Across industries, we keep hearing the same thing: operational and analytical systems live in silos. So do the teams. They don’t use the same tools, speak the same language, or even see the same data.

This divide is inevitable. Operational data teams focus on ingestion, stream processing, and pipeline performance, or delivering data quickly to downstream teams. Analysts, data scientists, and platform teams care about data quality and readiness, both critical for analytics and AI.

Fragmented operational and analytical systems, processes, and teams create compliance, costs, and blind spots. Data sits unused, models lack context  to create accurate, trustworthy outputs. Operational efficiency suffers because dashboards and alerts lack important information. Teams also have no way to track data lineage or access, leaving sensitive data unauditable and non-compliant.

Operational vs Analytical: Two Different Species of Data

Operational data is the raw, real-time data that is the lifeblood of organizations, powering real-time decisions, coordinated action across teams and systems, and AI-driven outcomes. Produced by applications, machines, users, systems, and infrastructure, operational data is diverse in format and purpose. This data can be anything from a user transaction on a travel booking website to a temperature alert on an assembly line. 

While operational data deals with an organization’s daily operations, some of it can also be persisted and used for strategic planning and insights. As operational data ages from hours to days to months, it still retains value: this historical data can be used to provide valuable context for analysis and learning. With analytical data, leaders can answer both big-picture and fine-grained questions, from revenue per quarter to the profit margin at a single store on any given day or week. 

These two types of data are also located in different places: operational data lives in databases, streams, APIs, and message queues, while analytical data is stored in data warehouses, lakes, lakehouses, and other forms of inexpensive, unstructured storage. They’re also stored using different schemas and structures: for instance, operational data might sit in the rows, columns, and tables of a relational data schema, while analytical data is often flattened and denormalized into wide tables that make it easier to run complex, multi-dimensional queries.

When Data Moves, Pipelines Break

One key challenge is moving operational data to analytical storage and systems. If even a minor flaw travels downstream, the effects are amplified—and the costs for repair increase significantly.

Maintaining data pipelines comes with version control, data quality and data security. For instance, upstream developers might use object-relational mappers (ORMs) or deeply nested JSON in their schema. When this enters data pipelines and other systems, such as data lakes or analytics, there might be a schema mismatch, and in most cases the pipeline will break and stop running until someone fixes it. 

At a Conduktor customer, a major European postal service, teams have made some progress in bridging the gap between the operational and the analytical system and teams. Previously, their analysts and data scientists couldn’t fully utilize real-time data because they lacked a way to discover and browse Kafka data scattered across multiple clusters; further, even when they did find the data, they weren’t always authorized to access it. 

With Conduktor, hundreds of data analysts and data scientists in this organization finally got practical, secure access to Kafka, without needing to learn Kafka. They can search across clusters, see the structure of a stream, check sample payloads, and quickly decide if the data fits their use case. Importantly, access control policies defined at group-level ensure that users can view data within their permissions; once they find useful data, they can request to persist that stream into the data lake. 

The Bigger Barrier Is Process and People

Unfortunately for the postal service, moving operational data to analytical systems is an area of friction arising from process concerns. While users can explore or view this data, the process of persisting data into analytics can be cumbersome; while the team has standardized the process of requesting a connector, the key roadblock is validating the permissions internally, adding up to several weeks’ time to this workflow.

This illustrates an important point: the divide between operational and analytical is not just technical, it is also organizational. Business units are often disconnected, unable to work together earlier in the data lifecycle (“shift left”) to make the whole process more effective. 

Another example comes from a leading French financial firm, where a team of 50 architects are tasked with building applications and overseeing the data warehouse and the platform. Due to process silos, the architects did not work with data scientists to create either a data contract or a data dictionary (a reference of data elements, such as field names, lineage, and formats). 

As a result, these two teams did not have a shared understanding or documentation on the finer points of working with data, or details such as schema, KPIs, freshness and retention rules, and ownership or access control. Ultimately, they weren’t clear on where the data originated from, what it represented, how it could be interpreted, and what guarantees or KPIs were associated with it. 

This lack of alignment makes it harder for everyone across different teams. Platform engineers and architects can’t easily build data quality checks, automate data archival and deletion, or enforce other governance policies, simply because they don’t know what to look for. At the same time, data scientists are forced to rely on the central platform team to troubleshoot data pipelines, resulting in a backlog of tickets that take a long time to clear, delaying analysis and impacting productivity. All the while, everyone remains unclear on issues such as how long to retain data for, when operational data “expires” and becomes historical data, and so forth.

Given this confusion, compliance and AI also become very risky. Without a way to determine the origins, lineage, and usage histories of sensitive data, organizations are flying blind and potentially violating legislation. For instance, the GDPR requires that personal data include audit trails, logging everyone who viewed or interacted with the data, their locations, and their reason for access. Any violation can result in a fine of up to 4% of a company’s global turnover or 20 million euros.

Closing the Divide with a Governed Data Hub

As a governed data hub, Conduktor sits on top of the data streaming environment, between source systems and consumers (including applications, lakehouses, business intelligence tools, and AI agents). Conduktor makes operational data more easily discoverable, auditable and actionable. 

Conduktor also centralizes user permission management, simplifying the process and enabling platform teams to better control which analysts and data scientists can access data. It helps simplify connector management through templated configurations that predefine security settings, transformations, and mandatory fields. Conduktor also includes monitoring connector status and alerting on misconfigurations, as well as self-service connector deployments.

The operational-analytical divide is both a technical and organizational problem, but bridging this gap is possible. Conduktor helps organizations cross the divide, bringing operational and analytical data, systems, and teams closer: data is now discoverable (and usable), governance and access control are easy to implement, and connectors can be standardized, managed, and spun up as needed. Conduktor acts as a control plane for your operational data—so it flows securely and efficiently into the systems and teams that need it.

To learn more about what Conduktor can do for your operational and analytical teams, data, and infrastructure, sign up for a demo today.