The first thing you should know about data architecture is that your organization already has one – whether you realize it or not. That’s because data architecture refers to two things: the way that information flows through and around your organization, and your efforts to control that data via a data architecture strategy. The first example refers to data architecture as a “thing,” while the second refers to it as a discipline.

It is easy to get the two aspects of data architecture confused or conflated. Understanding both the concept and practice is critical to maintaining clean and useful data. The rules by which you govern your data are simply tools, but a modern data architecture is an exciting practice that can help organizations like yours use and deploy information throughout businesses.

What does data architecture do for businesses?

At its core, data architecture bridges the gap between your business strategy and the data-based execution of that strategy. It fills the space between the data your organization needs and how that data gets into the hands of the people who need it. In short, the goal of your modern data architecture is to make sure each member of your organization gets the data they need whenever and wherever they need it the most. A well-constructed data architecture framework will also allow you to understand your data requirements based on what your business needs. In other words, it can help you translate your organization’s goals into tangible data requirements.

How to Build a Modern Data Architecture Framework

Start with the most valuable data

The first step is identifying what type of data is most valuable to your organization. In many cases, the metrics you should pay the most attention to are the ones that influence or relate to the overarching goals and objectives of the company. In order for information to be truly valuable to the organization, it should have a high impact on the business.

Ask yourself the following questions:

• How does this information contribute to the primary objectives of the organization?
• Does the data pertain to specific teams or individuals and their goals? How?
• How does this information bring the technological and “business” sides of the organization?
• Can you use the data to draw specific, tangible, and usable insights to benefit the organization?

Make governing your data a priority

We get it – there’s a lot on your to-do list. Still, prioritizing your data’s quality and maintenance pays dividends and can actually ease your workload in the long run. Data governance (how you manage and control information in the framework) is one of the best ways to make sure your data is not only valuable but directly correlates with your organization’s business objectives and long-term goals. It also ensures that data is high-quality, clean, and free of “data clutter.” In the end, you and your team will need to take responsibility for the integrity of your data. Only then can you trust it fully and use it effectively in your data architecture. If you make this your priority, you can approach the rest of your data architecture strategy with confidence knowing the information in it is accurate.

Build your architecture so it can change

It’s easy to assume that longevity equates to high quality. When it comes to creating a data framework, however, the opposite holds true more often than not. Simply put, data architecture should be built for change. It should be flexible, not immovable. If you create your data architecture framework with the intent of building something perfect and never changing it, you run the risk of missing new technology and process opportunities that could benefit the business in the future.

Instead of focusing on a framework that will last forever, focus on creating a data architecture that has the flexibility to grow with your organization. Find solutions that are structured enough to serve their purpose well, but pliable enough to accommodate the changing landscape of your organization’s sector. Your framework should be able to accommodate sudden changes just like your business adapts to changes within its unique sector.

Read next: How To Promote Data Literacy

Build a system that functions in real-time

Data exists within your organization to help key decision-makers make informed choices. This means your data architecture should facilitate real-time information so stakeholders can access the data they want when they need it. This could mean supporting real-time access to your existing data infrastructure, such as a data warehouse; or it could mean supporting user analytics from mobile devices as they occur in real-time.

Of course, not every piece of information is something users need moment-by-moment, so carefully select which metrics are valuable because they appear in real-time, as opposed to data sets that can be pulled less frequently (such as on a daily basis, etc.)

Remember: Data is a service

In the end, data is a service to users. For many organizations, though, providing data is difficult because it comes from multiple databases and sources. In these situations, users typically access data through a virtual layer – one that combines each source seamlessly into a cohesive environment, such as a dashboard. When you treat your users like customers who need a service, it’s much easier to package each data set so it will serve its intended audience well. Additionally, data can be vetted and scrubbed for inconsistencies more accurately when it is filtered into one, unified place. The result is a single source of truth supported by your data framework.

Bringing your data flow to life

Without proper data architecture, your organization’s data wouldn’t be able to reach the teams and individuals who need it. But what happens to your data once it reaches their laptops, tablets, and mobile devices? A front-end data visualization layer sitting on top of your data structure can pull information from a myriad of sources and seamlessly combine it into one, easy-to-understand platform. There, users can access reports and drill-downs that specifically relate to their unique functions within the organization and focus on what matters most: using that data to reach their goals.