The training function can lead the organization in adaptability
The baselines for normal business continuance have been shattered and there is no indication they will return. This is a critical change in how enterprise considers risk management, and in fact, throws the whole methodology on its head: without a baseline of normal an approach that tries to reduce risk to ensure that baseline falls apart.
We have entered an era of business capability over business continuity. Prediction has been replaced with preparation, and risk mitigation is better served with resilience-building.
The training function sits in a particularly important juxtaposition of business, allowing training to guide the entire organization through turbulent waters. The importance of nimble vILT programs during the early days of the pandemic are just one example. The continuing struggles with identifying and shoring up skill gaps in the wake of the Great Resignation another.
Scalable training functions can flex and adapt to meet these demands, keeping the business moving forward. This is the type of resilience that escapes the risk mitigation mindset.This scalability comes from a combination of a team’s methodology and their learning technology.
The methodology that must be deployed focuses on mastering learning analytics to increase the training team’s data literacy and lay the foundation for a powerful data model that reveals information about training and how it impacts the organization. Once a team has charted a strategic path to mastering learning analytics, it is time to focus on how your learning technology can bring this vision to life.
The Components of Scalable Learning Tech
There are five components that define the scale-readiness of learning technology. These components work holistically, and can not be considered as separate, individual components. They are part of an infrastructure that facilitates scalable training operations.
Data Fluency
This measures your team’s ability to gather and understand training data wherever it may be. Teams that are data fluent are resilient because:
- They empower other teams with training insights.
- They can address business OKRs and goals with predictive analytics.
- They understand where their data is located, and why it is important.
Data Quality
A team that is fluent in data utilization is only as good as the quality of their data. This one isn’t too difficult to grasp, teams with high quality data are resilient because:
- They are able to make accurate, repeatable decisions.
- The entire organization benefits from a reliable data model.
Resource Optimization
How well can learning technology manage resources? This includes automating repetitive tasks, optimizing training resources, and effectively turning training into a lean function vs a cost center. Teams that have mastered resource optimization are resilient because:
- They can easily adapt training resources to meet demands.
- They can report on how training resources are best used across the organization to make maximum impact.
- They reduce time spent on workflows and repetitive tasks, freeing up human capital to focus on strategy and insights.
Agility and Connectivity
Agility and connectivity refer to learning tech’s ability to connect with other critical business systems. Teams that have well-connected learning tech are resilient because:
- Data and insights flow along two-way connectors between core business functions.
- A 360 degree view of training is possible.
- Workflows can be implemented across the organization to meet demand.
Business Intelligence
This refers to learning technology’s ability to turn all of this data into actionable insights. A team with high business intelligence can explain why training performance resulted in certain outcomes. These teams are resilient because:
- They can show their impact, and bolster more support from stakeholders to advance programs.
- Showing why training impacts outcomes can radically inform strategy across the organization.
- Leverage data-driven decision making to arrive at accurate conclusions that do not have to assume a baseline “normal”.
