Your Learning Data Is Organizational Intelligence. Treat It That Way.
Organizations are investing heavily in learning technology. This much is obvious.
They are buying (and re-buying) LMSs and LXPs. They are building simulations. They are experimenting with AI tutors, LLMs, adaptive learning, immersive environments, skills-identification and competency assertion systems, the ubiquitous dashboards, and workforce analytics. The ambition is to bring about better learning, performance, readiness, and ultimately, decisions.
But there is a hard truth underneath all of this investment. Most learning data systems were not designed to work together. In some cases, they were actively designed to not work together. At best, they were designed to report completions. Track enrollments. Store scores. Maybe you could export spreadsheets to satisfy compliance requirements. Maybe you could impress someone with a dashboard visualization that they hadn’t seen before.
That may have been enough when learning data lived mostly inside a single LMS. It is not enough for modern learning ecosystems. Today, learning happens across platforms, devices, simulations, games, operational systems, AI-enabled tools, and real-world environments. If the data from those experiences is inconsistent, fragmented, or trapped in silos, organizations cannot see what is really happening. They cannot evaluate what works. They cannot connect learning to performance. And they definitely cannot build trustworthy AI on top of it.
That is where Yet Analytics comes in.
From Data Collection to Data Strategy
At Yet Analytics, we help organizations turn learning data into a strategic asset. That starts with the assumption that learning data is not merely a reporting output. It is organizational intelligence. And when learning data is designed well, it becomes a shared layer that connects systems, supports analytics, enables automation, informs decision-making, and creates the foundation for AI-ready learning environments.
Of course, when it is designed poorly, every new tool creates another silo. Silos waste money. They force people to waste time. And given the capabilities that exist (we have the technology) they are unnecessary. But this isn’t just a technical problem. It is a strategic challenge.
Organizations need to know what data matters, how it should be captured, what standards should govern it, how systems should interoperate, and how data can support real decisions about learning, performance, readiness, and capability. This “need to know” is of strategic importance. It is something that impacts an organization’s bottom line. That is why our services span strategy, implementation, and sustainment.
Advisory: Start With the Right Architecture
Many organizations know they need better learning data, but they are not sure where to begin. Should they implement xAPI? Do they need xAPI Profiles? How does Total Learning Architecture apply to their environment? What learning data is really available through simulations? How should ethics shape learning analytics, especially in regard to artificial intelligence? And can Learning Engineering help move a project from concept to implementation?
Our Advisory services help answer those questions. Through consulting, workshops, briefings, and technical planning, we help organizations define learning data strategies that are realistic, standards-aligned, and built for scale.
Advisory engagements often focus on:
xAPI and xAPI Profiles
Total Learning Architecture
Learning Engineering
AI Ethics for Learning
Digital learning ecosystems
Data architecture
Standards alignment
AI-ready learning data
The goal is to help teams make better decisions before they commit resources, buy tools, or get too far ahead in the build of their systems.
Team Augmentation: Bring Learning Data Expertise Into the Build
Strategy matters, but modern learning data ecosystems do not become real until someone builds them. That work can be complicated. It may involve instrumenting an LMS, simulation, game, or application to generate high-quality xAPI data. It may require designing xAPI Profiles, integrating systems, deploying SQL LRS, filtering data for Total Learning Architecture use cases, or meeting DevSecOps requirements in secure environments.
Most teams do not have that expertise. So, Yet Analytics provides team augmentation services for organizations that need specialized engineering support. We work alongside your team to help move learning data projects from plan to production.
That can include:
SQL LRS implementation
TLA data filtering
xAPI Profile design
Learning activity data instrumentation
DevSecOps support
System integration
Data pipeline implementation
Secure deployment support
In other words, we help your team build the thing without making them become xAPI, TLA, and learning data infrastructure experts overnight. The result is not just shipping product faster, but shipping quality product informed by over a decade of specialized experience.
Technical Support: Sustain the System After Launch
Open source software is powerful because it gives organizations flexibility, control, and transparency. But production systems still need support. Because SQL LRS and related learning data infrastructure may sit at the center of important operational workflows, teams need help with implementation, configuration, upgrades, training, troubleshooting, service-level expectations, and long-term sustainment.
Yet Analytics provides custom technical support for our software products and the organizations that rely on them.
Support services may include:
SQL LRS phone/email/Slack support
Custom SLAs
Training
Software upgrades
Deployment guidance
Troubleshooting
Sustainment planning
This is especially important for organizations operating in enterprise, government, defense, healthcare, or other mission-critical environments where data integrity, uptime, security, and auditability matter.
Why This Matters
Every organization is thinking about AI. But AI does not magically fix bad data. In fact, more often than not, it can make things much worse. If learning data is inconsistent, incomplete, or semantically weak, AI systems will struggle to produce trustworthy outputs. There may be logs, but not meaning. Events, but not context. Activity, but not validated signals. And auditability is a real challenge when it comes to AI. How do you audit what you don’t have access to?
AI-ready learning data requires design. It requires standards and data instrumentation. Semantic structure is tablestakes for any modern learning environment producing activity data. To be AI-ready, you need systems that can transform raw activity into meaningful evidence. AI will not do this on its own. And are you going to trust some of your organization’s most sensitive people-facing functions to an AI that you can’t control?
Sure, you can set up a Model Context Protocol (MCP) to link to external resources and organizational documentation. But if that data and documentation is not optimized, what outcome do you expect? Setting up an MCP server is trivial. Stocking it with what is relevant to your business problems is a whole other story. And when it comes to learning outcomes and meaningful integrations, running an MCP query over a mountain of raw and poorly designed xAPI statements will not improve your prospects.
The real work is in developing an organizationally-meaningful data architecture, properly designed data profiles, and business-ready filtering and data logistics mechanisms. That is the work that we pioneered at Yet Analytics, and it is the work that we do every day for our clients and partners. We help organizations build the data infrastructure that makes advanced analytics and AI possible. We’re not interested in the hype. We’re focused on building operational capability.
The Future of Learning Data Is Interoperable
The next generation of learning systems will not be defined by one platform. It will be defined by ecosystems. This idea is finally beginning to see traction. Because there is no alternative. The big monolithic systems have shown their hand. And most users of these systems are, at best, dissatisfied. Dissatisfied by vendor lock-in. By the inability to access (or in some cases even own) their own data. And by the endless headache of customer service pipelines and upsell calls masquerading as engineering support.
Learning has never been bound to occurring just during training events or just on learning platforms. But for some reason, despite the variety of learning experiences we all partake in every day, whether for work or because we are trying to fix a faucet for the first time, the procurement of learning systems has been stuck in the dark ages. That’s changing. Because Learning will happen across tools, simulations, environments, and contexts. Data will need to move securely and meaningfully across those systems, whether they are purpose-built learning systems or not. And organizations will need to connect learning activity to competency, performance, readiness, and decision-making. That’s the future that we’re stepping into.
But that future requires interoperability. It requires open standards and software that gives organizations control over their data. And it requires teams that know how to design, build, and sustain learning data systems in the real world. Where learning data has business value. Where it is recognized as organizational intelligence.
That is what Yet Analytics does. We help organizations turn learning data into infrastructure, infrastructure into intelligence, and intelligence into better decisions. So, if your organization is ready to modernize its learning data ecosystem, implement SQL LRS, design xAPI Profiles, align with the Total Learning Architecture, or prepare learning data for AI, let’s talk. At minimum, it’s worth having a conversation to talk through where you are and where you are trying to go.
Right now, Yet Analytics is offering 20% off your first advisory engagement. See here, or click below for details and to claim you discount code.