Building an AI-Ready Data Platform Step by Step

In this article, I'm going to show you how our incredible team of data engineers builds a modern and AI-ready data platform from scratch. We will explore how we start with a small dataset and gradually build a reporting-ready data lake and a fully modular data platform.

Before we start, let's talk about data lakes versus data platforms.

A Data Lake is fundamentally a storage platform where raw data from various sources is collected and stored, primarily focused on consolidating data, improving data quality and enabling reporting, business intelligence and accessibility.

A Data Platform, however, represents an end-to-end ecosystem that goes far beyond simple storage. It covers the entire data lifecycle and creates a trusted, scalable foundation for analytics, automation, and AI readiness.

Data Platform Implementation

Building a modern data platform is a progressive process, not a one-off task. Let's take a look into the five phases when building such a platform.

Phase 1 - Data Ready for Use

Our data journey begins with establishing the foundational Data Infrastructure component, designed to transform raw data into reporting-ready assets. The main objectives of this phase are to build a single source of truth, accelerate reporting capabilities, and reduce pressure on operational data source systems by centralizing data processing and storage.

This component consists of three essential layers:

• Data Workflow Layer: it is the part that moves and changes data around the platform. First, data is taken from the source systems and put into the platform in a way that is easy to understand and organised. This process includes batch loads, scheduling, managing schemas and checking data. When the raw data arrives, the transformation component takes over. It cleans, standardises and shapes the information into models that can be trusted and used for analytics. This stage uses business rules to join and group data together. This makes sure that the data sets are all the same and ready to be analysed.
• Store Layer: also called the Data Lake, provides the physical structure for organising data. It uses the medallion architecture, which goes from Bronze to Silver to Gold. The Bronze zone stores raw data exactly as it arrives, so you can audit and debug it. The Silver zone makes this data even better by cleaning, standardising, and checking it so that everyone uses the same definitions and formats. The Gold zone contains the datasets that have been checked and are ready to be used for reporting and dashboards. These datasets are the final, trusted version of the information used for decision-making.
• Control Plane Layer: makes sure that the data platform is automated, controlled, and stable. It manages all deployment processes through CI/CD pipelines, ensuring that every change to data pipelines, transformations, and configurations is automatically tested, validated, and promoted without introducing instability. Additionally, the Control Plane maintains table definitions, schemas, partitions, locations, and file formats, enabling processing engines to correctly interpret and query the data.

This foundational phase establishes the core data processing capabilities that enable organizations to move from disparate, siloed data sources to a unified, analytics-ready data repository.

Phase 2 - Data Accessibility & Compliance

In the second phase, the data platform evolves from simply preparing data for reporting to making that data highly accessible, consistently fresh, and governed by strong security and compliance standards. This is where the platform becomes reliable not only for analysts and BI teams but also for a broader range of business users who expect reliable, trustworthy, and well-structured information.

The introduction of real-time ingestion and improved data orchestration significantly reduces latency, allowing the business to rely on more up-to-date insights. At the same time, semantic models simplify access to data by providing a unified, business-friendly layer that shields users from technical complexity and ensures consistent definitions across the organization.

Security and compliance become core priorities at this stage. The platform enforces robust access policies and ensures that data is protected and handled according to industry best practices and regulatory requirements.

Together, these capabilities transform the platform into a secure, accessible, and business-aligned environment, ready to support broader self-service analytics and more advanced use cases.

Phase 3 - Trusted Data Foundation

In this phase, the platform evolves into a reliable, consistent, and well-managed system. The focus shifts from simply moving and securing data to making sure it can be trusted in the long-term. This includes creating golden records through master data management, enforcing canonical models and validation rules across systems, and introducing full observability in order to detect and resolve issues proactively.

• The MDM Layer establishes authoritative and consistent records for the most important business entities. It reconciles data coming from multiple systems, resolves duplicates, and creates a single, trusted version known as the golden record. This layer also supports controlled writeback to operational systems when corrections, enrichments, or standardized values need to be propagated back. By maintaining these unified identities and attributes across the organization, the MDM Layer ensures that every downstream dataset and analysis is based on the same consistent definitions.
• The Integration Layer enforces consistency and standardization across all incoming data. It introduces canonical models that unify the structure and meaning of data sourced from different operational systems, aligning them to shared definitions and formats. This layer also performs validations to ensure that the data adheres to business rules and expected quality thresholds before moving further downstream. Through these mechanisms, the Integration Layer guarantees that data remains coherent and interoperable across teams and applications.
• The Observability Layer provides transparency into how data behaves throughout the platform. It monitors data pipelines, freshness, schema changes, and processing quality, enabling teams to understand the health and reliability of the entire data ecosystem at any moment. When anomalies or failures occur, exception reporting highlights the issue, identifies root causes, and helps prevent downstream impacts. By making data issues visible and actionable, the Observability Layer brings predictability and operational confidence to the entire platform.

The result is a platform where data can be trusted, where errors are visible and traceable, and where every dataset follows strict and clearly defined rules.

Phase 4 - Data Platform Foundation

At this stage, the data platform becomes one place where you can find all your business information. It supports not just business intelligence, but also advanced analytics, quick experimentation, and machine learning projects that can be adjusted to suit your needs. Data scientists, analysts, and domain experts now have easier access to high-quality, well-documented datasets, which means it takes less time to get from exploring to using the data. They can explore new ideas quickly, try out different experiments, and use machine learning models in the same way that they use other data workflows.

The platform also encourages true cross-functional collaboration. Many teams can work together on the same data, information, and software without any problems. Robust versioning, access control and governance mechanisms ensure that innovation happens quickly without sacrificing data integrity or trust.

The main focus of this phase is to extract real business value from the data and make that data accessible to external systems. The platform scales analytics both across business verticals and functional teams, enabling deeper insights, specialized analysis, and cross-team collaboration. This creates a flexible and robust foundation for advanced analytics, operational intelligence, and data-driven products.

Phase 5 - AI Adoption

In the final phase, the data platform evolves into an intelligent ecosystem where AI becomes an active participant in daily operations. The focus shifts from analytical insights to real-time decisioning, automation, and conversational interaction. Generative AI capabilities enable teams to explore data, design workflows, and perform complex analysis through natural language prompts, removing the technical barriers that once limited access to data.

AI agents become a core component of the platform, providing a catalog of specialized capabilities such as incident detection, automated troubleshooting, and self-healing responses. These agents continuously monitor pipelines, infrastructure, and data quality, reacting instantly to anomalies and preventing issues before they impact the business. This unlocks a new degree of operational resilience and efficiency.

The platform also enhances collaboration by enabling intelligent, cross-team interactions. Business users, engineers, and analysts can interact with data and automation services conversationally, relying on AI to translate intent into actions. At the same time, the Data Share Layer ensures that the platform remains open and interoperable, exposing data through direct-query endpoints, APIs, and export mechanisms aligned with predefined integration contracts.

With AI fully integrated into the platform’s fabric, organizations can scale automation, accelerate innovation, and empower every team to operate with greater speed, precision, and intelligence.

The Evolution of a Modern Data Platform

Building a modern data platform is not a single project but a progressive journey through five transformative phases. Each phase lays the foundation for the next, gradually expanding capabilities, improving data quality, and unlocking new forms of business value. As the platform matures, it evolves from simply collecting data into a more advanced, intelligent ecosystem that supports analytics, automation, and AI-driven operations.

The journey begins with establishing the fundamental data infrastructure and delivering reliable reporting. It continues by improving accessibility, governance, and data consistency. From there, the platform expands into advanced analytics and multi-team collaboration. In the later stages, AI agents, automation, and natural-language interaction become integrated into daily workflows, turning the platform into an active participant in decision-making and operations.

Together, these phases illustrate the complete journey of data maturity, progressing through five key stages of value:

• Descriptive analytics helps organizations understand what happened by analyzing historical data and generating reports or dashboards.
• Diagnostic analytics uncovers why something happened by exploring patterns, correlations, and root causes within the data.
• Predictive analytics anticipates what is likely to happen using statistical models and machine learning, enabling organizations to forecast trends and outcomes.
• Prescriptive analytics recommends what actions should be taken by evaluating possible scenarios and suggesting optimal decisions.
• Agentic analytics takes this further by enabling autonomous agents or AI systems to act automatically on recommendations, making real-time adjustments and driving outcomes without direct human intervention.

By progressing through these stages, organizations build a robust, intelligent data platform that evolves from simply describing the past to autonomously driving business actions, unlocking innovation, operational efficiency, and a sustained competitive advantage.

About the Author

Lucian Neghina, Head of Data Engineering at eSolutions, is a leading expert in data strategy, modern data architectures, and building scalable data platforms, whether cloud-based or on-premises. He is experienced in delivering complete end-to-end data solutions, leading and mentoring cross-functional teams, establishing best practices, and promoting collaboration, innovation, and continuous improvement.