Data Architecture for AI: The Five Indispensable Pillars You Must Implement

Updated November 6th, 2024

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Organizations are rapidly incorporating generative AI into their business strategy and operations. According to a McKinsey survey, the number of enterprise companies adopting AI rose from 33 to 65 percent between 2023 and 2024 — and is expected to reach 80% in 2025.

In the expanding AI environment, does your organization have the data architecture to support AI? This article will explain AI data architectures and their components, and show you how to get started in this exciting frontier.
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Table of contents #

  1. What is Data Architecture for AI?
  2. Why AI requires a solid data architecture
  3. Components of data architecture for AI
  4. How Atlan can help
  5. Data Architecture for AI: Related reads

What is Data Architecture for AI? #

Data architecture is an organization’s overarching system for governing data collection, storage, management, and use. An effective data architecture creates high-quality, secure, and well-governed data, providing value to stakeholders across the organization.

Generally speaking, there are two implementation patterns for AI systems:

  • Training your own AI engine from scratch
  • Using an existing Large Language Model (LLM) combined with data from your organization.

While building a custom system can produce powerful products, it’s more common for companies to base their AI initiatives on the foundation of an existing LLM — a more accessible approach for teams that may not have the time or resources for dedicated AI development.

No matter what their origin, however, AI systems need a modern data architecture that supports large volumes of high-quality data without compromising data security and compliance.

AI data architecture specifically supports gen AI use cases, including customer service, anomaly detection, personalization, information summary, and product recommendations.

Why AI requires a solid data architecture #

AI systems can’t function without a solid data architecture to support them. There are four main reasons why:

  • AI requires high-quality data at scale
  • AI requires multiple different types of data
  • Many use cases require real-time data streaming
  • AI has issues with data privacy

AI requires high-quality data at scale #


“Garbage in, garbage out” applies even more to AI systems, particularly since AI models are prone to hallucinations. If data quality is low, you can end up with AI chat agents giving incorrect or even fabricated information to users — even with a large volume of training data. Scale is important; feeding AI the largest possible data set is good. But data quality is more important than data quantity.

Architectures like a data mesh, in which teams own their data products, allow for data quality maintenance across an organization. When an organization puts proper data architecture in place, it will have a wealth of high-quality data to draw from for its AI tools.

AI requires different types of data #


Due to cost, most organizations aren’t training their own AI models from scratch. Instead, they are using Retrieval Augmented Generation (RAG) to convert their own data into vector representations to use with a LLM.

RAG enables semantic search of an organization’s data objects, documentation, customer support calls, and more. With such a variety of data inputs, an AI data architecture needs to support the widest possible number of data types. Beyond structured data (tables, JSON, etc.), it also needs to ingest unstructured data like PDFS and multimodal sources like video, audio, and images.

Many AI use cases require real-time data streaming #


Having up-to-date data no longer suffices. AI use cases like personalization, fraud detection, and product recommendation need to stream user activity data in real time. But adding real-time data capabilities to a data architecture is complicated.

Real-time data capabilities require a low-latency network coupled with fast reads, incremental writes, and strong data consistency. To meet these requirements, organizations need to bring services like Debezium and Kafka to their architecture. Complex tools like these can be daunting and require significant investment to implement properly.

AI requires strict data privacy monitoring #


AI models can be manipulated into giving responses they aren’t supposed to.

In the best case, this is just a humorous mistake — like the car dealership chatbot that was talked into offering cars for $1. However, if Personally-Identifying Information (PII) isn’t governed and protected, the door can be opened to serious breaches of customer privacy.

AI data breaches undermine customer trust, damage an organization’s reputation, and can incur regulatory fines in the millions of dollars. With AI regulations like the EU AI ACT coming into enforcement, AI safety and responsibility are becoming increasingly important.

Components of data architecture for AI #

AI data architectures have five major components:

  • Data collection and storage
  • Data processing
  • Feature engineering
  • Data governance
  • Data deployment

Data collection and storage #


Before you can use data in AI, you need to get your hands on it. In this component of data architecture, data is collected, modeled, and stored in a format. These processes happen within a data system: an OLAP or NoSQL database, a data warehouse, a vector database, cloud storage, streaming datastore, etc.

A key data type to collect and store is metadata — the information that describes and explains other data. Metadata provides context with details such as the source, type, owner, and relationships to other data sets. data is discoverable and traceable, as well as to enforce accountability through data ownership.

Data processing #


This component is where data is accessed and prepared for use. ELT or ETL processes pull data out of storage, load it into the system, and transform it to fit its intended use case.

Data processing is automated with data pipelines that process data, whether in real time (using technologies like Change Data Capture) or in batches. These data pipelines automatically merge and resolve any data conflicts while detecting and fixing data problems at their source. This data quality assurance happens through tests that verify the accuracy, completeness, and validity of data.

LLM integration #


The LLM integration component of AI data architecture is where RAG happens. This is where the LLM (which itself may be outdated, since training data lags by a year or two) is augmented with an organization’s own data.

One of the great advantages of AI is that data can be queried using natural language, and the results can be included in LLM prompts as relevant context. A development tool like LangChain can even be used to bring different LLMs together to take on different workloads according to their different strengths.

Once an organization’s data is integrated with their LLM(s) of choice, model behavior is extensively tested to ensure that the AI is returning safe and accurate responses.

Data governance #


Data governance is the process of managing the availability, usability, integrity and security of the data in an enterprise system according to the organization’s internal standards and policies.

Governance is the aspect of a data architecture that is present at every point in the data lifecycle: Whenever data is being used, data governance is there. And it becomes even more critical in the context of the massive and ever-increasing data volumes required for AI.

AI-specific data governance includes:

  • AI Accountability: Identifying and appointing leadership to give oversight to AI with a clearly defined strategy
  • Security: Staying ahead of AI attack vectors to maintain integrity and privacy
  • Reliability and safety: Assessing and maintaining the quality and safety of AI agents
  • Transparency and explainability: Making AI models understandable by making their structure and behavior understandable and accessible
  • Increasing the value of underlying data: Create a culture of valuing data and accounting for data rights to increase the quality - and thus the business value - of data
  • Compliance: Classifying data, managing data lifecycles, and handling PII in adherence with data regulations like GDPR

Data deployment #


The final component of AI data architecture is deployment — when your AI product, including its RAG data, can be put into production. CI/CD (Continuous Integration and Continuous Deployment) systems automate this process, incorporating tests for accuracy and safety.

Data deployment also includes making data available internally for the use of different stakeholders across the entire organization. Your data team can create a master data set to be used by other teams, using techniques like master data management (MDM) to streamline this collaboration.

How Atlan can help #

Atlan is an active data governance platform that breaks data silos and prepares you for the AI age.

Active data governance automates data governance and quality policies, handling the large data volumes that AI projects require. Atlan uses its own AI to document your data estate, create automation playbooks, and manage metadata tags.

Atlan’s self-service experience unlocks data across the enterprise, enabling teams to build their own data meshes, keeping data quality high. See what Atlan can do for your AI development by booking a demo today.

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