This blog is a joint collaboration between Accedo and Merapar.

Tired of making decisions based on gut feeling, siloed dashboards, or conflicting reports? 

You’re not alone — many streaming services still struggle to truly understand what’s working and what’s not.

Truth be told: if you want to be an AI-first company, you must first be a data-first company.

That’s because any meaningful application of AI — from personalized recommendations to predictive churn interventions — depends entirely on the completeness, accuracy, and timeliness of your data.

However, despite major investments in features, content, and campaigns, it remains difficult to know what’s actually working. Why are users dropping off? What content truly retains subscribers? Where are the costs climbing — and why?

The culprit is often disconnected data. Without a unified foundation, services risk spending without insight, launching without learning, and optimizing without impact.

‍

1. Why leveraging data has always been a challenge

‍

To turn raw data into actionable insights requires two elements:

• Context — understanding not just what happened, but why.
• Actionability — being able to act on that insight in real time.

A raw event, like "buffering," is just noise. But when you add context – that this specific buffering event happened to a high-value subscriber watching the season finale of their favorite show – it transforms into a critical insight. Orchestration is what you do about it in that moment, like automatically triggering a different delivery path for that user.

The reason many OTT businesses haven't mastered this is largely historical. Many platforms were originally architectured for stability and delivery, not behavioral intelligence. Metrics for Quality of Service (like buffering or errors) are siloed from user behavior. Subscription records exist separately from content performance. As a result, key questions go unanswered — and decisions become reactive, rather than strategic.

Add to that deeply segmented teams — from product to marketing to operations — each working with their own tools and KPIs, and the challenge becomes clear. 

‍

2. Unifying data is the solution for a clearer vision

‍

A 360-degree data foundation brings together operational metrics, user behaviors, and business data into one integrated view. Instead of scattered dashboards and disconnected teams, you gain a shared, coherent understanding of your service — from performance to user experience to outcomes.

‍

From silos to shared accountability

With a unified foundation, teams stop working in isolation. They start speaking the same language and solving shared challenges together.

Product teams can use unified data to power AI-driven feature monitoring. After a UI update rolls out, the system correlates engagement metrics, device types, and performance data. If a segment — say, users on older Android TVs — shows a drop in activity, an AI agent can flag the issue, scan selected log files, and automatically roll back or adjust the feature for that group. No support tickets — just fast, targeted resolution.

Content teams go beyond what’s popular to understand what content actually retains users. Retention analysis might reveal that a niche drama series drives long-term loyalty more effectively than the headline blockbuster — guiding smarter renewal and acquisition decisions.

When all teams are connected through the same foundation, the conversation shifts from “my dashboard says…” to “our data shows — what do we do about it?”

‍

From system-wide metrics to personalized recovery

Operational teams focus on the big picture — they can’t monitor millions of users individually. But AI agents can. With access to unified data, AI can detect when a single user experiences buffering, drops off, or deviates from their usual behavior — and trigger timely actions like tailored recommendations or recovery messages. It’s a level of personalization at scale that human teams simply can’t achieve, helping prevent churn and improve experience one user at a time.

‍

From insights to measurable business outcomes

Beyond team collaboration and user personalization,  a connected view also enables proactive, high-impact action that brings real business values to streaming services.

• Imagine a high-value user whose viewing frequency drops just after experiencing buffering. With unified insight, the system can flag the risk and trigger a timely response — such as a personalized recommendation or recovery offer — preventing churn before the user cancels.
  
  
• When viewership trends and performance metrics are aligned, services can anticipate traffic spikes and automatically offload traffic — for example by activating peer-to-peer delivery during a major premiere. This maintains streaming quality while reducing delivery costs.

3. Building the data infrastructure for true observability

‍

To make this possible, the backbone must be a modern, composable data architecture — typically a lakehouse that merges the flexibility of data lakes with the structure and performance of data warehouses. This model supports both structured datasets (like user profiles and subscription records) and unstructured or semi-structured data (like player logs or content metadata), all in one place.

A composable infrastructure like this is essential to:

• Enable granular observability across Quality of Service, user behavior, content performance, and monetization flows.
• Support real-time data streaming and processing for instant detection and response to performance issues or user behavior shifts.
• Power intelligent automation and orchestrated AI agents, which rely on fast, holistic data access to make accurate decisions in the moment.

However, adopting this foundation doesn't mean starting from scratch. In most cases, it’s more strategic — and more efficient — to layer this architecture on top of your existing systems. By integrating rather than replacing, you retain existing investments while unlocking deeper, cross-platform insights.

As you consolidate data access, the challenge of data governance will emerge: managing who and what—including these new AI agents—can access that data becomes paramount. Ensuring compliance, security, and trust is a massive undertaking that requires a clear strategy from day one.

‍

4. Start small, but start now.

‍

Building a 360-degree data foundation can feel like a massive undertaking — but the key is to start small, with clear focus.  Don't try to "boil the ocean" by attempting to connect every single data source at once.

Focus on high-impact proof-of-value projects

Begin with a single, high-impact business challenge. For example, reducing churn among your most valuable users. To do this, you only need three core data sources: engagement metrics, subscription status, and QoS data. Form a small, cross-functional team — product, operations, marketing — and build a targeted proof-of-value.

If successful, this initial project not only delivers ROI, but also creates momentum and internal buy-in for broader transformation.

Learn from those who’ve done it before

This path may be new for many in media, but other data-driven industries have walked it before. Partnering with experienced teams can accelerate progress and help you avoid costly missteps.

Becoming a truly data-driven organization is a long-term commitment. But the first step — focused, strategic, and well-supported — is what sets everything else in motion.

Curious how your team can take the leap from siloed data to strategic insight and become a data-first company? We’re here to help. Let’s connect at IBC.