What is Data Architecture and how is it different from Information Architecture? - Data Architecture is a field that documents an organization’s data assets, plots how data flows in its systems. And provides a blueprint for managing data. The goal is to ensure that the data is properly managed and meets the business needs for information.
While data architecture can support operational applications. It explicitly defines the underlying data environment for business intelligence (BI) and advanced analytical initiatives. Its output includes a multi-layered framework for data platforms. And data management tools, as well as specifications and standards for data collection, integration, conversion, and storage.
Ideally, designing a data architecture is the first step in the data management process. This is not usually the case, however, as it creates incompatible environments that must be coordinated as part of the data architecture. Also, despite their fundamental nature, data architectures are not fixed. And need to be updated with changing data and business needs. This makes them a constant concern for data management teams.
Data architecture is associated with data modeling, which creates diagrams of data structures, business rules. And relationships between data elements. However, they are separate disciplines of data management.
This guide explains what data architecture is, why it matters, and what benefits it provides to the business.
How has data architecture evolved?
In the past, data architecture was less complex than it is now. They mostly consisted of structured data from transaction processing systems that were stored in relational databases. Analytics environments consisted of a data warehouse, sometimes constructed with smaller data for business units. And an operational data warehouse as a staging area. Transaction data is processed for analysis in batch tasks, using traditional extraction, conversion and loading (ETL) processes to integrate the data.
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Since the mid-2000s, the adoption of Big Data technologies in businesses has added unstructured and semi-structured forms of data to many architectures. This led to the establishment of the Data Lake, which often stores raw data in its original format instead of filtering and converting it for pre-analysis. This was a big change in the data storage process. The new approach is to make wider use of ELT data integration, an alternative to ETL that reverses the load and changes the steps.
The increasing use of streamline processing systems has also brought real-time data to the data architecture. Many architectures now support artificial intelligence and machine learning applications, in addition to basic business intelligence and reporting driven by data warehouses. The shift to cloud-based systems has added to the complexity of the data architecture.
Another emerging architectural concept is Data Fabric, which aims to simplify data integration and management processes. This concept has a variety of potential uses in data environments.
Why is data architecture important?
A well-designed data architecture is an important part of the data management process. The data architecture supports data integration and data quality improvement efforts, as well as data engineering and data preparation. It also enables effective management and development of internal data standards. These two factors, in turn, help organizations ensure that their data is accurate and consistent.
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Data architecture is also the foundation of a data strategy that supports business goals and priorities. “Modern business strategy depends on data,” wrote Donald Farmer, managing director of Tree Hive Strategy, in an article on key components of data strategy. Farmer said this makes data management and analysis so important that it should not be left to individuals. To manage and use data well, an organization needs to create a comprehensive data strategy that underpins a strong data architecture.
Conclusion
As data, analytics, and artificial intelligence become increasingly integrated into the day-to-day operations of organizations, it is clear that a completely different approach to data architecture is essential to building and growing a data-driven company. Those data and technology leaders who welcome this new approach will better position their companies on the path of agility, flexibility and competition for everything that lies ahead.
