By Dirk Budke and Anatoly Zelenin
In practically every industry, digitalization is forcing companies to radically rethink their data processing. Whether in finance, manufacturing, retail, or logistics – the transition from slow, batch-based processes to a modern real-time architecture is no longer a luxury but a strategic necessity. Outdated data leads to missed opportunities and poor customer experiences.
The open-source platform Apache Kafka is a key technology that enables this transformation across all industries. But its introduction is more than just a technical project; it’s a strategic journey. This 5-stage maturity model serves as your compass on this path. It helps you understand where your organization stands today and what steps are next to evolve from a rigid data silo to a dynamic flow ecosystem.
At this stage, data resides in isolated, static databases, forming data silos. Each department manages its own systems, and communication between them, if it happens at all, is delayed. Data is typically exchanged once a night in large batches.
A customer makes a payment with their credit card but only sees their updated account balance at the end of the month. This causes confusion and harms the customer experience.
Architecture: Isolated databases, often connected via traditional Enterprise Service Buses (ESB).
Data Flow: Slow, nightly batch processes.
Problems: Outdated and inconsistent data, slow corporate responsiveness.
The first step toward modernization often begins as a point solution for a specific problem. A department introduces Apache Kafka to establish a single, critical connection in real time.
A manufacturing company streams sensor data from a machine to the quality assurance system in real time to detect defects immediately.
Approach: Ad-hoc use of Kafka for specific use cases, often without an overarching strategy.
Outcome: Local value is created, but the organization as a whole remains in silos.
Challenge: Scalability and reusability are not established.
The success of initial projects sparks interest in other departments. The use of Kafka expands, and more systems are connected to enable the first cross-departmental processes.
A supermarket chain connects its point-of-sale systems with the central inventory management system. Sales are immediately reflected in the stock levels for all channels (online & offline).
Development: From isolated streams to a central platform connecting multiple systems.
Value: Cross-departmental real-time use cases become possible.
Challenge: Increasing technical complexity and different data formats require coordination.
With increasing connectivity, a clear governance structure becomes essential. To avoid chaos and ensure that data streams are consistent, secure, and compliant, company-wide standards must be established.
A national railway company streams real-time data from trains, stations, and ticketing systems. Governance ensures, through standardized formats and access rights, that the passenger app only receives correct and authorized data.
Focus: Establishing data quality, security, and compliance.
Tools: Introduction of Schema Management, data contracts, and clear access rights.
Responsibility: Business departments take ownership of the quality of their data streams.
At the highest stage of maturity, data is treated as a strategic asset – as a product. The organization establishes a decentralized data architecture, often referred to as a Data Mesh, in which teams autonomously manage and provide their data products.
Principle: Decentralized ownership; data is treated as a product developed and offered by domain teams.
Architecture: A Data Mesh enables self-service for data consumers.
Outcome: A self-service data catalog accelerates innovation, as teams can access high-quality data from across the organization in an agile way.
The transformation into a data-driven organization is a journey, not a sprint. The maturity model presented here shows that this change goes far beyond the mere introduction of a new technology. It is a holistic process that involves organization, culture, and technology.
Apache Kafka is the technological backbone that enables this evolution – from abandoning old data silos to building a comprehensive flow ecosystem. Companies that successfully navigate this path will not only increase their operational efficiency but also lay the foundation for innovative business models and a superior customer experience in the real-time world.
About Dirk Budke
Dirk Budke is Head of Engineering at mesoneer and responsible for AI and Data Engineering.
About Anatoly Zelenin
Hi, I’m Anatoly! I love to spark that twinkle in people’s eyes. As an Apache Kafka expert and book author, I’ve been bringing IT to life for over a decade—with passion instead of boredom, with real experiences instead of endless slides.
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