What is enterprise data infrastructure?

Data infrastructure isn't anything new. Companies across various industries have striven to create reliable data pipelines that run at the optimal cost.

It's one thing to run a couple of pipelines, however. It's another to process data at the scale, speed, and quality required of modern data-hungry applications.

This is especially true in our new Generative AI (GenAI) era. GenAI use cases require a large volume of highly accurate data from across the organization to succeed. A lack of such data is what will keep up to 30% of GenAI prototypes from making it into production.

Supplying the data needed for GenAI use cases requires scalability and a robust enterprise data infrastructure. In this article, we'll examine what an enterprise data infrastructure is, where companies struggle when it comes to implementing it, and how to build out an enterprise data infrastructure framework that minimizes the heavy lifting.

What is enterprise data infrastructure?

Data infrastructure is the set of systems and processes that businesses use for data management.

In the past, many businesses didn't approach data infrastructure in a uniform manner. Instead, they built one-off data pipelines that were hard to manage and maintain. Data infrastructure establishes a technological and business framework that makes data processing uniform, repeatable, and reliable.

Enterprise data infrastructure turns the volume up to 11, enabling a scalable framework for data ingestion, transformation, and data analytics that can feed a company's most data-hungry scenarios.

Enterprise data infrastructure also prioritizes data quality and compliance. It ensures that data is only accessible by authorized personnel and processed in accordance with all applicable regulatory requirements and industry standards, including regulations such as GDPR and HIPAA.

Common components of enterprise data infrastructure

Enterprise data infrastructure consists traditionally of multiple tiers or components. The key ones include:

Data ingestion. Data can come in many types of data and formats: unstructured, structured, and semi-structured. It can arrive from many data sources. The ingestion layer uses systems such as Fivetran to import data from sources and stores it in a consistent format for consumption by other downstream applications. This layer often relies on Extract, Transform, and Load (ETL) or Extract, Load, and Transform (ELT) pipelines to standardize and load data into downstream systems.

Data storage. Cloud computing brought us scalable data warehouse systems like Snowflake and Amazon Redshift that can easily store terabytes of structured data on demand. Other cloud-based data storage architectures, such as data lakes and data lakehouses, enable storing datasets in unstructured, semi-structured, or hybrid formats using low-cost cloud storage solutions.

Data transformation and modeling. A data transformation layer takes data in its raw state and makes it available to derive insights. Data is generally stored in its raw format and then transformed multiple times to serve specific use cases. This layer typically involves tools to model data as well as to test data models to ensure validity and data quality.

Data serving. The data serving layer makes data assets available for discovery and use in business intelligence and analytics, where they take their final form and can be used to drive valuable business insights and data-driven decision-making. This layer consists of tools such as data catalogs, visual dashboards, and automated report generation services to deliver a regular stream of insights in a timely manner.

Data governance. Data governance is a set of pillars that include data quality, data stewardship, data security, and data management. Rather than being a specific tool, it's a set of processes built into the fabric of the system, supporting access controls and compliance at every stage of the analytics development lifecycle.

How enterprise data infrastructure can hobble or help your GenAI projects

As the AI age accelerates, enterprise data infrastructure is more important than ever. Gone are the days when companies could "just get by" with hard-coded, brittle data pipelines that require constant manual intervention. Successful GenAI projects rest upon a data infrastructure that supports automation, is robust, and can scale to manage terabytes and petabytes of data.

What's stopping companies from getting there? Recently, Fivetran created a global benchmark of enterprise data infrastructure spend to find out where the gaps are between AI expectations and reality.

The report found three glaring issues:

Operational efficiency is killing AI data scalability. Data teams dedicate 53% of their time to maintenance. The costs involved in data pipeline downtime and repair may cost companies more than $36 million a year.

ROI on data integration is low despite massive spend. This manual, high-intervention approach to data pipelines is killing ROI. On average, enterprises allocate 14% of their data budgets to integration. However, only 27% say that ROI exceeded their expectations.

Pipeline modernization delivers higher ROI. The good news is that these problems are avoidable. Fivetran found that 47% of organizations running fully managed and standardized data pipelines exceeded their ROI expectations. The decrease in maintenance work enabled their data teams to address more pressing issues in analytics, AI, and governance, thereby increasing data quality and velocity.

Why enterprise data infrastructure remains a hard problem

The thing is, companies don't end up where they are by accident. There's a reason why organizations end up with a hodgepodge of data pipelines written in different languages and running on their own infrastructure, whether cloud-based or on-premises:

Data is fragmented. Without a centralized data catalog to track what's available, a lot of data ends up in silos. These data silos use their own formatting and storage conventions, making them difficult to integrate with other corporate data systems. They often go ungoverned and unmonitored, creating a data security risk for the company, including potential data breaches involving sensitive data.

Data exists in heterogeneous systems. Data storage systems have evolved over the past several decades. New architectures, such as data lakehouses, and new storage formats, such as Apache Iceberg, have brought needed improvements in quality, performance, and governance.

This evolution means data is naturally scattered across disparate, heterogeneous data warehouses, data lakes, and other systems. Often, it's not economically feasible or technically advisable to move that data into newer systems. The time, cost, and risk entailed are too high.

Building an enterprise data infrastructure takes time and money. With data engineers spending half of their waking hours fighting fires, there's little time left in a month to tackle the bigger issues. Many companies remain stuck in their current data infrastructure because building a replacement from the ground up requires time and computing resources they don't have.

Creating a single data control plane for analytics and AI

Companies looking to support GenAI don't need to centralize all of their data. What they need is an enterprise data infrastructure that supports delivering high-quality data at scale, no matter where it lives. They need adaptability and workflows that can respond to rapidly changing business needs.

What they need, in other words, is a data control plane.

A data control plane is an architectural layer that sits over all of your end-to-end data activities. It enables data integration, access controls, data governance, and protection so you can manage the behavior of people and processes in a distributed and dynamic data environment.

At dbt Labs, we've long worked to enable this vision of a vendor-agnostic, flexible, and trustworthy data control plane. dbt enables working with your data where it lives, transforming raw inputs into the high-quality data needed for both analytics and AI use cases. This approach gives stakeholders and data teams a real-time view of data health and streamlines the path from raw source to trusted insight.

With dbt, you're not starting from zero. dbt provides a rich framework out of the box for enabling a consistent approach to modeling, testing, and publishing data. That means you can focus less on your enterprise data infrastructure and more on your use cases.

dbt provides all of the essential tools you need to curate data for analytics and AI:

Authoring and testing data transformations locally. Using the dbt Fusion engine, data producers can model and test data transformation code locally, using standard tools like Visual Studio Code. Fusion implements a SQL compiler that automatically validates code as engineers write, enabling data producers to work faster and deploy changes more quickly than ever.

Version, test, and monitor continuously. All data analytics and AI code is version-controlled and reviewed before going live. Data developers can easily craft data tests to validate their workloads locally. Tests can also be run as part of a Continuous Integration (CI) pipeline to verify any changes before deploying to production. This optimizes the development lifecycle and reduces the risk of errors reaching production environments.

Document data for users and AI. dbt's built-in support for rich documentation means users can understand where data comes from and what it means. Using the dbt Model Context Protocol (MCP) server, you can also feed your docs to your GenAI systems as invaluable context for understanding your data.

Define global metrics for GenAI apps. Metrics definitions often differ from group to group, causing confusion when the data is fed to GenAI systems. The dbt Semantic Layer provides a single location for defining and accessing key business metrics. You can supply these "gold" metrics to LLMs and GenAI apps to eliminate confusion between competing definitions and improve machine learning model performance.

Drive governance across systems. dbt simplifies data governance as well. By standardizing on dbt as a single data control plane, you give everyone in the company a single source of truth for data and metrics. dbt Catalog provides a one-stop shop for global data discovery, with data access enforced via role-based access controls (RBAC).

Delivering data for AI requires a solid enterprise data infrastructure that regulates access via a centralized control plane. dbt enables building that foundation on top of your existing, heterogeneous data architecture and gives your organization a competitive advantage in a data-driven world. Contact us today to learn more about how dbt can modernize your approach to data pipelines and set you up for GenAI success.