Public health is only as strong as its data

Persistent myths about data quality undermine insight, action and trust 
Public health depends on data. Yet across the system, that data is not always complete, consistent or timely. Agencies frequently find themselves following up on missing details after the fact, introducing delays at the very moment speed matters most. 

Data arrives from hundreds of sources, including hospitals, laboratories and clinics, each operating under different rules and formats. What reaches public health agencies is often incomplete, delayed or inconsistent.  

For epidemiologists and public health leaders, data quality is not a technical detail but the foundation of effective public health practice. 
 
The myth of trustworthy sources 
One of the most persistent assumptions is that data from reputable sources must be accurate. In reality, every dataset carries limitations. Demographic fields may be incomplete. Patient identifiers may not match across systems. Reporting timelines can vary widely. 

A national analysis by Pew Charitable Trusts found that data reporting policies and practices vary widely across all 50 states, including how case, lab and surveillance data are submitted. 

Consider a foodborne illness investigation. Laboratory reports arrive from multiple hospital systems, each with slight differences in how patient information is recorded. Before epidemiologists can determine whether cases are linked, they must reconcile those inconsistencies. Time is lost. The window for early intervention narrows. 
 
Data quality is not just an IT problem 
It is tempting to treat data quality as something that can be solved with better systems. But even as public health infrastructure modernizes, gaps persist. A 2026 study found that many routinely updated surveillance datasets across the public health ecosystem experienced unexpected pauses or delays, interrupting visibility into critical areas such as vaccination tracking. These disruptions are not simply technical failures. They reflect the complexity of how data is defined, reported, and maintained across a decentralized system. 

Improving data quality requires more than infrastructure. It requires shared standards, clear governance and sustained coordination across program teams, data stewards, and leadership.  
 
Data quality is not a one-time fix
Data quality is not something that can be solved once and left alone. Reporting rules evolve. Surveillance requirements change. Staff turnover introduces variation in how standards are applied. 

A system may launch with standardized reporting, only to see inconsistencies reappear over time as new staff interpret guidelines differently. Without ongoing training and governance, quality degrades. 

Sustaining data quality requires continuous attention, not a one-time investment. 
 
Related: The science behind new precision in disease surveillance 
 
More data does not mean better insights 
There is a natural inclination to gather more data in pursuit of better analysis. In practice, more data often introduces more problems. Duplication increases. Conflicting records appear. Analysts spend more time cleaning data than using it. 

A chronic disease program may integrate multiple new datasets to expand visibility. Instead, it creates additional reconciliation work, delaying analysis and decision-making. 

The goal is not more data. It is better data that is fit for purpose. 
 
Modern systems do not guarantee perfect data 
Modernization is essential, but it is not a cure-all. Even the most advanced platforms cannot eliminate variability in human behavior, reporting practices, or local systems. 

A new surveillance platform may improve data exchange and integration. But if providers enter information differently or interpret reporting requirements inconsistently, data quality issues persist. 

The goal is not perfect data. It is data that is accurate, complete, timely, and usable for decision-making. 
 
Related: Transforming health equity analysis 
 
Turning better data into better decisions 
If the challenge is not a lack of data, but a lack of consistency and clarity, then the path forward is not simply adding more systems. It is building an environment where data can be trusted, understood, and used with confidence. 

This is where purpose-built platforms like Maven Public Health Solutions begin to make a measurable difference. Rather than treating data as a byproduct of systems, Maven is designed around the data itself. It supports the standardization, validation, and integration needed to ensure that information is not only collected, but usable. 

At a practical level, that means reducing the burden on teams who would otherwise spend hours reconciling discrepancies across sources. Data is aligned earlier in the process. The impact is immediate. Analysts spend less time cleaning data and more time interpreting it.  

None of this eliminates the need for human judgment. Data quality will always depend on how information is entered, interpreted and shared. But when the underlying systems are built to support accuracy, completeness, timeliness and interoperability, public health teams are better equipped to manage that complexity. 

Related: Public health needs a smarter path to optimization