What is Data Architecture?

In the last couple of years, firms have relied on data and information to create new business models. Back in the day, Data Architecture was a technical decision. Times have since changed. Data Architecture now creates a middle ground between technical execution and business strategy. 

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."

 The strategy of any organization relies on effective use of data. Data Architecture provides a set of policies for a solid foundation in any business model. Data Architecture has guidelines for many processes. This processes include, data collection, usage, processing, storage, and integration with different systems. 

 The individual components of Data Architecture include the outcomes, activities, and behaviors. These components cover the artifacts, means of implementing the architecture's intentions, and the different interactions.

In this data architecture guide, we will go through all the components of a data architecture. We'll also see how these solutions can make life easier for your data team.

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A Shift from Ancient to Modern

Data architects align the data environment of an organization with their strategies. Keeping in line with the tenets of a good architecture, architects work from the consumers to data sources. These practices customize the architect to the specific requirements of the organization.

Static data warehouses were the order of the day in years past. These warehouses hardly responded to the constant changes in the business environment. Organizations ended up with a raw deal. The frustrations from minimal ROI led to new data solutions that adapt to changes in the market. Organizations have also used data lakes to store raw data. Though the data lakes require large storage capacities, firms can analyze the data for any purpose. Lack of efficient data governance strategies has, however, plagued this resource.

While the present-day data architecture will still have a data warehouse, there's more to it. The warehouse is part of a data environment that is both flexible and agile. Each individual receives tailored access from the adaptable architecture. 

Building a Modern Data Architecture – Things to keep in mind

 Here are a couple of factors to consider when building a modernized architecture.

  1. Identify the most valuable data types. You want to only work with information that has a direct impact on your business. Leverage today's cloud offerings to manage such data without having to break the bank.
  2. Govern data well. Ensure that your architect is clear on the quality and relevance you want. Create committees to guarantee the quality of data.
  3. Create flexible systems. Building models that last are a more popular option for business owners. The best data architectures, however, are those that change with time. Such designs have no problem blending in with new solutions that are on the rise.
  4. Create a real-time data environment. Combine data warehouses and new approaches to ease real-time access to historical and current data. The idea is to ensure that decision-makers have everything to make spot-on decisions.
  5. Secure your architecture. Advances in data management systems have not been without a fair share of challenges. Threats from malware and viruses are always lurking. These threats come from different places, meaning that you'll need to be on high alert to keep your data intact.
  6. Use data as a service. Data as a service ensures that you can integrate all your systems with ease. This service works like software as a service (SAAS) solutions that lighten your work with different software. Create an internal cloud and offer your organization's decision-makers access to customized solutions.
  7. House all the components in a general system. A master data management strategy allows your firm to adapt to acquisitions, mergers, and realignments. You will get rid of any duplications from parallel databases and keep your business goals.
  8. Provide customized solutions. Management doesn't have to depend on IT before calling the shots. The business owners can use front-end interfaces to run queries and get prompt results. The tech guys will then specialize in security and data governance. 

 What are the benefits?

 Here's how a good, modern architecture will change your organization for the better.

  • Enhanced Integration – Your organization needs to combine scattered information to get accurate business insights. A good data architecture enables stakeholders to weave through information with ease, picking out relevant input from different data sets. With a converged architecture, your organization may well be on the way to more innovation and better creativity. An efficient data integration strategy will take care of migration, conversion, and connection of different data points.
  • Increased efficiency with dynamic platforms – Cloud and Edge Computing, among other technologies, have ensured that organizations can share data within the different sections. With a good architecture, you should be able to fit such technology in your systems.
  • Support for diverse data. A system that handles different data types with ease allows you to leverage robust, newer technologies. Good Architecture is flexible. This characteristic allows your data management team to transform data into various forms.
  • Easier evolution of your products. From Redshift, Snowflake, BigQuery to Azure SQL Database, the technology landscape is always on the move. Newer and better technologies continue to see the light of day. Data architecture ensures that your organization keeps in step with emerging technologies. The onus is on organizations to ensure that their systems are robust enough to adapt to such technologies. 
  • Better storage management. Cloud systems have offered great storage solutions for your organization to leverage. The trade-off between computing and storage has become much easier.

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 Steps to Designing a Data Architecture

Now that we have a good idea of what data architecture would entail, let's look at the steps that go into creating one.

1. Have a data strategy in place

Before setting up your organization's data architecture, you'll need to be clear on your data strategy. An ideal strategy will show how you intend to use data to influence your business. In the words of Donna Burbank, Global Data Strategy's MD:

"Your organization's business model and strategy inform the direction you take as you create your data strategy. The data strategy then gives you a clear picture of your client. You should be able to tailor your product line to fit the needs of the customer. You get to improve customer service in the long run." 

For an upturn in an organization's business impact, elaborate data infrastructures are necessary. The data strategy highlights all the areas that can influence the business' performance. Your data architecture is part of the whole strategy. 

Your data team can use information in data architecture to strengthen your strategy. So while the architecture stems from the plan, its components inform the output of the policy. 

2. Decide how you'll govern data

Data architecture minus data governance is a recipe for failure. Members of your organization can change the architecture to meet their end of the business strategy. Diverse viewpoints receive part of the blame for such changes. While these variations may look harmless on face value, your organization won't make the most of the strategy.

With Data Governance, you get to ensure that everyone uses data in the right way. Data governance also ensures that your architecture goes beyond the technical infrastructure. The practices and processes around data usage become centralized. 

You need your data strategy to handle the organizational culture. This feature goes beyond the clear operational technologies. Data governance supports your strategy in this regard. The governance strategy will touch on roles, responsibilities, and compliance matters. 

Governance ensures that any upfront errors do not impact the whole process of handling data. Good data governance also reduces the risks of errors from start to finish.

3. Connect the architecture to data modeling

It is becoming clearer that you shouldn't design your data architecture to work in isolation. The data strategy guides you on what to include in the architecture while data governance allows you to make the most of the architecture. One thing is still missing – a description of how different parts of the data ecosystem interact.

Data modeling covers you in regards to data relationships. You'll get a clear picture of how data structures in different databases work together. Data models ensure that architects use various components to improve business outcomes.

With the models, you won't miss out on any of your data assets. From the entities to the attributes and relationships, your team will identify weak links with ease. In case the team finds any issues, they won't have a hard time with the resolving such. In a workflow diagram, the dotted lines represent the interactions between the parts of the data architecture. 


In essence, data architecture helps your organization chart a way for the next couple of years. This component of the business also enables you to choose the best technology to pick for the greatest success. Remember to make provisions on how well you can integrate these emerging technologies in the data architecture.

With all this information in mind, you need a partner that will help you govern your data for your data flows. Such partners help you to enhance the efficiency and accuracy of your architecture. If you need a tool to integrate with your data, try Xplenty.  Xplenty is a cloud-based, code-free ETL solution that provides simple, visualized data pipelines across a wide range of sources and destinations. To set up a demo and a free 7 day trial, contact us here