production from 9 to 3 months.
You’ll also save money with data modeling because it catches errors quickly. This means you need fewer iterations, and your team are less likely to pass on error-ridden projects to higher-ups or, God-forbid, your customers. This results in data modeling reducing the budget of programming by up to 75 percent.
Data modeling can be complex and adds some paperwork (both on the IT and the business side). But it’s also one of the best ways to bring your data under control, reduce costs and accelerate development.
Data modelling forces you to articulate your business and its processes, and to do so in ways that allow different people to collaborate. You simply can’t define your data and what it’s doing if you don’t know how your business is operating.
For example, to build a database of your customers, you need to understand the data your business currently has on customers and how you’re using it.
The process of data modeling thus uncovers your data and its relationships, which provides a foundation for understanding your business processes and how to improve them.
To handle the tidal wave of data that every organization must face, you need to ensure your data is simple and low risk. The more data you have, the sooner you need to think about taming it. And, with all the regulations and dangers data can represent, you need to bite the bullet and do it right. This means documenting and connecting everything with the ever-changing data.
Data models provide intuitive diagrams of your data processes, so you have full visibility into your data architecture. This reduces risk because you get insight into all your data – no longer are transformations, metadata, or filters buried and scattered. Now, it’s easy for your business to gain a single version of the truth.
Data modeling also makes the complex, highly technical elements of your business more accessible to less technical staff members, such as business executives and those in the C-suite.
Now, your IT team can collaborate more easily with non-technical staff. Using data models, they can communicate in a technology-neutral way, but still with enough detail to create physical data structures when needed.
Data modeling makes it easier to integrate high-level business processes with data rules, data structures, and the technical implementation of your physical data. Data models provide synergy to how your business operates and how it uses data in a way that everyone can understand.
With the right data modeling strategy, you can gain complete control over data definitions and metadata. Then, you can enjoy all the benefits we’ve covered.
However, putting these data definitions into practice requires a little more work. To bridge the gap between data modeling and data integration, you need a way to turn written structures into actionable code.
If you’re curious how data modeling works in practice, our guide to making data modeling actionable might be helpful. It demonstrates how you can unlock the promise of data modeling, so your data strategy and business can prosper.