Data silos are an expensive problem, estimated to cost the global economy nearly $3.1 trillion annually. As environments and enterprises grow, data can be isolated by sinks, sources, streams, and teams, leaving it sitting unused as its value rapidly depreciates. 

Because it can support data synchronization, sharing, and migration, Apache Kafka is an excellent technical fix. However, the root of the issue is organizational, because Kafka cannot fix inefficient processes—it can’t help users independently onboard new accounts, discover or access data, or automate overly manual procedures. Often, Kafka scales more rapidly than its supporting workflows, meaning that teams are rapidly overwhelmed.

That’s where an intelligent data hub like Conduktor comes into play, by bringing granular controls to Kafka while helping companies reform their processes and structures.

How siloed data and operations harm your business

Without the right infrastructure, siloed data slows down the flow of insights to leaders, or is excluded from analytics and other business-critical functions. This creates flawed analyses, negatively impacting executive decision-making, competitiveness, agility, and product adoption. 

Siloed procedures are equally important—and perhaps even harder to solve. While Kafka’s design decouples producers and consumers (and makes it easier to share data asynchronously), that doesn’t necessarily make data shareable or reusable by default. 

Instead, someone will have to monitor data streams in order to understand what data is located where. Without comprehensive, in-stream visibility, it’s hard for teams to keep track of what data exists, who owns it, and where it’s being used—especially as Kafka usage, traffic, and infrastructure grow. Some organizations don’t assign ownership for data utilization—so it simply goes undiscovered and unused.

In addition, platform teams have to put in the work to promote discoverability, and authorize user accounts and permissions. However, in environments with over 10 clusters or projects, forcing a single platform team to carry out all these duties will be unsustainable. In turn, this also has knock-on effects for downstream, customer-facing (and likely business-critical) applications.

Apache Kafka is a data backbone—but it’s not enough

The first part of the solution is technical: organizations can use Kafka as a central nervous system to connect the disparate parts of their data infrastructure. Kafka’s publish/subscribe model decouples producers and consumers, freeing them from point-to-point data sharing, allowing them to communicate asynchronously and reuse data streams. By delivering one message to several subscribers, Kafka reduces silos by ensuring that data is always in motion, lives in multiple places and is accessible to various teams and users.

Kafka’s decentralized, agnostic model also makes it possible to easily and quickly share event data without configuring tools like APIs or relying on infrequent data syncs or dumps. Users can simply make data available and stream it across teams. Because Kafka also supports hybrid and multi-cloud architectures, teams no longer need to use multiple data sharing solutions or worry about infrastructure-based silos.

But Kafka by itself is insufficient to remove all silos. Operationalizing Kafka—putting it into practice across a complex environment or organization—is the real challenge. This includes getting visibility into Kafka streams, promoting data discoverability, regulating data access, and approval chains. 

Once Kafka environments exceed 10 clusters or projects, onboarding requests pile up, data becomes duplicated, and teams can’t find or get the data they need. 

At this point, it’s simply unsustainable for platform teams to resolve all these issues, and they become operational bottlenecks. 

In these situations, teams need Conduktor’s intelligent data hub to realize Kafka’s full potential. By filling both technical and process gaps in Kafka infrastructure and teams, Conduktor empowers platform teams to balance autonomy with platform stability, improve the discoverability and usage of data, and eliminate data silos. 

For instance, platform teams can manage user authorization policies, audit data access logs, and standardize self-service, so that developer teams can onboard or spin up new Kafka accounts and clusters within a consistent, secure framework. This ensures that teams can move quickly without compromising security, and removes bottlenecks such as ticketing workflows or ad hoc provisioning.

With Conduktor’s centralized resource management, users can more easily discover data of interest. By unifying all streaming resources, Conduktor helps teams more easily find the data they need, while ensuring that policies and ownership remain consistent. As a result, organizations can accelerate collaboration, improve data visibility and usage, and finally, speed up innovation and delivery. 

When scaling Kafka slows you down

Several real-world stories from the financial sector highlight this dilemma. Two Europe-based institutions with massive workforces, up to 20 business units, and large Kafka deployments, either split across multiple teams or administered by a central platform team.

At one bank, Kafka projects and infrastructure are distributed across eight teams, each in different time zones and departments. Any new actions, from onboarding new users to wrangling data permissions, required multiple weeks of processes. To compound matters, this bank also lacked the right tooling to keep track of infrastructure, resulting in duplicated data and high infrastructure costs. 

At another bank, a platform team of seven people oversees 30+ clusters, 18 business units, and multiple brands. Each action, no matter how small, required attention and intervention from the platform team, resulting in slow, manual workflows and stalled innovation. 

Both banks used Conduktor to resolve their challenges. At the first organization, teams are currently evaluating Conduktor’s self-service features, which will enable platform teams to define operational guardrails for developers to independently onboard and access data. At the second bank, teams use Conduktor to track usage across clusters and automate resource provisioning, removing overhead. 

How unified data benefits teams—and organizations

Centralizing data brings key benefits and new use cases, demonstrated by two customer stories, one from finance and the other from online retail. 

The first is a Europe-based stock exchange that uses Kafka for trading algorithms. Previously, data was stored and only made available to users and applications after the market closed. However, they soon realized that this siloed process was inefficient—waiting for data at the end of the day meant that it was inaccessible for other users and applications.

Instead, they freed up data for ingestion through the workday—and quickly reaped the benefits. They unlocked new use cases, such as trade matching algorithms or hour-by-hour replays, for more rapid actions and detailed insights. Best of all, they were able to extract value without disrupting existing data flows.

A similar example comes from an online retailer, which uses Kafka to power their shopping cart and their real-time analytics application. This feature helps them understand the customer journey, such as when they added an item, what other products are added alongside that product, and how long it takes to finally check out. This granular information helped them better improve their recommendation engines and analytics. 

Kafka provides a technical backbone to remove data silos, but Conduktor empowers your teams to unify and simplify procedures and workflows. To learn more about eliminating siloed data and operations, book a personalized demo with our team.