What Is Data Infrastructure? Components, Types, and How to Build It

Data infrastructure forms the foundation for every analytics initiative and AI deployment. Yet many organizations struggle to build systems that actually deliver usable insights. This guide breaks down the five core components of data infrastructure, compares legacy and modern approaches, and walks through practical steps for building systems that support informed decisions and AI-ready operations.

Key takeaways

Here are the main points to keep in mind.

• Data infrastructure is the foundation of systems, tools, and technologies that collect, store, process, and manage data across an organization
• The five core components include data storage, processing frameworks, integration tools, governance systems, and monitoring solutions
• Modern data infrastructure differs from legacy systems by supporting real-time processing, cloud scalability, and AI readiness
• Building effective data infrastructure requires aligning technical architecture with business objectives and governance requirements
• Strong data infrastructure enables quicker decision-making, higher data quality, and the foundation for AI and automation initiatives

What is data infrastructure?

Your data infrastructure is the systems, tools, and technologies you have in place to collect, store, process, manage, and use data effectively. The infrastructure includes all the physical components (servers, data centers, storage systems, and network hardware) together with the software layer, which consists of your databases, data warehouses, data lakes, integration tools, analytics platforms, and data governance frameworks. This combined infrastructure supports the full data lifecycle, from ingestion and transformation to analysis and visualization.

Companies use their data infrastructure to collect, manage, and use data to drive informed decisions, improve operations, and fuel innovation. An organization's data infrastructure is the basis for everything from real-time dashboards and predictive analytics to automation and machine learning. It allows your people to pull in data from multiple sources, like customer relationship management (CRM) systems, cloud apps, Internet of Things (IoT) devices, and internal databases, and store it in centralized systems, where it can be organized, cleaned, and made ready for analysis.

What data infrastructure enables

With your data infrastructure in place, you can do things like:

• Track customer behavior to personalize marketing and improve the customer experience
• Monitor equipment performance for predictive maintenance in manufacturing
• Analyze sales trends to optimize inventory and forecasting
• Detect fraud in financial transactions using machine learning models
• Streamline operations by automating reporting, alerts, and workflows
• Support executive decision-making with real-time, visual dashboards

Raw information becomes actionable insight. Quickly. More reliably. At scale.

Types of data infrastructure

Not all data infrastructure looks the same. The approach you choose depends on your organization's size, regulatory requirements, technical capabilities, and growth trajectory. Here are the three primary types:

On-premises data infrastructure

On-premises infrastructure means owning and operating your own servers, storage systems, and networking hardware within your physical facilities. This approach gives you complete control over your data environment and can be essential for organizations with strict data residency or compliance requirements. Higher upfront capital costs come with the territory, along with ongoing maintenance responsibilities and limited ability to scale quickly when demand spikes. And here's something that surprises people: on-premises does not automatically mean more secure. Without proper configuration and ongoing security investment, on-premises systems can be just as vulnerable as poorly managed cloud environments.

Cloud-based data infrastructure

Cloud-based infrastructure shifts your data systems to providers like AWS, Google Cloud, or Microsoft Azure. You gain elastic scalability, pay-as-you-go pricing, and access to managed services that reduce operational burden. Deployment happens more quickly. Your teams can spin up new environments without waiting for hardware procurement. For organizations prioritizing speed and flexibility, cloud infrastructure often makes the most sense.

Hybrid and multi-cloud approaches

Many organizations land somewhere in between, combining on-premises systems with cloud resources. Hybrid approaches let you keep sensitive data on-site while using cloud scalability for analytics workloads. Multi-cloud strategies spread infrastructure across multiple cloud providers, reducing vendor lock-in and supporting data residency requirements across different regions. These approaches offer flexibility but require careful orchestration to maintain consistency and governance across environments.

4 key components of data infrastructure

Regardless of which type you choose, effective data infrastructure relies on four core components working together:

Data storage and compute

Storage is where your data lives. This includes data warehouses for structured analytics workloads, data lakes for raw and unstructured data, and increasingly, lakehouses that combine the best of both. Cloud storage solutions provide scalability, while compute resources handle the processing power needed to query and transform that data. The right storage architecture depends on your data volume, query patterns, and performance requirements.

Data processing and integration

Data rarely stays in one place. Processing and integration tools move data between systems, transform it into usable formats, and ensure it arrives where analysts and applications need it. This includes ETL (extract, transform, load) and extract, load, transform (ELT) pipelines, batch processing for large historical datasets, and real-time streaming for time-sensitive data. Data migration platforms help organizations consolidate sources and modernize their data movement capabilities.

Data governance and security

Governance ensures your data is trustworthy, compliant, and accessible to the right people. This component includes access controls that limit who can see and modify data, compliance frameworks that meet regulatory requirements, data quality rules that catch errors before they spread, and lineage tracking that shows where data came from and how it changed. Without strong governance, even the best infrastructure produces unreliable results. Too many teams treat governance as a one-time setup rather than an ongoing operational practice. Policies drift. Access controls become stale. Data quality degrades unless you actively maintain them.

Monitoring and observability

You can't manage what you can't see. Monitoring tools track pipeline health, data freshness, and system performance. Data observability goes deeper, helping teams understand why something failed, not just that it failed. Alerting systems notify the right people when data quality degrades or pipelines break.

Data infrastructure vs data architecture

These terms often get used interchangeably, but they serve different purposes.

Data architecture is the blueprint. It defines how data flows through your organization, what systems connect to what, where data gets stored, and how different components interact. Think of it as the design document that guides decisions about technology, standards, and organizational structure.

Data infrastructure is the implementation. The actual servers, databases, pipelines, and tools that bring the architecture to life. If architecture is the plan for a building, infrastructure is the steel, concrete, and wiring that makes it real.

Why does this distinction matter? You can have a brilliant architecture on paper that fails in execution. Or solid infrastructure that doesn't align with business needs because no one thought through the design. Effective data programs need both.

How data flows through your infrastructure

Understanding data infrastructure becomes easier when you see how data actually moves through it. Think of it as a circulatory system.

Here's the end-to-end flow in six steps:

1. Source: Data originates from operational systems, applications, IoT devices, third-party APIs, and customer interactions
2. Ingest: Collection mechanisms pull data into your infrastructure through batch uploads, streaming pipelines, or event-driven triggers
3. Store: Data lands in appropriate storage systems (data lakes for raw data, warehouses for structured analytics, or lakehouses for both)
4. Transform: Processing frameworks clean, validate, enrich, and reshape data into formats ready for analysis
5. Serve: Transformed data becomes available to analysts, applications, dashboards, and AI models through query engines and APIs
6. Govern: Throughout every stage, governance controls enforce access policies, track lineage, and maintain data quality

This flow matters for AI because models require fresh, high-quality data with clear provenance. When your infrastructure supports this end-to-end flow with proper governance at each stage, you can trace any AI output back to its source data.

Concrete examples of data infrastructure

Definitions only go so far. Here's what data infrastructure actually looks like in practice across three common scenarios:

E-commerce analytics stack

A mid-size retailer needs to understand customer behavior across web, mobile, and in-store channels.

The data flow works like this:

1. Sources: Point-of-sale systems, website clickstream, mobile app events, inventory management, CRM
2. Ingestion: Event streaming captures web and app activity in real time; batch jobs pull daily sales and inventory data
3. Storage: Raw data lands in a cloud data lake; transformed data moves to a warehouse for analytics
4. Transformation: Daily jobs clean transaction data, calculate customer lifetime value, and build product affinity models
5. Serving: Dashboards show merchandising teams which products to promote; recommendation engines personalize the shopping experience
6. Governance: Personally identifiable information (PII) fields are masked before analyst access; data lineage tracks how metrics are calculated

At each step, the retailer makes decisions: batch versus streaming for different data types, which transformations run daily versus hourly, who can access customer-level data versus aggregated reports.

Software as a service (SaaS) product telemetry

A software company wants to understand how customers use their product and run experiments to improve engagement.

The data flow works like this:

1. Sources: Application event logs, customer authentication, subscription billing, support tickets
2. Ingestion: Real-time streaming captures every customer action; change data capture (CDC) syncs database changes
3. Storage: Event data streams into a lakehouse; aggregated metrics land in a warehouse
4. Transformation: Sessionization logic groups events into customer journeys; experiment frameworks calculate statistical significance
5. Serving: Product managers see feature adoption dashboards; data scientists access raw events for cohort analysis
6. Governance: Customer consent flags determine which data can be used for experimentation; retention policies auto-delete data after 24 months

The key decision here is latency. Product teams need near-real-time visibility into feature releases, while long-term trend analysis can run on daily batch jobs.

Manufacturing IoT streaming

A manufacturer monitors equipment across multiple facilities to predict failures before they cause downtime.

The data flow works like this:

1. Sources: Sensors on production equipment (temperature, vibration, pressure), maintenance logs, production schedules
2. Ingestion: Edge gateways aggregate sensor data locally; streaming pipelines push to the cloud every few seconds
3. Storage: Time-series databases store sensor readings; a data lake holds historical patterns for model training
4. Transformation: Anomaly detection algorithms flag unusual readings; ML models predict remaining equipment life
5. Serving: Maintenance teams receive alerts when equipment needs attention; operations dashboards show plant-wide health
6. Governance: Sensor data is tagged by facility and equipment type; access controls limit who can see production metrics

Where processing happens is the critical decision: edge computing handles immediate anomaly detection, while cloud infrastructure runs the heavier ML workloads.

Data movement patterns and pipeline taxonomy

How you move data through your infrastructure depends on your latency requirements, transformation complexity, and operational constraints. Here are the primary patterns:

• Batch processing: Data moves in scheduled intervals (hourly, daily, weekly). Best for large historical datasets where real-time isn't required. Lower cost, simpler to manage, but introduces latency.
• Stream processing: Data moves continuously as events occur. Essential for time-sensitive use cases like fraud detection or live dashboards. Higher complexity and cost, but enables real-time insights.
• ETL (extract, transform, load): Data is transformed before loading into the target system. Traditional approach that works well when you need to enforce data quality and structure upfront.
• ELT (extract, load, transform): Data is loaded raw, then transformed in the target system. Modern approach that takes advantage of cloud warehouse compute power and preserves raw data for flexibility.
• CDC (change data capture): Only changed records are captured and moved, rather than full table reloads. Efficient for keeping systems in sync without moving unnecessary data.
• Event-driven: Data movement is triggered by specific events rather than schedules. Enables responsive architectures where downstream systems react immediately to upstream changes.

When choosing a pattern, consider these tradeoffs:

Most organizations use a combination. A retailer might stream point-of-sale transactions for real-time inventory visibility while running nightly batch jobs for financial reporting. And this is where teams get into trouble: choosing a pattern based on what's trendy rather than what the business actually needs. Streaming everything sounds impressive until you're paying for infrastructure you don't need and managing complexity that slows your team down.

Legacy vs modern data infrastructure

The gap between legacy and modern data infrastructure is not just about age. It's about capability.

Organizations running legacy infrastructure often find themselves unable to support AI initiatives, struggling with data silos, and spending more on maintenance than innovation. Modernization does not always mean ripping everything out.

Why data infrastructure matters for analytics and AI

Data infrastructure and analytics are deeply connected. One enables the other.

A strong data infrastructure provides an essential foundation for analytics. It ensures data is collected, stored, organized, and made accessible in a way that analysts and business people can trust and use effectively.

Without reliable infrastructure in place, analytics efforts often struggle with data silos, poor quality, inconsistent formats, and delays in access. All of which can lead to inaccurate insights or missed opportunities.

Analytics is what gives data infrastructure its strategic value. The insights derived from well-structured data help drive stronger business decisions, optimize operations, and uncover trends and patterns that might otherwise go unnoticed. Whether it's through dashboards, reports, predictive models, or real-time alerts, analytics turns raw data into action. The tighter the integration between infrastructure and analytics tools, the more easily organizations can harness their data to create meaningful, measurable outcomes.

Quicker, more confident decision-making

When data flows smoothly from collection to analysis, teams can access the insights they need in real time. Integrated systems eliminate the delays and confusion caused by siloed or inconsistent data.

Higher data quality and consistency

Strong data infrastructure enforces data governance, validation, and standardization across systems. Cleaner, more trustworthy data. More reliable analytics. Stronger forecasts. Stronger strategic alignment across departments.

Foundation for AI and automation

Modern data infrastructure does more than support traditional analytics. It provides the foundation for AI and automation initiatives. AI models are only as good as the data they're trained on, which means your infrastructure needs to deliver governed, high-quality data at scale.

This goes beyond just storing data. Your infrastructure needs to support AI pipelines, model training environments, and the ability to operationalize AI outputs back into business workflows.

What makes infrastructure "AI-ready"? Machine-consumable data with enforced controls. Your data needs to be fresh enough for models to trust, with lineage tracking that traces outputs back to sources. Access controls must work at inference time, not just at rest. And governance policies (like preventing PII from entering training datasets) need to be automated, not manual checklists.

Think of it in three layers: Foundation makes your data AI-ready through proper governance and quality controls. Activation turns AI into action through agents and applications running on that governed data. Distribution delivers outcomes into the workflows people already use.

AI-ready data infrastructure

As organizations move beyond traditional analytics into AI and machine learning, infrastructure requirements expand significantly. Here's what AI-ready infrastructure looks like:

• Training and serving separation: Training environments need access to large historical datasets and significant compute. Serving environments need low-latency access to features and models. These workloads have different requirements and often run on different infrastructure.
• Feature and embedding pipelines: Machine learning (ML) models consume features, not raw data. Feature stores manage the transformation of raw data into model-ready features, ensuring consistency between training and serving. For generative AI (GenAI) applications, embedding pipelines convert documents and data into vector representations.
• Vector database integration: Semantic search and retrieval-augmented generation (RAG) applications require vector stores that can efficiently search across millions of embeddings. These become part of your infrastructure when you're building AI assistants or knowledge retrieval systems.
• Prompt and response logging: GenAI applications need observability too. Logging prompts, responses, and context enables evaluation, debugging, and continuous improvement of AI systems.
• Privacy controls for model inputs: Governance extends to what data can be used for training and inference. Automated controls prevent PII from entering training datasets and enforce consent requirements at inference time.
• Evaluation datasets: Measuring AI performance requires curated datasets with known-good answers. Infrastructure should support versioning and management of these evaluation sets alongside production data.

A SaaS company building a customer support AI assistant might use a feature store to serve real-time customer context, a vector database to retrieve relevant documentation, prompt logging to track conversation quality, and automated PII detection to prevent sensitive data from reaching the model.

Governance as operational practice

Governance is not a checkbox. It's an ongoing operational discipline that runs throughout your data infrastructure.

Roles and responsibilities

These roles define who owns key governance work.

• Data owners: Business leaders accountable for data quality and appropriate use within their domain
• Data stewards: Subject matter experts who define business rules, resolve quality issues, and maintain documentation
• Data custodians: Technical teams responsible for implementing access controls, retention policies, and security measures

Governance artifacts

These core artifacts help teams document and manage governance work.

• Data catalog: Searchable inventory of all data assets with descriptions, owners, and usage guidance
• Business glossary: Standardized definitions for key terms and metrics to ensure everyone speaks the same language
• Data lineage: Visual mapping of how data flows from source to consumption, enabling impact analysis and troubleshooting

Governance controls

These controls keep policy enforcement practical and consistent.

• Access management: Role-based permissions that limit data access to authorized people and use cases
• Retention policies: Automated rules that archive or delete data based on regulatory requirements and business needs
• Quality rules: Automated checks that validate data at ingestion and transformation, catching errors before they propagate

Measurable service level agreements (SLAs)

Governance without measurement is just policy. Define and track SLAs like:

• Data freshness: Analytics datasets updated within 24 hours of source changes
• Quality scores: 99 percent of records pass validation rules before serving to downstream systems
• Access request turnaround: New data access requests resolved within 48 hours

The governance workflow follows three steps: define policies and standards, enforce them through automated controls, and monitor compliance through dashboards and alerts.

Reliability and operability

Building data infrastructure is one thing. Running it reliably? That's another matter entirely.

Service level objectives (SLOs)

Define measurable targets for your infrastructure:

• Pipeline uptime: 99.9 percent availability for critical data pipelines
• Data freshness: Real-time datasets updated within five minutes; daily datasets available by 6 am
• Query performance: 95 percent of dashboard queries complete in under three seconds

Monitoring and alerting

Track the health of your infrastructure continuously:

• Pipeline monitoring: Track job success rates, run times, and data volumes
• Data quality monitoring: Detect anomalies in row counts, null rates, and value distributions
• Resource monitoring: Watch compute utilization, storage growth, and cost trends

Configure alerts that notify the right people when thresholds are breached. Before people notice problems.

Incident response

When things break (and they will), have a process ready:

1. Detect: Automated monitoring catches the issue and triggers an alert
2. Triage: On-call engineer assesses severity and impact on downstream systems
3. Remediate: Fix the immediate problem and restore service
4. Postmortem: Document what happened, why, and how to prevent recurrence

Disaster recovery

Plan for worst-case scenarios:

• Backup strategy: Regular backups of critical data with tested restore procedures
• Recovery objectives: Define recovery point objective (RPO) and recovery time objective (RTO)
• Failover testing: Regularly test your ability to recover from failures before you need to do it for real

A data engineer might use lineage alerts to detect schema drift in a Snowflake table, then roll back the change before downstream dashboards break. That's operational excellence in action.

Security beyond the basics

Data infrastructure security goes beyond firewalls and encryption. Here's how to think about data-layer security across the lifecycle:

Regulatory mapping

Different regulations impose different requirements:

• General Data Protection Regulation (GDPR): Purpose limitation and consent management at ingestion; right to deletion across all systems
• Health Insurance Portability and Accountability Act (HIPAA): Encryption requirements for protected health information (PHI); audit trails for all access
• Service Organization Control 2 (SOC 2): Access controls, change management, and incident response documentation

Privacy by design

Build privacy into your infrastructure from the start:

• Data classification: Tag data by sensitivity level at ingestion
• Minimization: Collect only what you need; delete what you don't
• Anonymization: Remove or mask identifiers when full fidelity is not required

Security is not a layer you add on top.

Cost model and capacity planning

Data infrastructure costs can spiral quickly without careful planning.

Cost anatomy

These cost areas shape how spending grows over time.

• Storage tiers: Hot storage for frequently accessed data costs more than warm or cold storage for archives. Move data to appropriate tiers based on access patterns.
• Compute scaling: Auto-scaling adjusts capacity to demand, but watch for runaway costs from poorly optimized queries or unexpected data volumes.
• Egress charges: Moving data out of cloud providers incurs costs. Architect to minimize cross-region and cross-cloud data movement.
• Workload isolation: Separate environments for development, testing, and production prevent expensive production resources from being consumed by experimental workloads.

Cost comparison by architecture

Common pitfalls

These pitfalls show where costs often drift off course.

• Over-provisioning: Allocating more compute than needed "just in case." Solution: Start small, monitor usage, and scale based on actual demand.
• Zombie resources: Forgotten development environments and unused datasets accumulating costs. Solution: Implement tagging and automated cleanup policies.
• Unoptimized queries: A single poorly written query can consume more resources than all other workloads combined. Solution: Query monitoring and optimization reviews.

FinOps for data means treating infrastructure costs as a continuous optimization problem.

Common challenges in data infrastructure

While data infrastructure and analytics are essential for modern business success, building and maintaining them is not always easy. Many organizations struggle to manage the volume and variety of data flowing through their systems and face technical, organizational, and cultural hurdles along the way. Without a solid foundation and the right strategy, even the best analytics tools can fall short.

Data silos and fragmentation

In many organizations, data is spread across multiple systems and departments, ranging from cloud apps and legacy databases to spreadsheets and third-party platforms. This fragmentation makes it difficult to create a single source of truth and can lead to inconsistent or conflicting insights. Breaking down silos and creating unified, accessible data pipelines is a critical but often complex task.

Start with establishing a centralized data architecture (whether a warehouse, lake, or lakehouse) that serves as the authoritative source. Modern integration tools can connect disparate systems without requiring you to replace them entirely.

Data quality and governance gaps

Dirty, incomplete, or poorly defined data can undermine even the most sophisticated analytics efforts. Without strong data governance, organizations risk basing decisions on flawed inputs. Ensuring accuracy, consistency, and accountability across the data lifecycle requires ongoing investment and cross-functional collaboration.

Address this by implementing automated data quality checks at ingestion points, establishing clear ownership for data domains, and creating governance policies that balance access with control.

Scalability and performance limitations

As data volumes grow and analytics become more complex, many legacy systems and poorly integrated tools struggle to keep up. Performance bottlenecks, storage limitations, and inefficient pipelines can slow down access to insights and limit the organization's ability to scale.

Cloud-native and hybrid architectures offer the flexibility to scale compute and storage independently, paying for capacity when you need it rather than provisioning for peak demand.

Best practices for building data infrastructure

A strong data infrastructure is the backbone of effective analytics. It enables organizations to collect, manage, and access data efficiently while supporting scalability, governance, and innovation.

Design for scalability and flexibility

Your data infrastructure should be built to grow with your business. Choose scalable storage and compute solutions (such as cloud-native platforms) that can handle increasing data volumes, evolving workloads, and new data sources. Flexibility is equally important.

Establish strong data governance

Without clear policies for data access, quality, and compliance, your infrastructure can quickly become unreliable and chaotic. Implement governance frameworks that include standardized metadata, data lineage tracking, role-based access control, and regular audits.

Enable self-service analytics

Modern data infrastructure should empower people. Not bottleneck them. By implementing self-service analytics tools, you allow teams across the organization to explore data, generate insights, and make decisions without relying on IT. This drives quicker decision-making, increases agility, and fosters a culture of data literacy. People-friendly dashboards with tailored views help non-technical people get value from data without specialized training.

Integrate AI and automation

To stay competitive, your infrastructure should support artificial intelligence and machine learning capabilities. Build in support for AI pipelines, model training, and real-time inference and use automation to streamline processes.

The key is building AI capabilities with governance at the center. Human oversight should be a core feature, not an afterthought. AI agents work best when humans set objectives and constraints while machines execute and coordinate within those boundaries. This bounded autonomy approach ensures you get the speed benefits of automation without losing control over critical business processes.

For organizations moving toward agentic AI and retrieval-augmented generation (RAG), infrastructure requirements expand further. You'll need embedding pipelines to convert documents and data into vector representations, vector stores to enable semantic search, and governance controls over what content gets indexed and retrieved. Freshness SLAs become critical.

Unify and centralize data sources

Avoid the pitfalls of siloed data by creating a centralized architecture that brings together structured, semi-structured, and unstructured data from across your business. Use modern data integration tools and warehouses to provide a single source of truth and make sure the infrastructure supports real-time or near-real-time data availability.

Data infrastructure implementation checklist

Building a strong, scalable data infrastructure requires a thoughtful approach and the right combination of technology, governance, and empowerment for your people. Whether you're implementing a new infrastructure or optimizing an old one, this checklist is designed to help your team translate best practices into actionable steps that support long-term success in analytics.

Scalability and flexibility

Use this checklist to make sure the architecture can grow with demand.

• Choose cloud-native or hybrid architecture to support dynamic scaling
• Plan for horizontal and vertical scaling of storage and compute
• Use modular, API-driven tools that can adapt as business needs evolve

Data governance

Use these items to confirm governance is defined and enforceable.

• Define data ownership and stewardship roles
• Implement role-based access control (RBAC)
• Standardize metadata and enforce naming conventions
• Set up automated data quality checks and validation rules
• Document data lineage for traceability and compliance

Self-service analytics

These checklist items help teams explore data without losing control.

• Deploy people-friendly analytics platforms with tailored dashboards
• Provide training and documentation for non-technical people
• Establish guardrails to ensure secure and governed access to data
• Enable visual data exploration and drag-and-drop reporting tools

AI and automation readiness

These items help confirm the environment can support governed AI work.

• Integrate machine learning and AI tools into your data pipeline
• Automate data ingestion, transformation, and orchestration
• Support high-performance compute environments for AI workloads
• Include data versioning and model lifecycle tracking

Unified data architecture

Use this checklist to confirm your core systems connect cleanly.

• Consolidate data sources using ETL/ELT or real-time streaming solutions
• Establish a centralized data lake or warehouse as a single source of truth
• Ensure compatibility across structured and unstructured data types
• Set up a data catalog for easier discovery and reuse of data assets

Data infrastructure examples by industry

A well-designed data analytics infrastructure reveals insights that power informed decisions across every industry. From real-time monitoring to long-term forecasting, organizations are using data to personalize experiences and respond more effectively to challenges.

Retail

Retailers use data infrastructure to ingest sales, inventory, and customer data from multiple channels. Analytics platforms help teams understand buying patterns, optimize pricing strategies, personalize promotions, and forecast demand to improve stock management and reduce waste.

Healthcare

Hospitals and health systems rely on integrated data infrastructure to bring together clinical records and patient monitoring data. With analytics tools, they can identify at-risk patients, reduce hospital readmissions, and improve outcomes through more targeted, data-driven care.

Finance

Financial institutions deploy real-time data pipelines to process transactional data and customer interactions. This enables teams to detect fraud, assess credit risk, analyze market conditions, and ensure compliance with evolving regulations through high-speed, accurate insights.

Manufacturing

Manufacturers use IoT sensors, data lakes, and analytics dashboards to monitor equipment performance and operational output. Analyzing trends and anomalies helps teams predict machine failures and reduce downtime while also optimizing production schedules for greater efficiency.

Marketing and sales teams

Marketing and sales professionals consolidate data gathered from across the CRM, web, and advertising platform into centralized warehouses. With modern analytics tools, they can track campaign ROI, segment audiences more precisely, score leads, and tailor outreach strategies that drive higher conversions.

Supply chain and logistics

Logistics companies gather real-time data from GPS, warehouse systems, and enterprise resource planning (ERP) platforms to track shipments and manage inventory. Analytics dashboards help teams optimize delivery routes, forecast demand, and reduce fulfillment costs while improving customer satisfaction.

Public sector

Government agencies use integrated data systems to consolidate records from various departments and services. With analytics, they can identify trends, allocate resources more efficiently, and improve public programs and citizen experiences through data-informed decision-making.

Energy and utilities

Energy companies use advanced data infrastructure to collect data from smart meters, grid sensors, and environmental monitoring systems. Analytics help optimize energy distribution, predict equipment failures, reduce outages, and support the transition to cleaner, more efficient energy sources.

Tools and technologies for data infrastructure

Building data infrastructure requires assembling the right combination of tools across several categories:

• Data storage: Data warehouses (Snowflake, BigQuery, Databricks), data lakes, and cloud storage solutions form the foundation for housing your data
• Data processing: Tools for batch processing, stream processing, and data transformation handle the work of preparing data for analysis
• Data integration: ETL/ELT platforms and data movement tools connect your sources and keep data flowing to where it needs to go
• Data governance: Cataloging tools, lineage tracking, and access management solutions ensure your data stays trustworthy and compliant
• Monitoring and observability: Pipeline monitoring, data quality tools, and alerting systems help you catch problems before they impact downstream people

The challenge is not finding tools. It's making them work together. Many organizations end up with a patchwork of point solutions that don't integrate well, creating new silos even as they try to eliminate old ones.

How Domo supports modern data infrastructure

Great analytics start with great infrastructure, and the right platform brings it all together. When your data flows freely, your insights hit harder and your teams move more quickly.

Domo approaches data infrastructure through three connected layers. Foundation capabilities make your data AI-ready through hundreds of connectors, transformation tools, and governance controls that work with your existing cloud data platform (whether that's Snowflake, BigQuery, Databricks, or others). Domo does not replace your data warehouse; it helps you get more value from it.

Activation turns that governed data into action through AI agents, applications, and analytics that run on your trusted data foundation. With support for your preferred AI models (OpenAI, Google, Anthropic, or your own) Domo makes AI actionable without requiring you to become an AI model provider yourself.

Distribution delivers outcomes into the workflows people already use. That means dashboards and reports, yes, but also AI assistants, embedded analytics, mobile apps, and automated workflows that put insights where decisions happen.

The platform is unified by design but modular by adoption. You can start with a single capability to solve a specific problem and expand as your needs grow.

Ready to see what your data can really do? Start a free trial or explore how Domo can transform your data infrastructure and analytics today.