Understanding data architecture

In the last few years, we have relied on data to inform us about the decisions we make or the business models we create. Back then, Data Architecture was a technical decision. But things are different now. It is a common ground where business strategy and technical execution meet.

Data Management Body of Knowledge (DMBOK) describes Data Architecture as "Data strategy specifications that outline the current state, describe data requirements, direct data integration and manage data assets."

A good strategy for any company depends on the effective use of data. And data architecture provides you with a set of policies that work as a strong foundation for your business model. It has guidelines for various processes which include methods for data collection, usage, processing, storage, and integration into various systems.

There are three individual outcomes of Data Architecture namely, the outcome, the activities, and the behaviors. These three components cover observations, the methods of implementing the architecture's purpose, and the various interactions.

In this article, we will explore all the components of data architecture and understand how these solutions can help your data team.

The shift from traditional to modern data architecture

As indicated earlier, data architects align the data environment of the company with their strategies. They worked from the customers to the data sources. This enabled the architect to customize the architecture to meet the specific requirements of the company.

Static data warehouses are something of the past. They were barely able to keep up with the constantly evolving changes and requirements of the business. This resulted in organizations being frustrated with the least returns on investment derived from these static data warehouses. This led to the need for new data solutions.

Organizations have used data lakes to store raw data. Though this required large storage capacities, organizations were able to analyze data for any requirement. However, a lack of proper governance of data has plagued this solution as well.

Though the present-day data architecture will still require or have a data warehouse, there is more to it. One must remember that a data warehouse is only a part of the data environment that needs to be both flexible and agile. Not to mention, each individual in the data team can have customized access from the architecture itself.

Things to keep in mind when building a modern data architecture

There are six factors that you need to consider before you begin building a modern data architecture.

  1. Identify the most valuable data types for your organization's needs. There is no dearth for data today. Hence you must decide and work with only that data which proves valuable to your business and deliver maximum impact. There are various cloud offerings that you can leverage to manage data without burning a hole in your pocket.
  2. Once you have identified the data that you will be working with, you need to focus on data governance. You need to ensure that you communicate the importance of the quality and relevance of the data to the architect.
  3. You need to create a flexible system. The best data architectures are those that can adapt to the constant changes in the business requirements. Such a design is easy to use since it blends in with new solutions that are on the rise in the market.
  4. Apart from building a flexible system, you need to create a real-time data environment. This kind of environment enables you to combine existing data warehouse with the new solutions so that you have real-time access to historical and current data.
  5. The modern architecture you build needs to be secure. As data management systems advance, so do the challenges and threats posed by malware and viruses. You need to be on high alert so that you can keep your data intact.
  6. Implement data as a service. This ensures that you can integrate all your systems easily and works like SaaS (software as a service) solution. This also enables you to provide decision-makers within your organization access to the solutions.

Benefits of a modern data structure

Modern data architecture has the potential to change your organization for the better.

Optimized integration

Your organization should be able to bring all scattered information as a single entity so that you can draw valuable insights for your business. Good data architecture empowers you to weave through information easily and helping you pick out relevant input from various data sets. With a converged data architecture you can be more innovative as a company.

Increased efficiency

Cloud and edge computing have enabled organizations to share data within different teams. Good data architecture should be able to seamlessly fit such a dynamic platform or technology in your systems. Additionally, cloud systems enable you to leverage the storage solutions they offer. The trade-off between computing and storage is a lot easier these days.

Diverse data

It is common knowledge that a robust system that handles different kinds of data with ease should enable you to leverage new technologies with ease. As mentioned earlier, good architecture is flexible. This makes data transformable into various forms.

Steps to design good data architecture

So far, we have look at the features and benefits of good data architecture. Let's now explore the steps involved in building one. There are three important steps when it comes to building good data architecture.

Step 1: Defining data strategy

Before setting up data architecture for your organization, you need a data strategy. It's the guiding framework when you begin building the architecture. An ideal strategy will show you how you plan on using data to influence your business and its decisions. The more elaborate your data infrastructure, the more detailed your strategy should be. It highlights all the aspects that can influence the performance of your business. And your data architecture is a part of the whole data strategy. Though the architecture itself stems from the plan, its components inform business decisions.

Step 2: Establishing data governance

Building data architecture without data governance is a sure step to failure. Teams in your organization can change the architecture depending on their requirements to meet their specific business goals. Though the intentions of such changes are good and the variations may seem harmless on face value, it results in disruptions that affect the overall organization and you cannot make the most of the strategy. With data governance in place, you can ensure that everyone within the organization use data in the right way.

Data governance also ensures that your data architecture goes beyond being simply a technical infrastructure. Practices and processes revolving around data usage now become centralized.

Governance also touches on aspects of organizational culture. This defines how data can be used based on your employees' roles, responsibilities. This, in turn, ensures that data errors are identified and addressed upfront.

Step 3: Connecting data architecture to data modelling

By now you'd be cognizant of the fact that designing your data architecture to work in isolation is a bad idea. All through, the data strategy you build in step 1 will guide you on what you need to include in the architecture, data governance permitting it.

Something that is still missing is a description of how these different parts of the data ecosystem will interact with each other. Enter data modelling.

Data modelling (usually confused for data architecture) gives you a clear picture of how various data structures in different databases work together. Data models enable architects to use various data components to positively influence business decisions and improve business outcomes. By using data models, you will not miss out on any of your data

Conclusion

Data architecture helps your organization create a way forward for the next few years. It also helps you choose technology that's best for your organization, setting you up for success.

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