Brightspace Performance+ delivers the unified dashboards and integration capabilities that transform learning data into workforce intelligence.
See how organizations prove ROI and connect training to business outcomes.
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Corporate learning analytics has evolved from basic completion tracking to AI-powered business intelligence. Most L&D organizations aren’t keeping pace. According to Deloitte, 95% don’t excel at using data to align learning with business objectives and 69% lack the skills to link learning outcomes to business results. This gap leaves teams defending budgets with activity metrics while executives demand proof of impact on retention, productivity and revenue.
This guide maps the five-stage analytics maturity model that organizations follow from basic reporting to strategic business impact.
You’ll learn which capabilities unlock each stage, what platforms enable progression and what implementation roadmap gets you from tracking completions to proving ROI. Whether you’re stuck at engagement dashboards or building predictive models, this framework shows where you are today and what infrastructure you need to advance tomorrow.
Executives Demand Proof That Learning Drives Business Results
Here’s what we’re seeing across the L&D organizations we work with. Leadership wants numbers that tie training to retention, productivity and revenue. The challenge is that most teams aren’t equipped to deliver those numbers. According to Deloitte, 95% of L&D organizations don’t excel at using data to align learning with business objectives. Even more concerning, 69% lack the skills to ask the right questions that link learning to business results.
This gap leaves L&D teams defending budgets with completion rates and test scores. Metrics that show activity, not impact. Meanwhile, executives are asking whether training actually closes skill gaps, reduces turnover, or improves performance. Without the infrastructure to answer those questions, learning and development struggles to prove its value. The organizations we partner with understand that the shift from tracking what employees did to measuring what changed in the business requires a different foundation. One that moves beyond dashboards and connects directly to outcomes that matter to the C-suite. Understanding the ROI of employee training starts with building that foundation.
The Five Stages of Learning Analytics Maturity
Most organizations don’t leap from basic reporting to strategic impact overnight. They evolve through distinct stages, each building the capabilities required for the next level. Understanding where your organization sits on this maturity curve shapes your technology decisions, your talent strategy and your ability to prove that learning drives measurable business outcomes.
We’ve mapped this evolution into five clear stages:
- Stage 1: Reporting attendance without proving impact – tracking completions and test scores
- Stage 2: Spotting engagement trends that don’t connect to ROI – monitoring logins and session time
- Stage 3: Linking training to competencies and closing skill gaps – mapping learning to defined capabilities
- Stage 4: Predicting at-risk learners before they drop off – using AI to forecast dropout risk
- Stage 5: Proving ROI by connecting learning to workforce KPIs – correlating training with productivity and retention
Each stage represents a fundamental shift in how learning data is collected, interpreted and used to drive decisions.
Organizations at Stage 1 track completions.
Organizations at Stage 5 demonstrate measurable business impact.
The framework that follows provides a diagnostic tool to identify where you are today and what capabilities you need to build tomorrow.
Reporting Attendance Without Proving Impact
Stage 1 of 5: ● ○ ○ ○ ○
Are you at this stage?
Your reports show completion rates and test scores
You track time to competency but not performance change
Leadership asks about business impact and you show activity metrics
You can prove training was delivered but not that it delivered results
Most organizations start here. They track completions, test scores and time to competency through systems like Brightspace core reporting. The reports document who finished what and when. Leadership reviews the numbers quarterly.
The limitation: Activity metrics don’t prove behavior change.
A 95% completion rate confirms people clicked through modules. It doesn’t confirm they retained information, applied it on the job, or improved performance. When budget discussions arrive, L&D presents participation metrics while executives ask about business impact. Basic reporting systems document what happened in the LMS reporting system, not what happened in the business.
| What this looks like in practice: SNHU initially faced this challenge with 90,000+ students. Their legacy system tracked completions but couldn’t connect that data to student success outcomes. Advisors knew students finished courses but couldn’t identify who was at risk of dropping out or why performance was declining. |
Stage 2 adds engagement tracking to spot participation trends →
Spotting Engagement Trends That Don’t Connect to ROI
Stage 2 of 5: ○ ● ○ ○ ○
Are you at this stage?
Your dashboards track logins, session time and content views
You can identify who’s active but not who’s improving
Your reports show participation trends across cohorts
Executives ask how engagement translates to performance and you pivot to activity metrics
Organizations at this stage have upgraded from basic completion tracking to engagement monitoring. Platforms like the Brightspace Insights Engagement Dashboard reveal participation patterns. L&D teams can identify which cohorts are logging in frequently, which content gets the most views and where session time drops off.
The limitation: Participation metrics don’t prove capability development.
High engagement looks impressive in quarterly reviews. Strong login rates and content interaction suggest employees are investing time in learning. The problem surfaces when leadership asks the follow-up question: what changed as a result? Engagement data documents behavior inside the platform. It doesn’t document whether that behavior translated into skill acquisition, job performance improvement, or business outcomes. Organizations at this stage can prove learning is happening. They can’t yet prove learning is working.
| What this looks like in practice: A mid-market financial services firm tracks completion rates and session time across its compliance training programs. The dashboard shows 87% of employees logged in and spent an average of 45 minutes per module. Leadership approved the budget renewal based on these participation numbers. Six months later, an audit revealed the same compliance errors persisting across branches. High engagement didn’t correlate with behavior change on the job. |
Stage 3 maps training to defined competencies and certifications →
Linking Training to Competencies and Closing Skill Gaps
Stage 3 of 5: ○ ○ ● ○ ○
Are you at this stage?
You map training programs to defined competencies and certifications
Your system tracks skill development against organizational capability frameworks
You can identify skill gaps at the individual and team level
Leadership asks how training translates to workforce capabilities and you show competency attainment rates
Organizations at this stage have moved beyond activity and engagement metrics to outcome measurement. They connect training directly to defined competencies, certifications and role-based skill requirements. Platforms like Brightspace Learning Outcomes workflows and the Assessment Quality Dashboard enable this shift. L&D teams can now demonstrate that employees didn’t just complete a course on financial regulation. They achieved measurable competency in interpreting compliance requirements.
The limitation: Competency attainment doesn’t predict performance trajectory.
Tracking skill development represents meaningful progress. Organizations can finally prove that training builds specific capabilities. The challenge surfaces when trying to use this data proactively. Competency tracking is retrospective. It confirms what employees have learned, not what they need next or who might struggle before they do. Leadership wants predictive insight. Which employees will succeed in new roles? Who needs intervention before performance declines? Stage 3 systems document capability gaps after they appear. They don’t forecast them before they impact the business.
| What this looks like in practice: A healthcare system implements competency-based training for its clinical staff, mapping each module to specific patient care protocols. The LMS tracks which nurses have achieved certification in each protocol. When an audit reveals medication errors persist in certain units, leadership discovers that competency completion alone doesn’t correlate with on-floor performance. The system proves nurses completed training. It can’t identify which nurses needed additional support or different learning approaches before errors occurred. |
Stage 4 uses AI to predict dropout risk and recommend interventions →
Predicting At-Risk Learners Before They Drop Off
Stage 4 of 5: ○ ○ ○ ● ○
Are you at this stage?
Your platform uses AI learning platforms to forecast which learners are likely to disengage or fail
You receive automated alerts when specific risk thresholds are triggered
Your system recommends targeted interventions based on learner behavior patterns
You deploy predictive models but struggle to connect them to workforce performance outcomes
Organizations at this stage have moved from retrospective reporting to predictive learning analytics. Platforms leverage AI and xAPI learning data to analyze engagement patterns, assessment performance and behavioral signals. Systems like D2L Lumi and Intelligent Agents forecast dropout risk before it materializes. L&D teams receive automated alerts when learners cross predefined risk thresholds, enabling proactive outreach rather than reactive damage control.
The limitation: Predicting learning outcomes doesn’t predict business impact.
Forecasting which employees will struggle with a compliance module represents meaningful progress. The challenge surfaces when trying to connect that prediction to organizational priorities. Predictive analytics identifies learners at risk of failing training. It doesn’t identify which failures will create operational risk, compliance exposure, or performance gaps that actually threaten business outcomes. Leadership wants to know which skill deficiencies will impact next quarter’s product launch or create regulatory liability. Stage 4 systems predict training completion. They don’t predict workforce capability gaps that drive strategic risk.
| What this looks like in practice: Southern New Hampshire University deployed Brightspace’s predictive capabilities to support 90,000+ students and 450+ advisors. The system aggregates data from their LMS, Student Information System and CRM to categorize risk levels as low, medium, or high. When a student crosses into high-risk territory, advisors receive automated alerts. The platform surfaces rubric-level data showing whether students struggle with content understanding or writing mechanics, enabling advisors to tailor interventions. According to Matthew Thornton, Associate Vice President of Student Technology Experience: “With data drawn from Brightspace, we can intervene with an at-risk student even before they’re aware they’re at risk.” |
Stage 5 correlates training data with workforce KPIs to prove ROI →
Proving ROI by Connecting Learning to Workforce KPIs
Stage 5 of 5: ○ ○ ○ ○ ●
Are you at this stage?
Your analytics link training data directly to workforce performance metrics
You correlate learning outcomes with productivity, retention and revenue
Your dashboards integrate data from LMS, HRIS and CRM systems
Leadership views learning analytics as a strategic capability, not an operational report
Organizations at this stage have achieved the integration that enables true ROI measurement. Learning data flows into a unified system alongside workforce performance data, creating the correlation analysis required to prove business impact. Platforms like Brightspace Performance+ and HRIS/CRM integrations make this possible.
L&D teams can demonstrate that employees who completed specific training programs showed measurable improvement in productivity metrics, reduced turnover rates, or accelerated time to full competency in new roles.
The advantage: Strategic analytics position L&D as a growth driver.
Mature organizations create what Deloitte identifies as a single source of truth that integrates learning and business data to contextualize skills development with performance metrics. This infrastructure enables L&D to participate in executive conversations about workforce strategy, not just training delivery.
When leadership asks whether the sales enablement program improved deal velocity, the answer is data-driven and definitive. When the board questions whether upskilling reduced contractor dependency, the analysis is already complete.
| What this looks like in practice: A retail organization integrated its Brightspace platform with Workday to track the correlation between customer service training completion and Net Promoter Score improvements across 200+ locations. The unified dashboard revealed that stores where managers completed the advanced coaching module saw 18-point NPS gains within 90 days, compared to 4-point gains in control locations. Armed with this data, L&D secured executive approval to scale the program company-wide and shift budget from external coaching consultants to internal capability building. |
Organizations at Stage 5 use analytics to drive strategic workforce decisions.
Building the Infrastructure for Data-Driven Learning
Moving from activity tracking to business impact requires building specific technical and organizational capabilities. Organizations successfully making this transition build four foundational capabilities in sequence. Each unlocks the next level of analytical maturity.
Capability 1: Establishing Unified Data Capture Across Learning Touchpoints
Stage unlocked: 2 of 5 (Engagement tracking)
| What it enables: Visibility into dropout patterns and content effectiveness Key D2L product: Brightspace with xAPI integration Time to implement: 2-4 months ROI indicator: Ability to identify engagement gaps before they become completion failures |
Organizations must aggregate learning data from every touchpoint where employees engage with development content. This includes the core LMS, third-party content libraries, virtual classroom platforms and offline training sessions. Without this unified view, analytics remain fragmented and incomplete.
Implementation in Brightspace:
Brightspace captures granular interaction data through xAPI (Experience API) standards, which track learning activities beyond traditional course completions. When an employee watches a compliance video in a third-party library, completes a simulation in an external tool, or participates in a live virtual session, Brightspace logs that activity and correlates it with their learner profile.
For organizations with multiple learning systems, Brightspace integrates with external Learning Record Stores (LRS) to create a single repository of all learning interactions. This technical foundation enables the Engagement Dashboard in Brightspace Insights, which tracks login frequency, session duration, content interaction patterns and drop-off points.
Implementation priority: Deploy first in high-stakes programs where engagement gaps create measurable risk. Compliance training, onboarding and certification programs generate the clearest signal.
Capability 2: Connecting Learning Outcomes to Defined Competencies
Stage unlocked: 3 of 5 (Competency tracking)
| What it enables: Measurable skill attainment tied to business capabilities Key D2L product: Brightspace Learning Outcomes workflows + Assessment Quality Dashboard Time to implement: 3-6 months ROI indicator: Ability to prove specific skill development, not just course completion |
Organizations must move from measuring course completions to measuring competency attainment. This requires mapping every training program to specific, measurable skills and building assessment workflows that rigorously evaluate whether employees have achieved those competencies.
Implementation in Brightspace:
Brightspace Learning Outcomes enable L&D teams to define outcomes that align to business goals and leverage intuitive workflows to align outcomes across courses and course content. When an employee completes a financial regulation course, the system doesn’t just record a completion. It documents achievement against defined competencies like “interpreting FINRA Rule 2210” or “identifying prohibited marketing practices.”
The Assessment Quality Dashboard measures the quality of assessment activities such as quizzes, assignments and discussions across Brightspace. It analyzes assessment data to identify questions with poor discrimination or assessments that don’t align with stated learning outcomes.
This infrastructure enables skills gap analysis. Leaders can identify which competencies exist across the workforce, which teams have capability deficits and where training investments should concentrate.
Implementation priority: Focus on competencies directly tied to business outcomes. Map sales enablement programs to deal velocity competencies. Map customer service training to NPS-driving behaviors.
Capability 3: Deploying Automated Interventions Based on Learner Activity
Stage unlocked: 4 of 5 (Proactive engagement)
| What it enables: Proactive outreach before learners disengage or fail Key D2L product: Intelligent Agents + Release Conditions Time to implement: 4-8 months ROI indicator: Reduction in dropout rates and faster time to intervention |
According to research on AI in learning analytics, generative AI can transform dashboards from static displays into dynamic, conversational systems. Organizations implementing Stage 4 maturity leverage automation capabilities to move from retrospective reporting to proactive intervention.
Implementation in Brightspace:
Intelligent Agents in Brightspace personalize learning with automated learner communication and other workflows based on learner activity. When specific conditions are met (such as a learner who hasn’t logged in for seven days, or whose grade drops below a threshold), the system triggers targeted interventions without requiring manual monitoring.
Release Conditions work in tandem to personalize learning with automatic course content release based on timing, achievements, or milestones. This combination enables adaptive learning paths that respond to individual learner progress.
| What this looks like in practice: Southern New Hampshire University demonstrates this capability at scale with 90,000+ students and 450+ advisors. By aggregating data from Brightspace, their Student Information System and CRM, SNHU categorizes risk levels as low, medium, or high. When students cross into high-risk territory, advisors receive automated alerts with rubric-level data showing exactly where the struggle exists. |
Implementation priority: Deploy automation first in programs where dropout creates operational or compliance risk. New hire onboarding, leadership development cohorts and mandatory certification programs generate immediate ROI.
Capability 4: Integrating Learning Data With Workforce Performance Systems
Stage unlocked: 5 of 5 (Strategic business impact)
| What it enables: Correlation of training with productivity, retention and revenue metrics Key D2L product: Brightspace IPSIS integration framework + Brightspace API Time to implement: 6-12 months ROI indicator: Proven link between specific training programs and workforce KPIs |
Proving ROI requires correlating training data with business outcomes. This final infrastructure component connects the LMS with HRIS platforms, CRM systems and performance management tools to create what Deloitte identifies as a single source of truth that integrates learning and business data to contextualize skills development with performance metrics.
Implementation in Brightspace:
Brightspace offers the IPSIS (Integration Pack for SIS) framework for integrating with Student Information Systems, HR systems and other enterprise platforms. The platform provides a RESTful API with OAuth 2.0 security for custom integrations and extensions. Event-driven integrations can be implemented using the D2L Link service for workflow automation.
The technical architecture uses APIs and pre-built connectors to sync data bidirectionally. When an employee completes a sales methodology course in Brightspace, that competency achievement can flow into your HRIS and update their skills profile. When the same employee closes a major deal in your CRM, that performance data can flow back into analytics dashboards, enabling L&D to measure whether training completion correlates with deal velocity improvements.
This infrastructure positions business LMS capabilities as strategic workforce intelligence rather than administrative reporting.
Implementation priority: Start with one high-visibility program where the business outcome is clearly defined and measurable. Proving the correlation between training and a specific KPI builds executive confidence.
Your Corporate Learning Analytics Implementation Roadmap
Organizations advancing through analytics maturity build these capabilities sequentially. Use this checklist to plan your progression:
Phase 1 (Months 1-4):
- Establish unified data capture across all learning touchpoints
- Implement xAPI standards and LRS integration
- Deploy Engagement Dashboard for high-stakes programs
Phase 2 (Months 5-10):
- Map training programs to defined competency frameworks
- Implement Learning Outcomes workflows in Brightspace
- Deploy Assessment Quality Dashboard to validate skill measurement
Phase 3 (Months 11-18):
- Configure Intelligent Agents for automated interventions
- Implement Release Conditions for adaptive learning paths
- Train advisors and managers on using engagement data for proactive support
Phase 4 (Months 19-30):
- Integrate Brightspace with HRIS, CRM and performance systems via IPSIS
- Deploy custom dashboards using Brightspace API for correlation analysis
- Establish executive reporting cadence on learning-to-business impact
Brightspace Performance+ delivers the unified dashboards and integration capabilities that transform learning data into workforce intelligence.
See how organizations prove ROI and connect training to business outcomes.
Frequently Asked Questions About Corporate Learning Analytics
What Is Corporate Learning Analytics?
Corporate learning analytics is the systematic collection, measurement and analysis of data about learners and their contexts to understand and optimize learning outcomes and the environments in which they occur.
This extends beyond basic LMS reporting to encompass engagement patterns, competency development, predictive risk modeling and correlation with workforce performance metrics. The practice enables L&D organizations to move from intuition-based decisions to evidence-based strategy, proving the business impact of training investments.
How do You Measure Training Effectiveness with Analytics?
Training effectiveness measurement requires moving beyond completion rates to outcomes-based metrics. Organizations measure effectiveness by tracking competency attainment against defined skill frameworks, monitoring behavior change on the job through manager assessments or performance data and correlating training completion with business KPIs such as productivity, retention, or time to full competency.
Advanced measurement integrates learning data with HRIS and CRM systems to demonstrate statistical correlation between specific training programs and workforce outcomes. The Kirkpatrick evaluation model provides a framework for measuring across four levels: reaction, learning, behavior and results.
Which Learning KPIs Matter Most for Business Impact?
The most impactful learning KPIs connect training activity to organizational outcomes. Leading indicators include time to competency (how quickly employees reach full productivity), skills gap closure rate (percentage of critical capability deficits addressed) and engagement velocity (pace of content interaction).
Lagging indicators demonstrate business value: correlation between training completion and performance improvement, retention rates of trained versus untrained cohorts and productivity gains measured through output per employee. According to Deloitte, mature organizations segment analytics into six categories—Alignment, Impact, Effectiveness, Engagement, Operations and Distribution—to capture both business and learner outcomes comprehensively.
How do Predictive Analytics Improve Corporate Training?
Predictive analytics apply machine learning algorithms to historical learning data to forecast future outcomes before they occur. Systems analyze engagement patterns, assessment performance and behavioral signals to identify learners at risk of failing or disengaging, enabling proactive intervention rather than reactive remediation. Organizations use predictive models to optimize resource allocation by identifying which employees will benefit most from specific training investments, which programs generate the highest ROI and which instructional approaches work best for different learner cohorts.
Research on AI in learning analytics demonstrates that large language models can deliver comprehensive feedback superior to human educators in some contexts, expanding the scope of what predictive systems can achieve.
What Is the Role of an LRS in Learning Analytics?
A Learning Record Store (LRS) serves as a centralized repository for learning activity data captured through xAPI (Experience API) standards. Unlike traditional LMS databases that only track activities within the platform, an LRS aggregates learning interactions from multiple sources—third-party content libraries, virtual classrooms, simulation tools and offline training sessions. This creates a comprehensive, unified view of all learning experiences across the enterprise.
The LRS enables advanced analytics by providing granular, statement-level data about what learners actually did, not just whether they completed a course. Organizations use LRS data to analyze learning paths, identify patterns of successful skill development and correlate specific learning activities with performance outcomes.
How Does AI Enhance Learning Analytics In 2026?
AI enhances learning analytics through three primary capabilities.
First, generative AI transforms dashboards from static displays into dynamic, conversational systems that explain and contextualize data, improving user understanding and actionability.
Second, machine learning models process hundreds of variables to identify patterns invisible to human analysis, forecasting which learners will struggle and recommending targeted interventions.
Third, AI enables personalization at scale by analyzing learner-level data (skills, experiences, assessment scores) to deliver adaptive, customized development in real time. Natural language processing allows systems to analyze unstructured data—discussion posts, written assignments, support tickets—extracting insights about learner comprehension and engagement that structured data alone cannot reveal.
What are Common Barriers to Using Learning Analytics?
According to Deloitte research, 95% of L&D organizations do not excel at using data to align learning with business objectives and 69% lack the skills to ask the right questions linking learning to business results. The primary barriers are structural. Data silos prevent integration between LMS, HRIS, CRM and performance management systems, fragmenting the view of learning impact. Analytics literacy gaps mean teams can generate reports but cannot translate data into strategic action.
Weak governance creates inconsistent data quality and undefined ownership of analytics responsibilities. Legacy technology infrastructure lacks the API connectivity required for modern data integration. Organizations often possess powerful analytics tools but lack the organizational muscle—skills, processes and executive sponsorship—to leverage them effectively.
How do You Align Learning Metrics with Business Goals?
Aligning learning metrics with business goals requires starting with the business outcome and working backward to the learning intervention. L&D leaders identify specific organizational objective, reducing time to productivity for new sales hires, decreasing compliance violations, improving customer satisfaction scores, and define which capabilities drive those outcomes.
Training programs are then mapped to those capabilities and metrics are established to measure both skill development and business impact. This creates a clear chain of evidence: training completion → competency attainment → behavior change → business result.
Mature organizations create what Deloitte describes as a single source of truth that integrates learning and business data, enabling correlation analysis that proves causation rather than merely suggesting it. Regular executive reporting translates learning metrics into business language that C-suite leaders understand and value.
What Is the Difference Between Reporting and Learning Analytics?
Reporting documents what happened in the past through static dashboards showing completion rates, test scores and login frequency. Analytics interprets what the data means and predicts what will happen next, using statistical methods to identify patterns, test hypotheses and forecast outcomes.
Reporting answers “how many employees completed the training?” Analytics answers “did the training improve performance, which employees are at risk of failing and where should we invest next?”
Reporting is descriptive and retrospective.
Analytics is diagnostic, predictive and prescriptive.
Organizations at Stage 1 of analytics maturity rely on reporting. Organizations at Stage 5 use analytics to drive strategic workforce decisions.
The shift requires moving from data presentation to data interpretation, building analytical capabilities alongside technological infrastructure.
How Can HRIS and LMS Integrations Strengthen Analytics?
HRIS and LMS integrations create the data foundation required to prove training ROI by connecting learning activity with workforce outcomes. When these systems sync bidirectionally, competency achievements in the LMS automatically update employee skills profiles in the HRIS and performance data from the HRIS flows back into learning analytics dashboards.
This enables correlation analysis that answers critical business questions: Do employees who complete leadership training receive higher performance ratings? Does product certification reduce time to quota attainment for sales representatives? Do onboarding programs that include specific modules reduce 90-day turnover? Organizations use integration frameworks like Brightspace IPSIS or custom API connections to create this unified data architecture. The resulting analytics position L&D as a strategic function that demonstrably contributes to talent development, retention and organizational performance.