An overview of Spin’s data analysis process
It’s no exaggeration to say that algorithms guide a considerable part of our daily lives. Whether reading news or replying to an email, our experiences are shaped by the enormous volume of information we share (and consume) across diverse digital platforms.
We’re so entrenched in the Information Age it’s difficult to imagine our virtual environment without the existence of guidelines (millions of them, actually!) based on data analysis. This so-called “big data” helps us navigate content and identify what’s most relevant or urgent.
When it comes to the content we need in order to develop ourselves professionally and learn new skills, it’s no different. Are managers aware of (and concerned about) the amount and relevance of the content transmitted to their work teams?Questions like this led Spin – Learning Thru Data to develop the concept and practice of Analytics in Learning and Development (L&D), bringing change to the way companies select content an measure the results of their training programs.
L&D Analytics: a powerful filter that prioritizes the most important content
It’s easier than ever to produce and consume content on a large scale. While this is good in terms of knowledge sharing and access to information, it makes it hard to select and process the enormous amount of data to which we are exposed day after day. It’s no surprise that the term “infodemia” has been trending on content platforms and social networks, demonstrating a growing concern about the quality and quantity of news, opinions and other information that we consume online.
In the context of corporate training, this scenario becomes especially critical when associated with the pressure many leaders feel to deliver consistent results for organizations, not to mention managing a workforce showing signs of intellectual and emotional exhaustion.
By incessantly consuming, generating and sharing content with their work team, organizational leaders feed this vicious and diffuse cycle, failing to prioritize and select only the necessary (and intellectually processable) content for each individual.And here’s the first big differential of L&D analytics: not only is it a powerful and highly scalable process for filtering workforce training content, it’s also designed to select said content according each team member’s performance gaps, not their role.
L&D Analytics: Reinventing How Training Results Are Measured
A corporate training methodology that starts with the individual’s needs (and not their job title) to determine the right learning path radically changes the way we measure results. Based on these findings, people can then be grouped into circles of interest.
With data analysis and strict content selection based on key performance indicators (KPIs), the focus is on the individual. In fact, assessing whether KPIs change is more important than measuring the success of the training action itself.
Each organization must rely on its range of indicators to guide its strategic decisions (when to promote, fire or train an employee, for example) and this data is essential for L&D analytics to really happen.These indicators allow us to establish correlations between individuals and their performance gaps, mapping the tasks that affect these indicators and identifying the skills needed to perform the tasks. And so begins the “Learning Thru Data” journey, which here at Spin consists of four well-defined stages:
1. Establishing correlations: identifying the tasks that affect KPI’s and, of course, the skills needed to carry out those activities.
2. Individual analysis: based on these correlations, assess team performance and define which training sessions each person needs (or not!).
3. Optimized instructional actions: groups with equivalent needs organized in learning paths based on the company’s performance indicators.
4. The moment of truth: monitoring and evaluating the behavior of each group’s KPI’s.
Each of these steps will be approached in our next articles, when we will be able to detail the stages of this journey. Come with us!