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In a world where data and analytics have become an integral part of business growth and success, Learning & Development (L&D) teams must leverage them to drive strategy development and tactical decisions to remain competitive. Whether the goal is to identify and support pain points in the learner journey, allocate resources efficiently, or improve learner experience, we see the benefits of these data-backed solutions.
The question then becomes how to use data and analytics to create better learning programs and improve learner success.
Data analytics can be broken down into four types: Descriptive, Diagnostic, Predictive, and Prescriptive. L&D teams can use each type separately to reach specific goals or together to create a full picture of data that can inform the decision-making process.
While descriptive analytics is leveraged to determine what happened in the past using historical and current data, diagnostic analytics is used to explore why it happened. In the case of predictive analytics, it can be employed to predict future outcomes of a program, or identify risks and opportunities using historical data combined with statistical modeling, machine learning (ML), and other data techniques. Lastly, prescriptive analytics parses through large amounts of data using ML algorithms to make data-informed recommendations.
As the simplest form of analytics, descriptive data is used to report on traffic and engagement, aggravate survey results, or track progress. However, it often happens too late in the design process – preventing assessment and evaluation from being proactive. Before even developing a solution, it’s important to determine which metrics and data should and can be collected, especially against organizational goals and KPIs. This approach would answer the question about the efficacy of learning programs and their impact on the business.
It is not only a process and design shift but also a mindset shift, which requires the understanding that to measure the return on investment (ROI) of learning programs, L&D teams and businesses need to know what they are measuring against.
Predictive and prescriptive analytics is the answer behind personalized and adaptive learning experiences that meet learners’ real-time needs. Adaptive learning technology uses artificial intelligence (AI) and ML techniques to monitor and assess learners’ current skills, knowledge, and progress. These inputs are then analyzed to provide relevant feedback, content, and tools needed to improve the experience. Simply put, technology has gone from a conduit for content delivery to one that continuously gathers data, identifies the needs, and provides individualized learning paths in real time.
Content is not the only thing that can be customized. Other elements such as interactions, communications, learning activities, assessments, or co-curricular activities can also be tailored to different needs. This technology is a more human-centric approach to learning design, as we actively respond to what our audience indicates they want, instead of giving them what we think they want.
Applications such as chatbots and virtual assistants use AI, natural language processing (NLP), and predictive analytics to answer questions, suggest relevant content, or even anticipate learner needs – simulating human conversation. Imagine a field worker needs an answer to a technical question during late night shifts. What’s better and easier than asking the chatbot? This virtual friend will give them the piece of information they need to carry on with the tasks.
Chatbot ability ranges from shallow dialogues covering multiple topics to deep knowledge about a well-scoped domain, depending on how it is configured and how much data is used to train it. The original chatbots – an interactive FAQ – were text-based and programmed to reply to a set of questions with answers pre-written by the developers. Over time, they have integrated more rules and NLP to enable more accurate interactions. The longer an AI chatbot has been in use, the stronger its responses become. This is due to the use of predictive analytics and the amount of data it collects from interactions with a human, which helps build a web of appropriate responses, including needs anticipation.
Leveraging data in L&D proves that valuing and building trust with key stakeholders often results in increased support for more effective solutions. Ultimately, data and analytics should be used to inform L&D teams about what learners need and how they need it. Not only will data and analytics improve learning experience, knowledge retention, and application on-the-job, but also intervene when needed to turn a rough learning path into a more engaging one in real time.
If you want to learn more about how to use data in your learning strategy, contact an Ardent expert today.