Building Competent and Confident Empower™ Users

Empower™, Waters’ chromatography data system (CDS) for HPLC and UHPLC, isn’t an easy system to learn.

So when a new analyst starts at your company, the logical step is to start training them in Empower™ as quickly as possible. You’ll want to get them on a two-week intensive course, teach them the whole system in one go, and send them back to the bench to apply what they’ve learned. Right?

Wrong! I’ve watched this play out many times, and people rarely retain a whole system delivered in a single training block. If you send someone away for two weeks to learn everything, most people can perform the handful of tasks they’ve actually repeated in the lab, but not much else.

So if you’re looking at onboarding new Empower™ users, read on. I’ll explain why teaching less when onboarding new users might actually get you a productive analyst much sooner.

4 Steps to a More Reliable Analyst

Contrary to what you might think, the most reliable way to obtain a confident, independent analyst is not to teach them everything they need to know upfront. Instead, teach them less at the start, get them solid on the set of tasks they’ll do every day, and stage the advanced training later once they’re working in the lab.

This sounds like a slower option, but usually it isn’t. A new user who is properly grounded in daily routine analysis can start contributing to the team quickly, and everything you teach them afterward lands on a practical foundation instead of pure theory.

Here is the most efficient way to deliver your training to new Empower™ users:

Step 1. Ground them in the fundamentals

This is the stage labs most often bypass, because everyone is eager to get the new analyst onto real data. I see it constantly: someone arrives, and the first thing they’re shown is how to acquire data with no grounding in what Empower™ actually is.

This basic knowledge includes:

• The terminology (e.g., project, method set, sample set, channels, result sets)
• How data is structured and stored
• How the objects relate to one another
• How to navigate and search
• Who holds which roles and privileges

But this causes problems later when they hit a workflow they don’t understand because they never learned the fundamentals. So don’t skip them.

Step 2. Explain why GMP and data integrity are foundational

“Starting narrow” means building a small, solid base in the right order. In a regulated lab, that base has to include learning compliance foundations from the very beginning. It’s tempting to treat compliance as an advanced topic and defer it, but in a regulated laboratory, a new user is making decisions with data-integrity consequences almost immediately.

From early on, they need to understand data-integrity expectations, such as how audit trails and traceability work in Empower™ and what counts as a controlled method and an authorized change.

They also need to know:

• How manual integration is governed and justified
• The need for contemporaneous documentation
• How user roles and privileges constrain what they may do
• How to handle exceptions and escalate them.

Exactly which of these apply, and how deeply, depends on the analyst’s role and your local procedures.

Step 3. Describe the everyday workflow from acquisition through to reporting.

Once the vocabulary and compliance groundwork are there, you can proceed to teaching the complete workflow the analyst will actually run day-to-day, e.g., acquiring data, processing it, reviewing it, and reporting it. Keep this training anchored to what they’ll actually be doing daily.

Training in processing deserves particular care as it isn’t the first place an employee makes GMP-relevant decisions. Those decisions are spread across the workflow, including sample-set preparation, method selection, changes made during acquisition, data review, documentation, and how unexpected events are handled.

As processing is one of the broadest and most complex of those areas, it needs proportionate attention. You’re not trying to make someone an expert in every processing function on day one, but instead are making sure they understand their daily work well enough to gain experience.

Step 4. Use your lab’s own data to train them

If you train someone on a chromatogram that has nothing in common with their real samples, the skills they learn in theory don’t transfer to real life. So instead, build the first set of training on the methods, chromatograms, and situations they’ll meet at your bench.

It also helps to be clear about what training you’re deliberately leaving until later. The right-hand column in the table below describes what first prepares a new starter to perform routine work independently within the lab’s procedures and controls, and what you might consider leaving until later on.

When to Advance Training, and How to Test Competence

So when do you widen the scope? As a rough, experience-based guide, I’d often start extending into more advanced topics somewhere after they have been employed for six months to a year.

However, time is not a regulatory expectation, a universal benchmark, or a substitute for demonstrated competence. The real test depends on the analyst’s prior chromatography and CDS experience, the complexity and frequency of the assays, how standardized your workflows are, the range of deviations they’ve actually met, and your local qualification and authorization requirements.

For example, someone who arrives already fluent in another CDS and runs high-frequency routine assays may be ready far sooner than someone who is learning chromatography and Empower™ for the first time.

How to formally verify competence

Before you broaden their knowledge (and certainly before you let anyone work unsupervised!), it’s worth verifying competence formally. A new analyst is ready to work independently when they can:

• Complete routine acquisition, processing, review, and reporting without prompting
• Explain why they selected particular methods, parameters, or actions (not just memorize the steps)
• Recognize unexpected results, workflow deviations, or situations outside their authority
• Escalate appropriately rather than improvising a fix
• Follow local SOPs and data-integrity requirements as a matter of course
• Demonstrate correct use of audit trails, controlled methods, documentation and result review, where these apply to their role

Completing the training and being ready to work alone are not the same thing, so these simple checks should help you separate someone who is competent from someone who requires more experience.

Why staging tends to work

Staging training works because competence develops progressively. Teaching the fundamentals and compliance first makes daily lab tasks possible, and confident daily work gives advanced topics a practical foundation to build on.

There’s a consistency payoff too. A deliberate, staged program gives everyone the same core grounding, reducing the transfer of individual habits into the team, and ensuring everyone in your lab is on the same page.

If you are working through Empower™ workflow challenges or onboarding new analysts and want to discuss what structured competence building looks like in practice, LabWissen specializes in practical, GMP-compliant Empower™ training and consulting built around your lab’s own workflows and data.

Further reading

• Waters Corporation — Empower™ Chromatography Data System (product overview).
• Waters Corporation — Empower™ Tip #390: Defining roles of users (user types and privileges).
• Waters Corporation — Audit Trails in Empower™: System Audit Trails (Tip #368).
• Waters Corporation — Audit Trails in Empower™: Project Audit Trails (Tip #369).
• Waters Corporation — System Policies and Privileges for Processing Data in Empower™ Software with Data Integrity in Mind (white paper).
• US FDA — 21 CFR 211.25, Personnel qualifications.
• European Commission — EudraLex Volume 4, GMP Annex 11 (Computerized Systems).
• Cepeda, N. J., Pashler, H., Vul, E., Wixted, J. T., & Rohrer, D. (2006). Distributed practice in verbal recall tasks: A review and quantitative synthesis. Psychological Bulletin, 132(3), 354–380.