Why Your Company’s Data Infrastructure Should Be Built for Machines First

Key Design Principles for leveraging AI tools.

In The Fourth Paradigm: Data-Intensive Scientific Discovery, researchers from Microsoft envisioned a future where data isn't just something we collect — it's the engine of discovery and innovation itself. Today, that future is here. Companies that build their data systems with machine-first principles and AI-readiness in mind are already reaping significant advantages in efficiency, insight, and speed.

If your business wants to compete in an increasingly AI-driven world, it’s time to rethink how you manage your data. Here's what that looks like — and how leading organizations are already making the shift.

1. Start with Machine-Readable Data

Traditional data systems are designed for human eyes. But in a world where AI, automation, and analytics drive business value, data needs to be created from the start in formats that machines can process, interpret, and act on.

That means using open, structured formats like JSON or Parquet, tagging data with metadata, and creating semantic schemas that define relationships between data points.

Example: Toyota built a system where factory workers could train and deploy AI models using Google Cloud. That was only possible because the data was already structured and machine-ready.

2. Apply FAIR Data Principles

The FAIR framework — Findable, Accessible, Interoperable, and Reusable — is quickly becoming the gold standard for data infrastructure, especially in science and enterprise AI.

• Findable: Use consistent identifiers and indexing.

• Accessible: Store data in open, secure formats.

• Interoperable: Connect data across systems and domains.

• Reusable: Make sure data includes proper licensing, provenance, and documentation.

Examples:

• The UK’s National Health Service (NHS) is using FAIR principles to make health data safely accessible for AI applications.

• Hopsworks, a data platform for machine learning, uses FAIR design to help teams build models faster and more responsibly.

3. Track Data Lineage and Provenance

As AI becomes more integral to decision-making, it’s critical to trust where your data comes from and how it’s been used. Automating data lineage — the tracking of data’s journey through pipelines and transformations — helps ensure reproducibility, auditability, and compliance.

Example: Siemens combines real-time data with digital twins to monitor and optimize industrial systems. This requires not only sensor data, but clear records of every data transformation step.

4. Embrace Scalable, Cloud-Native Infrastructure

Data-intensive workloads — especially for training and running AI models — require scalable compute and storage. Cloud-based data lakes and distributed computing platforms offer on-demand resources that can grow with your business.

Example: Databricks powers data and AI platforms for companies like GM, Unilever, and McDonald’s, helping them deploy machine learning across operations using unified infrastructure.

5. Design for Interoperability Across Teams and Tools

To unlock true business value, data needs to flow freely — not sit in departmental silos. That means adopting open standards, semantic metadata, and APIs that allow teams to integrate and remix data across use cases.

Example: Workday is rearchitecting its systems to be modular and interoperable, enabling cross-functional AI tools for HR, finance, and beyond.

6. Align Data Strategy with Innovation Goals

It’s not enough to collect data — you need to make it usable. Forward-thinking companies treat datasets as internal “products” that are versioned, documented, and made available for reuse across teams.

Example: Hugo Boss recently invested €15 million in a centralized data hub to drive AI-powered product design, marketing, and customer experience — signaling a shift to tech-first fashion innovation.

Why Machine-First Design Enables AI Success

AI doesn’t work well with messy, fragmented, or opaque data. When data is clean, structured, and documented, models train faster, predictions are more accurate, and insights can be trusted. A machine-first data foundation:

• Accelerates AI model development

• Makes insights more reliable and repeatable

• Reduces manual data wrangling and engineering bottlenecks

• Supports compliance and governance

Conclusion: Build for Tomorrow, Today

Companies that treat data as infrastructure — not a byproduct — are positioning themselves for long-term success. Whether you're looking to power real-time analytics, launch new AI products, or create a more agile organization, the first step is clear:

Make your data work for machines first.

The future of innovation will belong to those who build it that way.

More details on the examples:

1. Siemens – Industrial AI and Digital Twins

Siemens has implemented AI-driven solutions in manufacturing, utilizing digital twins and real-time data analytics to enhance operational efficiency. By comparing real-time data against ideal models, Siemens can detect anomalies and prevent breakdowns on production lines, showcasing a machine-first approach to data infrastructure. The Washington Post

2. Toyota – Empowering Factory Workers with AI

Toyota has deployed an AI platform using Google Cloud's infrastructure, enabling factory workers to develop and deploy machine learning models. This initiative has led to significant time savings and increased productivity, demonstrating the benefits of making data accessible and actionable for AI applications. Google Cloud

3. Hugo Boss – Data-Driven Fashion Innovation

Hugo Boss invested €15 million in a data hub to transform into a tech-driven fashion platform. By integrating data analytics into product design, marketing, and sales, the company aims to enhance decision-making and prepare for future advancements in generative AI. Vogue Business

4. Workday – Transition to a Machine-First Ecosystem

Workday is shifting from monolithic systems to an ecosystem approach, integrating AI to improve user experience and automate tasks in HR and finance. The company's "Everyday AI" initiative reflects a commitment to embedding AI into core business processes, supported by a robust, machine-readable data infrastructure. The Verge

5. Databricks – Unified Data and AI Platform

Databricks provides a platform that unifies data engineering and AI, enabling organizations like GM, Unilever, and McDonald's to build scalable AI applications. By adhering to FAIR data principles, Databricks ensures that data is findable, accessible, interoperable, and reusable, facilitating efficient AI model development. Databricks

6. Hopsworks – FAIR Data for Machine Learning

Hopsworks has built a machine learning platform grounded in the FAIR data principles. By ensuring data is well-annotated and machine-readable, Hopsworks enables efficient training and deployment of AI models, emphasizing the importance of data quality and governance. Hopsworks

7. NHS (UK) – Leveraging FAIR Data in Healthcare

The UK's National Health Service (NHS) is focusing on implementing FAIR data principles to enhance care quality and efficiency. By making data findable, accessible, interoperable, and reusable, the NHS aims to support AI applications that can improve treatment outcomes and reduce costs. Financial Times