Data Architecture

Data architecture is a framework that guides how to collect, store, manage, and use data in ways that support an organization’s business goals.

Architecture isn’t infrastructure — it doesn’t specify hardware or technologies. Instead, data architecture maps the infrastructure and sets the policies and standards that shape how the infrastructure manages data.

What is the objective of data architecture?

The point of data architecture is to ensure enterprise data management aligns with and supports business strategies. When a data architecture framework guides how companies manage and use data, they make better data-based decisions while optimizing the cost and performance of their data assets.

To achieve this objective, enterprise architectures must address these three areas:

1. Data connectivity

Effective decision-making depends on having a holistic view of all data, no matter where it resides in the IT infrastructure. Data scientists, business intelligence analysts, and general business users need to know they can pull data from anywhere in the company.

Company-wide standards for metadata generation harmonize data across different data systems and domains, making discoverability easier. Policies governing when, where, and how data may move reduces the risk of duplicative and stagnant data.

2. Data access

Data-driven enterprises can only succeed by making data accessible to business users who need it. Data democratization speeds time-to-insight by making it easier for data analysts to get the data they need using tools they are already familiar with, such as SQL.

Data architectures set policies that democratize access and promote data literacy. Everyone who analyzes data should have the access, tools, and skills they need to succeed.

However, democratization cannot mean universal access. Data security and compliance risks require architecture policies that balance data democratization and data protection.

3. Data quality

How a company collects and maintains its data determines the quality of its decision-making. At best, poor-quality data forces executives to wait as data engineers bring stale, inconsistent, and inaccurate data into a usable state. At worst, the company bases its decisions on incorrect information.

Data teams rely on architectural standards to build data management processes and data quality control procedures. These data standards clarify business priorities, allowing teams to make better acquisition and development decisions.

With strategy-driven data management practices, companies make business decisions based on high-quality data.

What are the 3 most important things when considering data architecture?

Many companies create their data architecture long after building their IT infrastructure. They must replace their de facto practices with an intentional data architecture that supports business needs, makes decision-making more responsive, and protects the company’s data.

1. Business alignment

Everything the company does must serve its long-term plans, so corporate strategy is the touchstone for data architecture.

Data architects must understand the priorities of board members, executives, and other stakeholders. From these business requirements, they can develop the key elements of an effective data architecture, including:

  • Types of data,
  • Data volume and velocity,
  • Level of data integration, and
  • Data usage patterns.

2. Scalability and flexibility to future-proof your architecture

Within a single career, IT infrastructures evolved from data centers to data warehouses to data lakes and now to data meshes and fabrics. On-premises systems morphed into hybrid-cloud implementations. Where previously overnight batches delivered raw data and analysis results, today’s real-time data streams require instant access and automation.

Scalability and flexibility are hallmarks of robust data architectures that can handle ever-increasing data complexity. For example, modern data architectures rely less on data warehousing, instead decoupling storage and compute so companies can optimize each without constraining the other.

3. Data governance and security

Architecture complements governance. Data architectures map how data flows within a company’s data infrastructure, while governance policies focus on how people use that data. Security and compliance are essential factors in both.

Data architectures, for example, may set guidelines to minimize data duplication. Besides reducing storage costs, de-duplication makes sensitive data easier to secure and minimizes the impact of security breaches.

Meanwhile, governance policies describe the people, purposes, and contexts in which to permit access to sensitive data. Effective governance policies depend on the data architecture’s description of data storage and data flows.

Related infographic: Snowflake data warehouse vs. Starburst data lakehouse TCO and Performance Comparison

What are examples of data architecture?

A data-driven culture is the secret sauce powering the most successful companies in every industry. These three use cases describe how industries rely on data architectures for better decision-making.

Financial data architecture examples

Financial systems generate petabytes of daily transaction data that industry and regulators must monitor for signs of fraud, insider trading, money laundering, and other crimes. These stakeholders include decades-old enterprises with data architectures encrusted with significant technical debt.

Financial data architectures provide the framework for collecting, storing, and managing this flood of high-velocity data. At the same time, these architectures must support the industry’s risk management priorities by providing rapid, accurate, and actionable insights into suspicious activity.

Healthcare data architecture examples

Healthcare organizations operate in a fast-paced environment in which lives depend on providers getting accurate, speedy insights into their patients’ conditions. Simultaneously, healthcare regulations demand that these organizations protect patient health records.

Healthcare data architectures address several challenges, including multi-location operations, a revolving mix of employees and third parties, and various internal and external sources of patient records.

Retail data architecture examples

Fast fashion, direct-to-consumer, and other data-driven retail business models depend on data architectures that reveal customer insights and support rapid decision-making.

These retail data architectures must cover every sales channel and customer touchpoint to deliver 360-degree views of consumer behavior. Linking this data with point-of-sale, inventory, and supply chain systems lets retailers forecast demand and manage inventory more efficiently.

Data architecture components

Every company will develop a unique data architecture that reflects its infrastructure, culture, and business culture. That being said, every data architecture consists of the following elements:

1. Data sources

The first step in architecture design is to map the organization’s data sources. Startups have the luxury of starting from a blank slate — although many do not have the luxury of time.

Most companies begin the design process long after their infrastructures are in place. Architects must identify every data source among their companies’ mix of legacy, on-premises, cloud, and third-party data platforms.

2. Data integration

Data architectures define data structures, formats, and schema to make the contents of these disparate sources accessible and usable by business users.

These standards are easy to implement in new systems. However, changing legacy data storage systems may be impossible. Data teams must develop fresh extract, transform, and load (ETL) data pipelines for ingesting and data processing.

3. Data storage

Data architectures also define where and how companies store data. Companies increasingly rely on the scalability of cloud-based data lakes to handle the accelerating volumes of big data. Yet, the cloud has not completely replaced on-premises relational databases and data warehouses, which may offer regulatory, performance, or other advantages.

Architectures must address when it’s appropriate to use each type of data storage as well as the criteria for moving or copying data from each location.

4. Data modeling

Data models visualize data flows within the organization. But these are not roadmaps tracing network connections between systems. Instead, models show how data flows through specific decision-making processes, from the sources to the final analysis presentation.

Modeling existing data flows lets companies identify where to bring data management practices into compliance with architecture policies. Data architecture will inform the creation of data models to guide the development of new data products.

5. Data governance

Data architects play a significant role in a company’s governance process. As mentioned earlier, governance and architecture are closely related. Architecture provides the technical framework data governance teams use to develop effective policies.

6. Analytics and reporting

Data architecture’s first priority is to support the organization’s business goals. It must go beyond data management to guide the company’s data analytics. The right approach will let users access data at the source, speed time to insight, and make data teams more productive.

In addition, the size of today’s data sets means architecture frameworks must support the replacement of traditional data mining techniques with machine learning and artificial intelligence.

Related videos: Modern data architecture videos

Data analytics architecture vs data architecture

Although the term data analytics architecture is superficially similar, it is conceptually much narrower in scope. Data analytics architecture refers to the infrastructure and tools supporting data analytics, including query engines, business intelligence software, and visualization systems; while data architecture focuses on the overall structure, organization, and governance of data.

Related webinar: How to integrate your data lake and data warehouse as a single semantic layer for analytics

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