This is the first in a series of five blogs on achieving excellence in Training Administration and recognizing the pitfalls along the way.
Five ways that Training Administration can make or break your training:
- Part 1 – Data: Quality is everything
- Part 2 – Technology: How to leverage it for efficiencies
- Part 3 – Learner Engagement: Understanding individual’s Needs
- Part 4 – User Experience: It’s more than Training
- Part 5 – Designing the Process: Bringing the TA elements together
Part I: Data: Quality is everything
Today, especially in the workforce training space, data piles in every day.
The Learning Management System produces hundreds of reports for each employee. And that’s just the beginning of employee performance data. Every business organization gets flooded with information, so, from the outset, training practitioners must take the time to understand the data and how it connects to business and individual needs.
BE BOTH RELEVANT AND ACCURATE
The core of Training Administration is the data contained in the LMS. This information is the basis of every other process, and falling down at this stage has serious consequences. User data, such as job titles, contact information and approval chains needs to be up-to-date and easily extractable. You might think that it’s difficult to get it wrong at this basic level, however, we have found that we have had to work with many of our clients to clean up legacy and generic data. Once done, that led to better quality planning and improved results.
GETTING STAKEHOLDERS INVOLVED
Once we have the basics right, we need to think about stakeholders —department heads, managers and the finance department— and ask what is important to the business. This may include certification rates, compliance, training spend or getting right down to the specifics of individual completions and course progress. This is one of the ways we ensure we connect learning to business results.
And finally we cannot forget that the quality of data will affect the strength of planning and outcomes. Good data has certain characteristics. Is it accurate and complete, to start? We have to extract, from all of the data, information that leads to the right solutions for our stakeholders.
Stay tuned for Part II of this series