How Media Platforms Can Break Their Data Silos With Optimized Databases & Integration Engines

It’s no secret: media consumers’ priorities have shifted. In 2025, media conversion for social media stood at 78%, compared with just 45% for news media.

So, the imperative is clear: find a way to compete with social media and the like for the limited reader attention – or die.

Data is the key ingredient to capturing that scarce attention. It tells you who your readers are, what content performs well with different segments, and which marketing spend delivers the highest ROI. It’s also the foundation for real-time personalization, automation, and engagement forecasting.

Yet, even though data may be abundant, it remains disparate. Almost half of media companies (48%) don’t systematically connect their systems. Only 5% have them fully integrated. The result? Data silos that hinder real-time data availability, undermine analytics, and make process automation impossible.

No wonder 57% of media CEOs believe their company won’t be viable in a decade. But there is good news: reimagining your business model is within the realm of possibility. Breaking silos and creating a unified media data architecture is the first step to doing so. Here’s how Exoft helps media publishers take it.

What Leads to Data Silos Across Media Systems?

Data silos are a persistent issue across industries. In fact, 68% of organizations cite the number of data silos as a key challenge. Moreover, 82% say data silos disrupt their critical workflows.

Having isolated datasets hinders data sharing between teams and systems. It also leads to incomplete or inconsistent records, duplicated entries that inflate storage costs, and unreliable analytics output.

The sheer number of media data systems is the main culprit behind disconnected data. Enterprise IT leaders surveyed for the 2025 Connectivity Benchmark Report reported that their organization uses an average of 897 (!) applications. These systems can include:

• E-commerce systems
• Subscription engines
• Website and content analytics
• Marketing automation tools
• Content management systems
• Agency tools

Media businesses may find themselves juggling dozens or even hundreds of separate systems for a number of reasons, such as:

• Rapid business growth. Adding new revenue streams often calls for using dedicated tools and creates separate databases.
• Mergers and acquisitions. Following M&A, companies find themselves with multiple systems and tools serving the same purpose, sometimes with incompatible schemas or wildly different data models.
• Team-level decisions. Without enterprise-wide guidance on tooling or media data integration, teams are left to their own devices, unintentionally creating a data silo or duplicating functionality.
• Legacy systems. Previous capital investments and migration challenges may discourage abandoning them. Yet, monolithic, rigid systems create additional challenges, such as a lack of support for modern APIs or event-driven data flows.

You may have as many databases as you have systems. One database may use a relational data model, while another may store data as entities and relationships. Separate databases may also use different formats for the same data (e.g., DD/MM/YYYY vs MM/DD/YYYY) or different IDs for the same record.

That’s why creating a centralized media data repository isn’t as easy as connecting a bunch of APIs to yet another database. Before it can be brought together, data has to be standardized, cleaned of duplicates, and checked for errors.

Pro tip! Opt for an integration engine to streamline data transformation and validation before its integration. This engine creates a centralized layer that ingests, transforms, and consolidates data from diverse sources.

What Is the Impact of Data Silos for Media Businesses?

Breaking data silos may require a major overhaul of the way you store, process, and access data across applications. That is, of course, not cheap, nor is it easy. But the cost of leaving them be is even greater, since data silos lead to:

• Lack of personalization. Personalization is king in the age of algorithmic content delivery, think adaptive content feeds and tailored messaging. Without real-time data combined with customer data, however, you risk incorrect personalization, which may lead to lost subscribers and disengagement.
• Incorrect targeting. Without a 360-degree view of user data, you can’t segment your user base and pinpoint the most impactful messaging or monetization strategy. Plus, personalization in marketing matters: 96% of marketers say personalized experiences lead to higher sales.
• Faulty analytics. “Garbage in, garbage out.” That’s the common name for a simple truth: if your analytics tools ingest incorrect, incomplete, or flawed data, their output will also be inaccurate.
• Poor adaptability. If you’re using multiple channels (and you must be), unifying these performance metrics is a must to catch engagement trends. Data silos fragment that data, leaving your editorial team either to guess the trends or follow imprecise analytics.
• System performance degradation. Isolated pockets of data contain duplicate records and/or inconsistent data. Duplicates inflate storage needs and slow down processing time. Data inconsistencies, in turn, cause performance issues over time.
• API errors or performance issues. Databases that were left to grow chaotically slow down API calls or cause errors. That means API access to data becomes patchy and prone to delays.

How Exoft Can Help You Break Silos (Spoiler: Databases and Integrations Are the Key)

By this point, it should be clear: breaking data silos isn’t optional. But there’s a reason why the problem persists. Getting rid of silos means revamping the whole data architecture, investing in integration engines and APIs, and, in some cases, moving away from legacy systems.

It’s a massive undertaking. Here’s how we approach it at Exoft.

Build a Unified Data Architecture

Media and entertainment giants like Netflix have moved to a unified data architecture. It serves as the single foundation for all the connected data. Its motto is simple: model once, represent everywhere.

To build a unified data architecture, you’ll have to create domain models for all the data your company handles. Then, work out how it should be represented in specific systems using different data models.

When implementing the unified data architecture:

• Review how you store data. If you still use legacy databases, replace them with more modern counterparts. Modern databases (e.g., graph, NoSQL databases) can store relations between records, making datasets richer. They are also more scalable and flexible, enabling faster data processing and exchange.
• Use consistent data models across systems. Every system you use may require a different model adapted to the database type. That said, schemas and naming conventions still have to be standardized across systems to facilitate integration.
• Optimize queries. Give enough thought to media database optimization to ensure fast data processing. Query optimization can involve indexing and removing redundant data retrieval. Review query execution plans to identify areas of improvement.
• Improve data quality. Your analytics will remain unreliable if you don’t ensure data quality. Set up automated data cleansing and implement data validation techniques.
• Monitor data integrity. To maintain high data quality, implement automated tools that profile, evaluate, and clean data.

Query optimization and data integrity monitoring can go a long way. For example, it helped one of our clients support database operations across six enterprise-scale systems, all while improving the MTTD and MTTR.

Connect Systems Through Integration Engines & APIs

If you use dozens of applications, connecting them requires an integration engine. This software enables data exchange between them. So, you won’t have to manually export and import data or puzzle over resolving interoperability and communication protocol issues.

Integration engines use APIs and other integration methods (e.g., web services) to connect systems. But data exchange isn’t the only feature of an integration engine. It also:

• Automates data transformation
• Validates data to ensure its quality
• Monitors data flows for interruptions or errors
• Tracks the overall system performance

For example, the platform integration engine we modernized and maintained for one of our clients enabled data exchange between:

• Marketing orchestration platforms
• Agency management tools
• Content management systems
• Order management platforms

Adopt Modern, Composable Software

Retaining legacy systems will only perpetuate data silos. The reason? They don’t play well with newer applications and technologies, making integration an endless nightmare. Interoperability issues, data quality problems, and slow performance feature prominently in that nightmare.

While legacy modernization isn’t an easy undertaking, it’s not impossible. For example, with our help, one of our clients moved away from a monolithic enterprise system to a scalable, event-driven one. Implementing microservices architecture and replacing Microsoft SQL Server with PostgreSQL led to:

• Seamless data validation and verification
• Faster performance
• Fewer bugs and errors
• Improved code quality and security
• Enhanced support for expansion through new features and integrations

You can resolve the legacy system conundrum by either updating the application or replacing it with a new one. Changes can span from rehosting and refactoring to full-on rearchitecting. The best way forward depends on the state of your legacy system.

That said, no matter which path you choose, make sure your new system:

• Runs on microservices
• Is API-first
• Uses an event-driven architecture

Establish Clear Data Governance

All of your data unification efforts will be in vain without a solid data governance framework. This framework defines:

• Data policies, i.e., how the data should be stored, accessed, used, managed, and maintained
• Standards, i.e,. clear rules that ensure data quality and interoperability
• Roles and responsibilities, i.e., who is responsible for what (e.g., media database management)
• Stewardship, i.e., dedicated stewards/teams who continuously oversee data management processes
• Tools, i.e., software you use to catalog data, manage metadata, monitor compliance, and so on

To develop a data governance framework, start by defining goals and objectives first and roles and responsibilities second. Then:

• Create policies and standards
• Establish a data catalog and classification
• Outline data quality and management protocols
• Implement data controls and permissions
• Set up data stewardship

Regularly review and improve the framework to make sure it keeps up with changes in your business needs and available data.

Implement System Monitoring & Alerting

There’s always a risk that integrations fail, be it due to format mismatches, API downtime, server overload, or network congestion. Real-time system monitoring will help catch those issues before they become a problem. Automated alerts, in turn, will let your dedicated team know that they need to intervene.

Here’s what you should look out for:

• Delays in sync
• Failed jobs
• API failures
• Slow queries
• Performance drops

In addition to that, set up data health monitoring. It’ll help you catch data quality issues early, before they cause significant damage. Data health can be measured using the test-fail rate or failed-row-count metrics. Other data quality metrics to watch include:

• Duplication rate
• Dark data amounts
• Consistency score
• Timeliness index
• Data-to-error ratio
• Completeness ratio

Conclusion

Breaking data silos isn’t as simple as stitching separate systems together with a couple of APIs. To make data truly reliable and valuable, you’ll need to rethink how you store and process data with a unified data architecture. Your databases will also need optimizing to ensure data quality and integrity.

While it can be a colossal undertaking, think of it as a long-term investment that:

• Reduces manual work with data
• Improves reporting speed
• Increases data accuracy
• Boosts customer lifecycle metrics

Ready to make data silos a thing of the past? Exoft specializes in helping media publishers turn their data into a value-adding asset. Get in touch with Exoft to discuss how we can help you set up a unified data architecture, integrate systems, and optimize databases.