Why data skills are at the cutting edge of learning
In a volatile market, data is what makes the world go round. Neglect these skills at your peril.
Learn, data, action: how to make your learning data actionable
How to get the most from your learning data.
Learning analytics in academia: the problems and pitfalls of collecting learner data
Learning analytics is useful, but proceed with caution.
Learning innovations: firing up your measurement capability
How can you ensure that your learners’ on-the-job behaviours have been improved by their training?
Why we may not be doing learning analytics
Learning analytics—using data to improve learning and development decisions—requires four key skills: domain knowledge, analytical techniques, technical tools, and communication abilities. Many professionals avoid it because they underestimate their existing capabilities and overestimate the technical complexity involved.
Why learning analytics may be challenging
Learning and development analytics uses data to improve L&D decisions and efficiency, following a four-step DAAR process. However, most organizations face challenges because their learning management systems lack robust analytics capabilities, making external tools like Excel necessary for effective data analysis.
Sales Training
Sales teams typically invest in certification and learning development to boost performance. Benchmark costs vary by industry and company size, but understanding average spending on annual certification and training per employee helps build effective business cases for sales development programs.
Measuring L&D impact: a realistic approach in a VUCA climate
In a VUCA business environment, measuring L&D impact is essential yet challenging. While the Kirkpatrick Model remains the gold standard for evaluation, many organizations struggle with implementation due to time, cost, and resource constraints, requiring a more realistic and practical approach to demonstrate training effectiveness.
Analytics Step 2 – Which data and from where
Discover how to identify the right data sources for analytics. Learn which data you need to collect and where to source it from using a real-world example of analyzing employee compliance training across an organization.
Analytics Step 1 – Defining the question
Learn the first step of the learning analytics process: clearly defining the question you want to answer with data. A Head of L&D example shows how to establish measurable objectives before collecting and analyzing training compliance data.
Start with the low hanging fruit for analytics
Learning and development teams can build quality analytics by starting with existing data sources like training attendance, completion rates, and feedback surveys. Focus on analyzing what you already have before pursuing sophisticated tools, and master basic reporting to prepare for more advanced opportunities.
Learning and Development Analytics
Learning and development analytics applies data analytics techniques to L&D to answer specific business questions. Start with a clear problem definition, identify necessary data sources, collect and analyze the data using tools like Excel, then communicate findings to support better L&D decisions.
Learning analytics: why we need synergistic skills
L&D professionals must develop synergistic skills combining traditional learning expertise with digital dexterity and data analytics to drive business outcomes. Effective learning analytics requires diverse team talent across multiple competency areas rather than expecting individual practitioners to master all capabilities.
How to develop a learning culture for young talent
Create a learning culture that develops young talent by helping early-career employees apply their strong technical and interpersonal skills to workplace contexts. Research shows new hires excel at digital skills and teamwork but need guidance on business communication, commercial awareness, and managing workplace relationships through extended induction and cultural acclimatization programs.
Learning analytics: how to be more strategic in your use of data
L&D professionals today have access to vast amounts of data but struggle to use it strategically. This article explores how to start with business questions rather than data collection, then select appropriate metrics and sources to measure training’s impact on organizational goals and employee performance.
Machine learning
Machine learning builds on programming fundamentals to enable computers to learn from data. Beginners with programming experience can start by learning Python basics, understanding core ML concepts like supervised and unsupervised learning, then practice with libraries like scikit-learn and TensorFlow using real datasets.
Deep Learning
Deep learning has become increasingly influential in applied machine learning, driving major advances in computer vision, natural language processing, and autonomous systems. Its ability to automatically learn complex patterns from large datasets has made it the preferred approach for many real-world applications.
Having learning impact, not just showing it
Learning functions should focus on creating conditions for actual impact rather than just measuring courses and completion rates. Research shows organizations struggle with outdated measurement strategies that ignore informal learning and fail to account for how development happens across the workplace.
Do we always need learning analytics – or is gut instinct a valid approach?
Learning analytics is essential for measuring L&D impact on performance, but not every organizational problem requires a learning solution. While data should inform major programs, experienced professionals can develop valid gut instincts for identifying true performance gaps versus other business barriers like poor management or faulty systems.
Let’s welcome a new dawn of behavioural learning analytics
Learning experience platforms now enable L&D teams to measure employee behavior throughout training journeys using advanced analytics, offering a solution to the industry’s long-standing struggle to prove learning’s business impact. Unlike traditional LMS systems that only tracked attendance and completion, these platforms capture detailed behavioral data through the Experience API standard, finally connecting training investment to measurable results.