1. Capture facts at the source
The first step is simple but powerful: capture business facts where they happen. Operational domains are closest to the action, which makes them best positioned to record events as they occur. Yet, in a typical setup, data teams spend up to 80% of their time cleaning and preparing messy, batch-exported data from operational systems. This is a massive drain on resources.
Instead, EDA promotes a model where operational domains publish clear, standardized business events—like OrderShipped—as they happen. These events become immutable facts. If the data is clean and well-structured at the source, the need for costly and error-prone cleaning processes downstream is virtually eliminated.
2. Separate facts from interpretation
A huge source of confusion in any organization comes from duplicated and conflicting business logic. Operations define a metric one way, data teams define it another, and soon the same underlying reality produces inconsistent results.
The way forward is to separate immutable facts from evolving interpretations. An event is a statement of fact: "Car X collided with tree Y at 10:30." This fact never changes. Interpretations, like calling the car a "total loss," are projections built on top of that fact. These interpretations can and should evolve depending on the context (e.g., insurance vs. repair assessment).
The danger lies in allowing interpretations to circulate and be treated as new facts. One team builds on another's interpretation, and a third team adds its own logic. Before you know it, your entire analytical framework is built on quicksand. Anchoring every analysis in the raw, immutable events prevents this downward spiral. It doesn't remove the need for alignment on the semantics of interpretations, but it ensures you always have a stable, trusted foundation to fall back on.
3. Let technology follow the use case
Too many organizations force every problem into their favorite tool, whether it's Kafka, Databricks, or Snowflake. This one-size-fits-all approach inevitably leads to technology silos and vast sums of money wasted on "duct tape" integrations to glue them together.
It doesn't have to be this way. An event backbone like Apache Kafka allows both operational and data ecosystems to work from the same stream of trusted facts. Real-time operational dashboards can project state directly from events, while complex analytical reports can be generated in a dedicated data platform like Databricks or Snowflake.
With modern advancements like Tableflow and native Iceberg support in Kafka, the flow of data between the operational and analytical planes becomes seamless. You don't need to force your teams to leave their comfort zones; you empower them with the right tools for their specific job, all while working from a single source of truth.
Conclusion: creating a shared language of facts
By forcing us to qualify data at the source, define responsibilities clearly, and choose the right tool for each use case, Event-Driven Architecture addresses the root cause of the operations-data divide. The result is higher data quality, less friction, and an organization that can react faster because everyone is working from the same trusted facts.
Ultimately, bridging this gap isn't about more tools or reports. It's about creating a shared language of facts that the entire organization can trust and build upon.
Bridging this gap is a complex technical and organizational challenge. If you're looking for an expert partner to help you build this shared language, we're here to help.
