In her work as a Senior Data Engineer for VMG, the quality of the data being used is always top of mind for Jennifer Akers. That’s because Akers wants the end users — VMG members — to be able to make the best decisions possible from the work she does.
“Think about it like this,” she says, “if you put the wrong numbers into a calculator, the math will be wrong, and you will get the wrong answer.”
Indeed, most systems aren’t smart enough to fix bad inputs; the systems just process whatever they are given.
“We care about good data because accurate data going in means safer care, better decisions, and smoother operations.”
Bad Data Leads to Faulty Decisions
“Garbage in, garbage out” is an everyday phrase for data professionals. It basically means that if the data you put into your systems is inaccurate, incomplete, or inconsistent, then the results will be inaccurate and the decisions you want to make will also be inaccurate.
“Garbage in” can take many forms in the veterinary space, Akers says. Common examples include incomplete medical records where fields like weight or vaccination dates haven’t been updated, diagnoses entered as free-form text instead of standardized terms, or inconsistent entries. For example, one staff member may write “Canine Parvo,” another “Parvo,” and another “CPV” — these values must be standardized before any meaningful analysis.
“When you have garbage going in, you get problems like revenue reports that don’t match reality or inaccurate inventory numbers — these can affect your bottom line,” Akers says. Worst of all, faulty decisions can lead to wrong dosages, missed follow-ups, or overlooked vaccines.
Real-World Consequences of Poor Data
To illustrate, Akers describes a multi-doctor clinic relying on its practice management system to track preventive care, but inconsistencies start to creep in. Vaccines are logged in multiple ways — “Rabies,” “Rabies 1-yr,” “RV” — patient weights aren’t reliably updated, reminders aren’t consistently tracked, and some pets marked inactive are still active.
“These issues quietly degrade the quality of the data,” Akers explains. When leadership reviews dashboards, the numbers tell a troubling story: a 15% decline in preventive care compliance, lower rabies vaccine revenue, and poor reminder response rates. Acting on this, the practice reduces marketing, pressures doctors to shorten wellness visits, and delays hiring a needed technician.
In reality, preventive care is still being delivered — it’s just not being recorded consistently. Decisions based on faulty data create real consequences: rushed doctors, burned-out technicians, unhappy clients, and declining revenue.
Challenges in Maintaining Accurate Data
Clinics face several hurdles in maintaining high-quality data. “Clinics are fast-paced environments and data entry often becomes rushed or postponed, leading to skipped fields, defaults left unchanged, and items copied forward without review,” Akers says. Multiple roles may touch the same record, leading to differences in terminology or entries in the wrong place. Limited standardization in practice management systems can mean workarounds become the norm, and unclear data ownership creates blind spots until reporting breaks or decisions fail.
Predictive Analytics Depends on High-Quality Data
Data quality also drives predictive analytics. “If you want to forecast revenue, predict which patients or outcomes are higher risk, or even stay on top of your inventory — you need good quality data. Historical data accurately reflects reality and when that assumption breaks, predictions do too,” Akers explains. Inconsistent, missing, or incorrect data can teach models the wrong patterns, leading to inaccurate forecasts and misguided decisions.
Clean Data Supports Better Veterinary Care
Akers’ takeaway: clean, standardized data isn’t just a back-office concern — it’s the foundation for safe care, smarter business decisions, and reliable predictions.
