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People have been poring over the nature, meaning, and value of data for centuries. The idea that it could be mechanically structured and managed dates back at least to Charles Babbage and his hypothetical Analytical Engine—in which concepts of modern data science can be glimpsed. Flash forward about 200 years, and now the voluminous data that modern enterprise-scale organizations regularly generate requires cutting-edge cloud computing technologies and clever-yet-practical design patterns.
Enterprise data architecture supports efforts to leverage data as effectively as possible to benefit the bottom line. No exploration of major data trends could be comprehensive without a discussion of data architecture and how best to design and implement it at scale.
What is enterprise data architecture?
Enterprise data architecture can be thought of as a roadmap that depicts data locations, data flow, and data availability.
Roots in enterprise architecture
When exploring this topic, it helps to consider the overarching methodology of enterprise architecture—a system that a business adopts to effectively structure its operations, technology, and other resources.
Business processes and projects are supported by data, which is generated, collected, processed, analyzed, and stored using various business applications. These apps are undergirded by technology infrastructure—host devices, drives, servers, network switches, and so on. This framework allows organizations to analyze, design, plan, and implement their mission-critical strategies and initiatives.
Undoubtedly, data architecture is a key domain within enterprise architecture. In a common data architecture standard like The Open Group Architecture Framework, it represents the structure of an organization's data management, maintenance, and storage processes, including logical and physical data models.
Supported by leading tech
Data architecture in today's enterprises requires leading-edge technologies and best practices. For example, an enterprise data architecture might be supported by a hybrid cloud deployment that utilizes multiple clouds, along with advanced database management system (DBMS), analytics, and storage tools. Organizations at this scope cannot properly collect, store, transform, and use their data to its fullest potential without a sensible architecture and the technology to optimize it.
Why is reliable data architecture important for enterprise operations?
Strong data architecture supports mission-critical business applications and resources by providing a foundation and clear path for the data they rely upon. It will only become more important in the years to come.
Data volume
The clearest reason for data architecture's increasing importance is the sheer scope of data enterprises will use. Between 2021 and 2025, Statista predicts worldwide data generation will jump from 79 to 181 zettabytes per year, increasing at a 23% compound annual growth rate—much of which will be used by enterprises. This must be accounted for in the setup and oversight of data architecture.
Increasing tech adoption
There are also advanced technologies to consider. Multi-cloud and hybrid multi-cloud platforms are becoming the enterprise norm, which has significant ramifications for the management of data: Gartner has projected that by the end of 2022, approximately 75% of databases will be either original cloud deployments or have been migrated from on-premises infrastructure.
At the same time, large organizations are using artificial intelligence (AI) and machine learning (ML) more often. The complexity, wide variety of data sources and formats, and data volume necessary for these initiatives further highlights the need for modern enterprise data architecture.
Key characteristics of enterprise data architecture
The following elements should be a part of an effective enterprise-scale data architecture:
Enablement of automation
Automation is no longer uncommon in the business world, but rather a resource that enterprises can leverage in a modern data architecture. Data pipeline and data flow structures are logical areas to automate. Doing so accelerates the speed and efficiency of data transit throughout the organization and allows for simplified data integration. AI and ML are also vital to data processing, data cleansing, data mining, and analytics.
Furthermore, automation can help to curate and clean new data to ensure data analysts and scientists spend their time getting value from data rather than dealing with quality and standardization issues.
Optimized use of cloud technologies
The multi-cloud and hybrid cloud architecture that today's enterprises have ready access to opens up a range of possibilities for organizations' data analytics operations as wide as that of AI and ML. In particular, readily deployable data warehousing, storage, and analysis tools in cloud environments grant data architects the freedom of virtually unlimited scalability.
This is an invaluable resource, given that the volume and formats of structured data and unstructured data passing through an enterprise's apps and systems fluctuate rapidly. The freedom of the cloud allows data teams to quickly set up a colossal data lake when storage must be scaled up at a moment's notice. Similarly, scalability allows data scientists and analysts faced with sudden spikes in data queries to spin up compute resources to handle the uptick and scale them back down when activity normalizes.
Enterprise data architecture also plays a key role in securing and organizing data, making the deployment of hybrid multi-cloud technology more efficient. Without the strategic approach and structure that data architecture allows, an organization can end up with a messy implementation that quickly eats up the budget without producing significant value.
Leveraging streaming data and real-time analytics
Things move quickly in the enterprise business world, and data is certainly one of them. In fact, in certain operations, it never stops moving—this is called streaming data or continuous real-time analytics. Streaming data has many financial services applications—like credit scoring and card transaction processing—and is also useful for customer activity analysis and predictive analytics for industrial maintenance.
Any enterprise data architect must design their organization's data ecosystem to support real-time data analysis. Streaming analytics are necessary for analysis processes that can't wait for the time-consuming steps of batch processing. A well-thought-out data strategy and strong architecture makes it easier to plug in new streaming data sources or transition pipelines from batch to real-time.
Reliable data sharing
The specifics of how data is shared across an enterprise vary, but it's categorized broadly as a necessary aspect of modern data architecture. Application program interfaces (APIs), data integration tools, and emerging data design patterns such as data fabric all contribute to better enterprise data sharing and greater availability of information for business users. This enables more effective collaboration in all units of the business.
Comprehensive security
Taking the most comprehensive security approach possible is paramount for enterprise data architecture. Cybersecurity solutions can include everything from encryption and endpoint detection and response (EDR) tools to cutting-edge firewall as a service (FWaaS) solutions and zero-trust network access (ZTNA) policies. That said, these security measures must not infringe upon the ability to share data as needed.
4 benefits of enterprise data architecture
When properly designed and implemented, enterprise data architecture allows organizations to realize numerous benefits. The following are some of the most notable.
1. Increased visibility
Data underlies virtually all business processes in an era where it is so abundant. Enterprise data architecture grants leaders the comprehensive visibility and oversight necessary to have a clear understanding of how data captures operations and critical concepts throughout their organizations. This helps create a unified source of truth and brings the enterprise toward a more data-driven vision.
2. Better decision-making
On the other end of the spectrum, individual business users must have appropriate access to all data relevant to their daily responsibilities. Well-constructed enterprise data architecture that allows for simple sharing not only facilitates better overall decision-making, but also allows the implementation of cutting-edge concepts like decision intelligence.
3. High data quality
Modern enterprise-scale data architecture contributes positively to data quality through the implementation and use of well-defined data standards, definitions, formats, and domains, which allows for more reliable reporting along with data reuse.
4. Bottom-line gains
In the long run, increased efficiency across critical data processes leads to the more effective application of data by end users. This supports the bottom line in numerous areas, whether it's cutting down on operating expenses or improving the end quality of a product. Ultimately, data architecture also facilitates the enablement of new business lines by allowing data to more easily be shared with third-party partners.
Forging an enterprise data architecture that's built to last
Creating an effective data architecture in an enterprise environment can't be an instantaneous process. The data strategy that informs an enterprise data architecture must be crafted with the agility that the business needs in mind.
Begin by taking stock of the organization's most pressing data needs that support key business initiatives, while also inventorying resources—tech and personnel alike. Experienced data architects can handle the planning stages with relative ease, but they should oversee the implementation of the architecture with the aid of a senior data engineer—as well as data scientists, analysts, quality specialists, infosec experts, and other collaborators.
Because the cloud is such an ideal foundation for modern data architecture, the adoption of leading- and cutting-edge cloud technologies will be essential, if present resources aren't up to the task. Deploying a hybrid multi-cloud solution allows your data architecture to bridge the gap between the cloud and the data center, thus preventing siloing. Meanwhile, a versatile, cloud-ready data analytics platform like Teradata Vantage is integral to achieving the data integration and oversight that the architecture needs.
To learn more about Vantage, check out the 2021 Gartner Magic Quadrant for Cloud Database Management Systems report, in which Teradata was named a leader of the field.
See the Gartner Magic Quadrant Report