By Dmitry Butalov
For years, we’ve heard, “Data is the new oil.” However, for EdTech companies, that analogy falls short. Oil gains value when refined—so does data. Its worth doesn’t come from mere collection but from thoughtful design, integration, and application. That’s the essence of Data as a Product (DaaP).
Data is no longer a passive byproduct in the evolving EdTech landscape—from self-paced apps to corporate learning platforms—it’s a core, designable asset. Treating data as a product means managing it with the same intentionality as curriculum or code: structured design, clear ownership, and continuous iteration.
The EdTech market is booming—projected to reach $158 billion in 2024 and $430 billion by 2030—driven by AI, VR, and hybrid learning. But so is competition. Traditional institutions have gone digital; simply digitizing textbooks or layering quizzes onto videos isn’t enough. Today’s stakeholders—schools, parents, employers—demand proof. Does it work? Can you show the results? What’s the ROI?
Answering those questions often hinges on how well a company can productize its data.
What ‘Data as a Product’ really means
DaaP means treating data like software—deliberately designed, rigorously maintained, and continuously improved. It’s not just about gathering data but about making it:
- Clean: structured and standardized
- Contextualized: tied to user behavior and outcomes
- Accessible: easy to use across teams and systems
- Actionable: supporting real decisions, not just reports
Most EdTech companies collect plenty of data—from test scores to clickstreams—but few turn it into intelligence that drives outcomes. The leaders don’t just store data; they build systems that learn from learners.
Turning data into a learning advantage
Treating data as a product unlocks continuous improvement and deeper personalization across the educational experience. Here’s how:
Real-time course optimization
Behavioral data—where learners disengage, which videos are rewatched, where assessments fail—can surface friction points instantly. Teams can then adjust courses dynamically, not months later.
Personalization at scale
True personalization means more than greeting users by name. DaaP supports adaptive pacing, targeted content, and just-in-time nudges. In corporate settings, learning paths can automatically adapt by role, region, or performance pattern.
Smarter strategic decisions
One constant question for CTOs, instructional designers, or L&D leads is: What’s working? With DaaP, dashboards replace static reports, offering real-time insights across cohorts, geographies, and content types.
Data as a differentiator in sales and accountability
While courses are often the core product, data is increasingly part of the value proposition—especially in B2B and institutional contexts.
- For enterprise training, clients want proof of learning. DaaP powers insight delivery, not just content delivery.
- In K–12 and higher education, real-time reporting builds trust with administrators and families. It’s not surveillance—it’s clarity.
Integration is key for large customers. If your learning data can’t be integrated into their HR systems, ERPs, or internal dashboards, you’re not just lagging—you’re invisible.
AI that works starts with data that’s ready
Everyone’s talking about becoming “AI-first,” but most EdTech efforts start at the wrong end: building flashy features instead of solid infrastructure. Chatbots and generative interfaces are exciting, but their impact is shallow without high-quality, structured data. Instead, treat foundational models as infrastructure. Then, build intelligent, context-aware layers on top—informed by real user data.
Enter model-context protocols (MCPs): frameworks that define how an AI agent behaves in educational settings. They pull in learner history, lesson goals, performance patterns, and misconceptions. The result? An AI tutor that doesn’t just answer—it teaches. Smart AI starts with smart data.
Rethinking business models with data as a product
Subscription models have limits—especially in crowded markets like language learning. DaaP opens doors to new models:
- Freemium content in exchange for anonymized data used to generate benchmarks or research partnerships.
- Data-backed outcomes are a premium service, particularly valuable in workforce training and compliance.
This approach isn’t for everyone—it demands strong governance and ethical boundaries—but it view data as a strategic asset, not just operational exhaust.
Laying the groundwork for DaaP maturity
Real DaaP capability doesn’t come from exporting CSVs. It comes from connecting your entire tech stack—LMS, CMS, CRM, and assessments—and intentionally designing data flows. And like any product, data needs an owner. A data product manager should oversee the following:
- Data quality
- Privacy compliance
- Governance
- API accessibility
- Cross-team usability
Without ownership, data remains fragmented. With it, it becomes foundational.
Motivation is the metric
Ultimately, motivation is the single greatest predictor of learning success. But motivation can’t be left to chance. Data and AI should be used to reduce friction, personalize experiences, and nudge learners forward. EdTech should be more than content delivery—a dynamic system that learns from every learner interaction. We need a new mindset to make that possible: data isn’t just something we collect. We design, refine, and deliver it—just like any product.
Image credit: IR_Stone
