Table Contents
What Is Data Architecture? Definition and Key Concepts
Data architecture is the blueprint for how data is collected, structured, transformed, managed, stored and used in an enterprise. This comprehensive framework manages how information flows through systems, enabling data-driven decision making and ensuring data quality, security and alignment with business goals.
The purpose of data architecture is to define, enable and orchestrate consistent, accurate and accessible data flows through different systems. Clear data requirements are essential for designing and implementing effective architectures that support organizational strategies and compliance. This helps users easily make better data-led decisions and unlock insights for competitive advantages.
Without a robust data architecture framework, organizations face data silos, quality issues and limited accessibility, creating bottlenecks for efficiency and growth. Effective data management focuses on understanding information movement and structure, emphasizing the importance of proper data models, storage solutions, integration tools, and governance mechanisms within the overall infrastructure.
The Key Components of a Modern Data Architecture
A modern data architecture comprises platforms, tools and applications that collect, manage and distribute data across the organization.
Data Storage – Data is collected, stored and managed from various internal and external sources, in both structured (spreadsheets, CRM, sales, databases) and/or unstructured (social networks, emails, videos, images, third-party APIs, etc.) formats. The organization can leverage data lakes, warehouses and other storage solutions to handle structured and unstructured data efficiently.
Data Management – This layer has two major deliverables: the integration and governance of multi-structured datasets to support analytical decisions and action. Data from various sources, whether on-premises or in the cloud, has to be connected, enriched, transformed and processed to deliver a unified view to business users. Data quality, security and compliance with industry, government and privacy regulations is non-negotiable. Data must be accurate, complete and consistent so that business users can confidently use it for smarter decision making.
Data Consumption – Modern architecture should orchestrate data flows across distributed infrastructure that spans multiple data centers, regions and/or clouds, so that business users across the organization can access the right data for their diverse use cases.
The Evolution of Data Architecture: From On-Premises to Cloud to AI-Ready Systems
As business landscapes evolve and data shifts from the information era to the AI era, data architecture designs must keep pace.
Initially, enterprise data lived on physical servers within the company premises, managed manually by large IT teams. This on-premises approach set the ball rolling for data-led decision making but was expensive, data siloed, fragmentated and struggled to scale with the rate of business growth.
Innovations to address these challenges led to cloud data management, ushering in an era of scale, flexibility and operation efficiency. Remote servers hosted by cloud service providers such as AWS, Azure and Google Cloud freed enterprises’ infrastructure burdens, offering scalable, flexible, pay-as-you-go storage and computing. Cloud also enabled streamlined data integration and automation, democratizing data access and freeing valuable IT resources.
Not only did cloud computing liberate companies from the large upfront investments needed for expensive server infrastructure and maintenance, it also gave them flexibility to add or reduce data storage and computing power as needed, paying only for the resources used.
Today’s data teams manage not only large volumes of structured enterprise data, but also unstructured and semi-structured data from a variety of new internal and external sources. They balance on-premises, private cloud and public cloud environments to optimize their data architecture.
With AI now central, data management has become even more advanced and intelligent, requiring specialized data fabric approaches for AI-powered architectures that can support continuous model training, real-time inference, and the automated governance essential for trustworthy AI outcomes.. AI and ML capabilities for advanced analytics, predictions and automation are now part of data management. AI-driven tools integrate data seamlessly, detect and prevent security threats proactively and safeguard data privacy.
Despite these technology advancements, designing and implementing an agile, scalable and flexible data architecture to handle emerging use cases and new technologies is complex and remains a challenge. (See Figure 1.)
Figure 1. Timeline showing evolution from early to modern data architecture
The Business Case for Modernizing Data Architecture
Outdated data architectures struggle to meet the demands of modern analytics, real-time decision-making and emerging AI use cases. The consequences? Missed opportunities, inefficiencies and competitive disadvantage. A good data architecture standardizes data storage, reduces redundancy, and improves data quality, leading to more efficient analysis and integration.
Investing in a modern data architecture that supports business and innovation goals can put you in a stronger position to navigate constant change with greater confidence. Modern data management leverages advanced tools and cloud-based solutions to handle increasing data complexity and volume, aligning technology with business strategy.
Unlock Data as a Strategic Asset
Modern architectures enable more robust data enrichment, transformation and integration. They support real-time data streaming, processing and analytics, delivering a 360 view of organizational data essential for AI use cases. Both are crucial for industries relying on immediate insights, such as finance, e-commerce and healthcare. Built-in governance and quality capabilities ensure data remains reliable, secure and compliant despite the complexity of formats, sources and flows.
Improve Cost and Operational Efficiency
A modern data architecture enables data democratization. Trusted and timely enterprise data is available for consumption and sharing across business teams in a seamless, governed and optimized manner, giving users simplified data access through the marketplace experiences they demand.
Be AI-Ready and Future-Proof
Modernizing your data architecture is essential to enabling petabyte and exabyte-scale AI training and analytics. Modern architectures such as data fabric and data mesh prepare your business for any change by enabling seamless data integration and movement across the enterprise, accelerating decision-making and innovation.
Data Architecture Modernization Challenges: Risks of Outdated Systems
Rising Operational Costs From Legacy Data Systems
Maintaining multiple disconnected legacy systems is expensive. Data silos and manual processes add bottlenecks, errors and inefficiencies. Organizations with fragmented legacy systems face exponentially higher maintenance costs as data volumes grow, creating unsustainable cost trajectories.
Data Security and Access Risks in Outdated Architecture
Data quality, governance, and security-related risks and vulnerabilities increase when data is fragmented and access cannot be monitored effectively. Without centralized governance, organizations face increased compliance violations and data breach risks. Manual oversight becomes impossible at enterprise scale.
Limited AI and Modern Workload Support
Older systems often lack the scalability, performance and flexibility to handle real-time processing, AI-driven workloads or emerging use-cases effectively. Legacy systems create bottlenecks that prevent real-time analytics and machine learning initiatives, limiting competitive advantage in data-driven markets.
Modern Data Architecture Best Practices: 5 Key Implementation Considerations
A modern data architecture should be built on a robust foundation and a well-defined strategy encompassing data, API and application integration, data engineering, data quality, observability and governance and data products.
Eighty-six percent of data leaders prioritize factors such as avoiding vendor lock-in with a flexible enterprise data management platform, improving data management and governance and improving efficiency and operational costs when selecting their data architecture.
When designing your modern data architecture, consider these five fundamental principles that address current concerns while also preparing you for the future.
1. Flexibility in Data Architecture Design
To what extent does your data architecture need to adapt to and address emerging data formats and latencies, use-cases and technologies, including new AI use-cases in a dynamic landscape?
2. Scalability for Growing Data Volumes
How responsive does your data architecture have to be to adjust to changing data storage and processing volumes as you grow? Would that involve significant additions to operational or infrastructure costs?
3. Data Governance and Quality Management
Data quality will directly impact analytics and AI outcomes. You should consider solutions that offer built-in end-to-end data quality, governance and security capabilities to ensure your data remains protected and compliant at all points and business users can use it with confidence for quick decision making.
4. Continuous Optimization and Automation
A modern, simplified data architecture helps reduce costs and boosts productivity and efficiency through AI-powered workflow automation and process orchestration. Consider the benefits of a unified data management approach versus multiple point solutions — lower upfront and ongoing costs, flexible payment options and less admin and vendor management.
5. Interoperability and Data Access
Know your organization's data access and democratization needs. Centralized data management combined with federated delivery enables secure, self-serve access for authorized business users without compromising on security and compliance.
The 3 Approaches to Modern Data Architecture
Data Mesh
Data mesh is a solution architecture that helps teams build business-focused data products that meet their specific needs. Business users can define their data product requirements upfront and create fit-for-purpose domain-oriented self-service data platforms. This decentralized approach reduces complexity and prevents data bottlenecks, accelerates data delivery and improves data relevance by putting users closest to the data they use. With AI data mesh, organizations can add intelligent capabilities to enhance data product discovery, quality monitoring, and automated governance.
Data Fabric
Data fabric delivers unified, intelligent end-to-end data management across distributed data environments, easily managing large volumes of structured, unstructured or semi-structured data to handle new and emerging use cases. AI data fabric and metadata help automate ingestion, transformation, orchestration, AI governance, security, preparation, quality and curation processes, removing data storage complexities and streamlining delivery for faster insights and analytics.
Data Lakehouse
Data lakehouses combine the scalability of data lakes with the structured querying and transaction support of data warehouses. They have gained popularity because of the flexibility, scalability and simplified governance they deliver in a complex data environment. Because they reduce the need for separate systems for data lakes and data warehouses, they can also be more cost-effective.
Which Approach to Modernizing Data Architecture Is Right for You?
Each approach has unique strengths and suits specific business needs, as summarized in Figure 2 below. The choice of architecture usually depends on your business priorities, availability of technical expertise and current position on the data maturity curve.
Data mesh is ideal for large, complex organizations with decentralized teams and diverse business departments. Each function may want autonomy to manage and govern their specific data needs and the ability to scale without the bottlenecks of centralization. For instance, large multinational companies or e-commerce firms where different business units or domains generate and consume data independently.
While this approach is great for domain experts who know their data, it can be challenging for non-experts. Cross-domain collaboration can be tricky without clear standards, so this federated structure needs additional focus on data governance to minimize inconsistencies across domains.
Data fabric is ideal for organizations where multi-format data is distributed across systems, which typically occurs with regulated industries with stringent data governance and data format norms, such as healthcare and financial management. If your organization has a growing number of data consumers who need on-demand access to relevant data, data fabric adds a unified layer for seamless data integration and access and helps deliver the right data to the right user despite the data being distributed.
While this approach provides end-to-end visibility, it can require significant investment in tools and expertise to ensure comprehensive outcomes.
Data lakehouse is a good option for companies with analytics-driven workloads, seeking a balance between scalability and performance, especially if your teams need a single system to access the diverse data they need for advanced analytics and AI workloads. For instance, technology companies and D2C eCommerce brands need real-time insights for predictive modeling and advanced analytics on a daily basis.
Data lakehouses are a promising new technology, especially for the AI-driven analytics era, when designed with expertise to maintain data quality and avoid becoming a "data swamp."
| Data mesh | Data fabric | Data lakehouse | |
|---|---|---|---|
| Data ownership | Decentralized — domain teams own their data | Centralized — creates a unified layer to manage data across distributed environments | Centralized — brings data for analytics and AI use-cases to a single platform |
| Strength | Domain-driven data products | Seamless integration of fragmented data | Unified storage and real-time analytics |
| User advantage | Simplifies data ownership | Simplifies data sharing and access | Simplifies advanced analytics with a single source of truth |
| Ideal user | Large, complex organization with technically advanced functional teams | Organizations with multi-cloud or hybrid data environments | Organizations with analytics-heavy workloads seeking cost-effective storage solutions |
Modern Data Architecture Design’s Hybrid Approach
Organizations require a modern, cost-effective and future-proof data architecture — but they don’t need to restrict themselves to a single approach. With the advent of the AI era, access to AI-ready data is key. Bespoke solutions may not be primed to manage data in a way that makes AI initiatives successful, or may prove limiting in scale, speed or performance.
A hybrid modern data architecture, illustrated in Figure 3, integrates elements of data mesh, data fabric and data lakehouse and can be ideal for enterprises seeking a balance of scalability, flexibility and innovation. If your organization is experiencing significant growth and handling increasing volumes of diverse data from multiple sources, a hybrid approach ensures scalable data management that adapts as your needs evolve.
For instance, when fostering collaboration between IT teams, data scientists and business users is a priority, a hybrid architecture combines the centralized scalability of data lakehouse, the democratized access of data mesh and the seamless integration of data fabric.
Figure 3. Key components of a modern data architecture
Real-World Examples of Modern Data Architecture
Paycor
HR software leader Paycor faced a challenge familiar to many fast-growing technology companies: data fragmentation. Each department had its own idea of what data should be and how to define and label it — there was no data warehouse or integration platform to bring it all together and make it easy to understand metrics across the organization.
They decided to embrace the power of a comprehensive data platform to cleanse, organize and vet data before making it available to business users through a series of data marts and APIs.
They built a multi-tenant cloud data warehouse in partnership with Informatica and Snowflake and tapped Informatica Cloud Data Integration for fast and accurate data pipeline creation.
As a result, they have seen a 512% ROI on cloud data warehouse, a 90% reduction in AI data prep time and saved approximately 36.5 work hours annually.
Banco Brasil
Banco ABC Brasil, a wholesale bank with 30 years in the business, has a wealth of financial and customer information that it can use to make strategic and data-driven decisions, but until recently most of that information was difficult for its analysts and scientists to access. Extracting data from source systems was a manual process, so it was often outdated when it was available to analysts.
They leveraged Informatica Intelligent Data Management Cloud (IDMC) to power data in Google Cloud Storage and Google BigQuery. With 100% of their data lake catalogued and monitored for data quality, analysts and data scientists can quickly find the data they need to run their jobs using data cataloging, which provides metadata-driven data management and search capabilities.
Credit approvals have accelerated by 70% and 360-degree customer management has improved. With a consistent experience across all types of data, analysts can resolve credit decisions up to 30% faster. Financial and customer data is ingested 50% faster and predictive model design and maintenance times have reduced by 60 to 70%.
TelevisaUnivision
TelevisaUnivision, the world’s leading Spanish-language media and content company, needed a robust data ecosystem in the cloud that would cover governance, quality, master data management and a data marketplace for internal users and more.
With Informatica they built a single, comprehensive cloud-native data management platform that scales organically and serves as the focal point of their growing and maturing data ecosystem.
The organization is now more efficient, cutting its enterprise integration costs alone by 44% and has improved decision-making across virtually every line of business. Customer service efficiency has increased by 35% and they have achieved steady gains in scalability, cost and resource management.
Emerging Trends in Modern Data Architecture
The evolving data architecture landscape, especially in the context of AI, means that new concepts and trends are emerging that demand your attention. Key trends include:
Strengthening data governance
Data governance, observability and privacy-preserving techniques are becoming essential, especially as data regulations at industry, national and global levels evolve. The use of AI puts a renewed focus on ethical and reliable data. Emerging concepts such as synthetic data — artificially created datasets that mimic real-world information — will help organizations gain valuable insights and train AI models without exposing sensitive data or impacting privacy.
Unified data ecosystems
Seamless data integration supporting interoperability, automation and real-time processing remains a key competitive advantage. A unified approach, with end-to-end data management capabilities from ingestion and integration to governance, quality and analytics reduces complexity by eliminating the need for multiple tools and ensures seamless workflows to support advanced and emergent AI use cases.
Real-time data processing
Companies will need a data architecture that supports instant data processing, allowing them to react quickly to market changes. This calls for event driven architectures that work seamlessly with edge computing and advanced analytics use cases.
Future proofing
To future-proof their operations, fast-growing businesses prefer hybrid models and data platforms that offer vendor neutrality and the freedom to test and deploy new technologies easily, cost-effectively and without risk. Data platforms with a modular and scalable design ensure that organizations can adapt to emerging technologies and business needs without overhauling their data management infrastructure.
AI-powered data management
We will continue to see the emergence of new AI-powered capabilities to optimize speed and responsiveness while driving productivity and efficiency.
Building a Future-Proof Data Architecture for the AI Era
The evolution toward a hybrid modern data architecture — combining elements data mesh, data fabric and data lakehouse — is crucial for organizations aiming to stay ahead in today's data-driven landscape.
The AI era demands a data architecture that is scalable, flexible, responsive and vendor-neutral to support a wide range of emerging technologies.
A unified approach minimizes redundancies and points of failure, while giving you visibility and control over complex data management workflows. It also ensures consistent governance across all data sources and environments — vital in hybrid architectures where data is distributed across multiple systems.
Informatica's strength lies in its holistic approach to data management, which makes it a trusted choice for enterprises focused on AI-driven automation, cloud-native capabilities and comprehensive data management and governance solutions. Its ability to work with any architecture, combined with a simplified IPU-based pricing model, minimizes system and vendor complexity and helps future-proof your data strategy in an AI-first world.
Start modernizing your data architecture by choosing the right approach and technologies to meet current and future business needs. Explore Informatica Intelligent Data Management Cloud™ today.
