For today’s veterinary practice owners, data is one of the most powerful tools for driving smarter decisions — from tracking revenue and inventory to measuring patient outcomes and team performance.
But as Jennifer Akers, Senior Data Engineer for VMG, puts it, success starts by following a simple mantra: “Garbage in, garbage out.”
“In practice, this means deciding what data matters and how it should be entered/collected,” Akers said. “This ties back to having shared standards that everyone should know and follow. This might sound simple, but it’s the foundation everything else sits on. Consider this, could you accurately track your personal finances with a data point saying ‘around $500’ for groceries?”
Tools and workflows
When it comes to keeping data clean and reliable, Akers emphasizes that veterinary practices don’t need expensive analytics platforms to make meaningful progress. Often, simple system settings and thoughtful workflows can dramatically improve accuracy.
One of her strongest recommendations is to use structured fields wherever possible. Dropdown menus and standardized codes reduce variability and prevent common errors that come with free-text entry. Something as simple as selecting a state from a dropdown instead of typing it can eliminate countless spelling inconsistencies.
Making critical fields required and setting basic guardrails — such as reasonable age limits — can also prevent missing or clearly incorrect data from entering the system in the first place.
Of course, clean data must coexist with the fast pace of clinical practice. Akers suggests distributing documentation responsibilities across the team: front desk staff can collect client demographics, technicians can record weights and vitals, and doctors can enter diagnoses and treatments. Clear templates and written documentation procedures help ensure everyone understands expectations.
She also recommends conducting small, monthly spot checks of a handful of records to identify patterns of missing or inconsistent data.
“Once you spot the problems, you can work towards preventing them,” she said.
The human factor
Staff training and engagement are critical to maintaining strong data quality in veterinary practices. In many hospitals, team members unintentionally become “de facto” data stewards simply because they are the ones entering information throughout the day. Since it’s rarely practical for one person to own all data oversight, everyone must understand and follow clear workflows. As Akers explains, if staff members don’t know the process or why it matters, they can’t be expected to protect data quality.
Ongoing training ensures consistency, but it also opens the door for valuable feedback. Team members often know exactly which fields are confusing, which steps take too long, and where errors commonly occur — insight that can help refine systems and improve accuracy.
Culturally, the most successful practices stop treating data as a purely technical task and instead frame it as a tool that supports better care and smoother operations. When teams see that accurate weights prevent dosing errors, complete histories reduce missed follow-ups, and reliable records lower stress and rework, data becomes a clinical responsibility rather than clerical burden.
“Keep in mind that leadership must model good data practices as well,” Akers says. “Nothing stagnates a data initiative faster than leaders who ignore the standards.”
The “quiet multiplier” for your practice
Clean data may not be flashy, but as Jennifer Akers describes it, it’s a “quiet multiplier.” “When it’s good, everything else works better; when it’s bad, even great people and systems struggle,” Akers said. “When data is accurate and consistent, clinicians can deliver safer and more proactive care, have access to complete medical histories, know when to issue follow-ups, while leadership can rely on trustworthy insights to make confident decisions about staffing, services, and growth.”
Quality data also reduces day-to-day friction. It minimizes miscommunication, prevents rework, strengthens client trust, and supports sustainable scaling as a practice grows. In that sense, good medicine and good business share the same foundation: high-quality information that enables better outcomes and stronger relationships.
For veterinary leaders, the key is how they communicate this message. Data quality should never feel like extra “busy work.” Instead, it should be tied directly to what teams already care about—patient safety, smoother workflows, and reduced stress. Focusing on a small set of high-impact data points, clearly defining who enters what information and when, and modeling strong data habits at the leadership level reinforces that this is a shared responsibility.
“When progress is acknowledged and data quality is positioned as something that reduces stress and supports better care and overall business growth, it becomes a practical tool the team embraces rather than an added burden.”
