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Data architecture: Definition, Overview, Components Explained

Big Data is omnipresent – and that is no news today. Businesses have long realized the power of big data that was traditionally stored and held in physical silos but then moved to the cloud. However, this data is extremely random, varying, and inconsistent. To truly derive insights from the heaps of data, it is essential to begin by sorting it out and arranging it in a structure that is easy to read, understand, and analyze. That is precisely where data architecture enters the scene. 

Let’s take this opportunity to better understand data architecture – what it is, how to understand it, and what common data architectures are used by businesses today. 

What is Data Architecture?

Data Architecture, in simple terms, can be understood as a standardized process of collecting, storing, manipulating, sharing, and using data. The idea of data architecture is to help in the analysis phase and ensure that relevant data reaches the required people whenever needed – so that they can further use it as they require. 

Traditionally, data engineers would use custom SQL queries to manipulate and share data. This was a tedious and time-taking process and often left the non-technical recipients of this information at a loss. This approach wasn’t adequate to meet the data requirements as it was limited to business strategy making and directly proportional to the strength of their IT team. 

However, with the growth of real-time data from all sources, businesses have been pushed to re-look at this strategy for good. That has led to the realization that the old system doesn’t cut it for this day and age. Instead, modern-day data architecture puts business strategists and data engineers/architects at the same table, thereby fostering more transparent communications – resulting in better insights and actionable information transfer. 

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Importance of Data Architectures

Well-designed and adequately managed data architectures form the backbone of any successful data management process. Data architecture is all about data integration, quality improvement efforts, data preparation, and engineering. By applying internal data standards, data architecture promotes better data governance and security. These things provide businesses with the advantage of accurate and consistent data. 

Data architecture also forms the foundation of further data strategies, and plans will be built to help achieve business goals. Just this fact makes data architecture an inseparable part of the entire data analysis cycle. Data architecture is fundamental for more than one reason and puts businesses in a comfortable seat when it comes to managing and understanding their data. 

With the importance of data architectures in place, let’s talk about some crucial characteristics of good data architectures. 

Characteristics of a Good Data Architecture

As mentioned earlier, one of the primary purposes of data architecture is to organize data to make it easier for relevant people to access and use it whenever required. Apart from that, some common characteristics of a good data architecture include the following: 

  • A business-oriented approach that takes organizational strengths and requirements into consideration. 
  • Scalability and flexibility to meet the growing business need ahead of time. 
  • Proper security measures to prevent unauthorized access and data fraud. 

Data architecture does not necessarily include tools, platforms, and other technologies from an extremely theoretical standpoint. Instead, it is a theoretical framework/infrastructure described by a connection of various components in a diagram. Data management teams use this architecture diagram to guide tech deployment and further data management. In that light, some important components of data architecture include: 

  • Data models, definitions, and vocabulary for any jargon-like elements. 
  • Flow diagrams to illustrate the flow of data through different systems and applications. 
  • Elaborate documents that map usage to business processes. This can include a CRUD matrix (create, read, update, delete). 
  • Other supporting documents elaborating the business goals, functions, and concepts to align data management initiatives accordingly. 
  • Standards and policies governing data acquisition, storage, integration, transformation, and use. 
  • High-level architectural blueprint describing the different layers for various processes like data ingestion, integration, storage, usage, and more. 

Let’s now move on to some data architecture frameworks that are available and widely used by modern organizations. 

Data Architecture Frameworks Available for Businesses

Businesses can use standardized and templatized architecture frameworks instead of creating one from scratch to implement their data architectures. Broadly three framework options exist for organizations, and those are: 

  • DAMA-DMBOK2: This stands for the DAMA Guide to the Data Management Body of Knowledge. This is a data architecture framework created by DAMA international. Currently, this framework is in its second edition, and it addresses various data management and data architecture disciplines. 
  • TOGAF: TOGAF is an enterprise data architecture methodology created in 1994 and has been updated multiple times since. It was developed by The Open Group and has proved beneficial for businesses across the globe. 
  • The Zachman Framework: The Zachman Framework is an ontology framework that uses 6×6 matrices to describe business architectures, including various data elements. This framework doesn’t include implementation methodology but is meant to serve as the skeleton for architecture. 

We’ve restricted the description of these frameworks since it falls out of the purview of this article. However, we encourage you to read more about these frameworks and get some hands-on knowledge! 

With that settled, let’s now move on to the more important question – what roles and opportunities are available in the data architecture domain? Let’s see! 

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Different Roles and Opportunities in Data Architecture Design and Development

Data architects are generally at the top of any data architecture initiative hierarchy. Data architects need a command of different technical skills along with various soft skills to manage teams and communicate with business owners. Data architects spend a lot of time working with end-users to document data usage and business processes and gather new requirements. On the technical side, data architects work on creating models and supervise model creation by others. They are also responsible for building architecture blueprints, flow diagrams, documents, and other vital pieces. 

Other duties of data architects involve outlining data integration processes and overseeing the data development processes, all while staying in line with the business goals and requirements. Data architects are also often responsible for designing data platforms and selecting required technologies to work on. Apart from data architects, other professionals are also involved in the entire data architecture design and development strategy. Those include: 

  • Data modelers: They work with end-users to determine precise business needs and review data processes accordingly. Using this information, they create reliable and scalable data models. 
  • Data integration developers: Data integration comes in handy once the data is implemented. The task of data integration developers is to create ELT and ELT jobs that will integrate various data sets. 
  • Data engineers: Data engineers are responsible for funneling data to data analysts and other scientists by building pipelines. They also help with the data preparation process. 
  • Data scientists/analysts: Data scientists or analysts work at a slightly higher level and try to derive insights from data engineers’ data architecture and integration. Their role is to study all the data and find patterns and information businesses can use to make better-informed decisions. 

In conclusion

Data architecture is an extremely important aspect of the entire data management and analysis process. It is clear that without a well-planned data architecture, all other strategies are bound not to achieve their full potential. This is what also makes data architecture one of the more sought-after parts of the software development process. 

Essentially, data architecture opens two doors for people – either they can dive deeper into data management and analysis, or they can develop software with well-managed data architecture. 

At upGrad, our Executive PG Program in Software Development is perfect for aspiring software developers, IT professionals, engineers, analysts, and freshers wanting to kick-start their careers in software development. The course offers you a complete journey into software development, including data architecture. 

Is data architecture limited to data analysis?

No, data architecture is not just limited to data analysis, although it is an integral part of it. Data architecture is primarily related to software development and the architecture required to manage the data collected by the software on a real-time basis.

What should data architecture include?

In general, your data architecture should include the models, policies, rules, and standards that govern the collection, storage, manipulation, integration, and use of data.

What are the various jobs in data architecture design and development?

Data architects, data integration designers, and data engineers primarily work from the beginning to the end of the data architecture design and development process.

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