How to improve marketing data usability

When anyone in your organization can open a report or AI tool and trust what comes back, your teams can make faster, more accurate decisions. That trust depends on how usable your data is. In this blog, I'll be covering what data usability is and the practical steps you can take to make your data easier for people and AI to use.

What is data usability?

Data usability describes how easily people can understand and use data. The idea borrows from product and interface design, where usability simply means ease of use. When it comes to your marketing data, usability relies on five key principles:

• Learnability: How easily can a first-time user accomplish basic tasks using your data?
• Efficiency: Once someone is familiar with the data, how quickly can they accomplish a task, like moving from a business question to a reliable answer?
• Memorability: How easily can users jump back into using the data after time away, like a stakeholder who pulls a report once a quarter?
• Errors: How often do people misread or misuse the data, and how significant are those mistakes?
• Satisfaction: How pleasant is it to work with the data day to day?

Why does data usability matter more as teams adopt AI?

Usability has always mattered for the people reading your reports, and it matters just as much now that AI tools are reading them too. An AI model can't ask a colleague what an obscure abbreviation means, so it depends on what your data and its documentation communicate. Clear event names, consistent parameters, and thorough documentation give AI tools the same head start they give a new marketer on your team, which means every improvement you make for human readability pays off for AI as well.

What are the warning signs that your data isn't usable?

Four red flags can signal that your data needs attention.

Red flag 1: Tracking that loses relevance over time

Many analytics setups include at least one event nobody can explain, with no record of who requested it or whether anyone still uses it. Business needs evolve, and your data collection should evolve with them instead of running on set-it-and-forget-it autopilot.

Red flag 2: Data that depends on deep business knowledge

When reports rely on internal abbreviations and team-specific lingo, only a handful of people can interpret them. If a new team member would need months of insider knowledge to answer a basic question, newcomers and AI tools alike are at a disadvantage.

Red flag 3: Implementation challenges

Workarounds and hacks you use to solve immediate tracking problems can break over time and introduce duplicate or conflicting data. Once your team has to caveat the numbers, trust in the data starts to fade.

Red flag 4: Complexity winning out over simplicity

When every event carries a long list of parameters and your teams don’t follow a clear naming convention, your data becomes difficult to learn and more prone to errors. And tracking everything “just in case” can make it harder to find the data you need.

How can you make data easier to learn and faster to use?

Learnability starts with reducing the mental effort your data demands, so step back and look at it the way a new employee would. A few practices can help:

• Pick a naming pattern for events and apply it everywhere: Noun-verb (content_click) and verb-noun (click_content) both work, as long as you stay consistent.
• Add parameters that answer real questions: Parameters like content_type and content_description tell users exactly what happened without outside context.
• Be strict about data governance: Clear rules for naming and approving new events keep your data readable as it grows.

Efficiency comes down to the details. Instead of asking how much information you can fit into one event in case someone asks for it later, ask what the least amount of information is that still answers the business question.

A report that needs one filter serves your team better than one that needs three. Reusing a standard set of parameters, such as action, type, name, and link_url, helps too, because one report can then answer many questions with a flip of a filter, and you don't have to reinvent the wheel every time.

How do you make data more memorable and less error-prone?

Memorable data saves people from having to look up what an event means every time they return to it. Four qualities make the difference:

• Clarity: Event and parameter names make sense at first glance.
• Conciseness: Names stay short and to the point without relying on abbreviations.
• Correctness: Each event and parameter tracks exactly what its name says.
• Consistency: Similar actions are always named and structured the same way.

When data meets those standards, people can comfortably reuse a parameter even when its context shifts slightly from one event to the next, as long as reports always show it alongside the event name.

You can't prevent every platform or developer error, but you can reduce human error. Spot potential failure points early by:

• Updating naming conventions to bring legacy events in line with your current standards.
• Using built-in platform dimensions instead of creating custom ones that duplicate them or share their names.
• Auditing your tag management and analytics setups regularly, and testing new and updated tracking so you can catch issues before they affect reporting.
• Providing clear documentation that gives everyone a single source of truth to check against.

How do you keep stakeholders satisfied with your data?

Simple, accessible data is the foundation of a satisfied team. To check whether you're hitting the mark, pay attention to how often reporting questions pull your team away from other work, and consider whether all your stakeholders would understand how you're tracking a given action. A trusted teammate or partner who's less familiar with your setup can also offer a fresh perspective.

Pro tip: Create a clear deprecation process that keeps everyone informed when functionality changes.

How to address your data usability red flags

Every setup is unique, but you can resolve each red flag by reducing, refining, and reusing what you already have. Here's how you can address each of your data usability issues:

Where should your team start?

Improving data usability requires upfront effort. Changing how you track things can make historical reporting more challenging, and your teams will need time to adjust.

That upfront effort pays for itself. Teams spend less time implementing and analyzing data and far less time fielding questions about what a report means. If your team queries raw data in a cloud data warehouse, leaner data also means less to scan, which can lower costs under usage-based pricing.

If addressing all your usability issues at once feels like too big of an undertaking, start small. Clean up confusing event names or retire tracking that no one uses. Once you prove initial success, it'll become easier to build support for bigger changes.

From there, ongoing education helps everyone see themselves as stewards of the data, and regular business reviews give you a chance to discuss how your reporting and tracking needs evolve.

Make your data work for everyone

Usable data gives your teams and AI tools a shared foundation they can trust. Our team helps organizations build durable, usable data foundations and uncover new opportunities for efficiency and accuracy. If you're ready to make your data easier for your whole team to use, we'd love to talk.