The Business Case for Data Quality in Healthcare
In healthcare organizations, data quality is often viewed as a technical concern rather than a strategic imperative. This perspective creates a significant blind spot—one that can impact everything from operational efficiency to strategic decision-making. For business leaders, understanding the concrete business impact of data quality is essential for prioritizing investments that will yield the greatest returns.
When healthcare organizations evaluate their strategic initiatives, data quality rarely tops the list of priorities. However, as Viktor Lazarevich, CTO and co-founder of Digiteum, explains, data quality directly impacts an organization’s ability to execute its strategy:
“If your roadblocks are unclean data, unstructured data, or missing data, these prevent implementing specific strategies,” Lazarevich explains. “But with clean, well-organized, structured data, you face fewer obstacles.”
With over 20 years of experience helping organizations implement digital technologies and build effective data strategies, Lazarevich has observed how data quality challenges can derail even the most promising initiatives.
The business impact of poor data quality can be measured in concrete terms—specifically, in the effort required to implement strategic initiatives.
“If your dataset is already good enough, the effort is zero. With slightly lower quality data that just needs simple processing, it’s low effort and relatively easy. But if you need tons of manual work like scanning hundreds of documents, the effort is tremendous,” notes Lazarevich.
This perspective shifts the conversation from seeing data quality as an overhead cost to recognizing it as an investment that yields returns through faster, easier implementation of strategic initiatives.
Consider the implications for different types of healthcare organizations:
- For hospitals: Poor data quality might require manual chart reviews before quality reporting, adding weeks to the process and pulling clinicians away from patient care
- For research institutions: Inconsistent data structures across study sites could necessitate months of harmonization before meaningful analysis can begin
- For pharmaceutical companies: Data quality issues might delay clinical trial analysis, potentially costing millions in additional expenses and delayed market entry
- For insurers: Inaccurate or incomplete claims data could lead to payment errors, increasing administrative costs and damaging provider relationships
In each case, the business impact extends far beyond the IT department, affecting core operations, strategic initiatives, and ultimately, competitive position.
The dramatic efficiency improvements possible with high-quality data can transform healthcare operations. Lazarevich shares a compelling example:
“We worked with an organization where planning and payroll modules were all managed in Excel files shared between different people. It took about five days just to calculate payroll for a two-week period. We built a smart system with integrated business rules that reduced payroll processing from five days to just 15–30 minutes.”
Similar transformations are possible in healthcare contexts, where proper data structure can enable automation of routine analysis, identification of patterns across large datasets, and more efficient resource allocation.
In many healthcare organizations, highly trained professionals spend significant time on manual data processing—time that could be better spent on analysis and decision-making.
“A lot of processes, even nowadays in the healthcare industry, are manual. There is someone who goes through document after document, trying to find some pattern. AI can do it in a minute. A person needs to do it in a couple of months,” explains Lazarevich.
The business impact of this inefficiency is substantial when considering the cost of healthcare professionals. Clinical researchers often spend weeks cleaning data instead of designing studies, while physicians devote valuable time to reviewing charts to identify patients for clinical trials rather than seeing patients. Similarly, data analysts find themselves manually combining reports instead of performing meaningful analytics that could drive better decisions.
By improving data quality, healthcare organizations can redirect valuable human resources from data processing to data interpretation and decision-making—activities that directly contribute to better patient outcomes and business results.
Beyond efficiency improvements, high-quality data creates opportunities for innovation that would otherwise be impossible. As Lazarevich notes:
“With interoperable data, you can switch systems, add new tools, or share information with partners and researchers—without extra effort, because the data is already in a standardized format.”
This capability is particularly valuable in healthcare, where research depends on diverse datasets. Organizations with high-quality, interoperable data can participate in broader research initiatives and collaborate across organizational boundaries without the friction of data transformation.
The business implications extend beyond research to core operations. Organizations gain vendor flexibility, allowing them to select best-of-breed systems without being locked into specific vendors. Partnership opportunities become significantly easier to pursue, while new technologies can be implemented more quickly across the organization. Additionally, merger and acquisition integration becomes less complex when data is standardized and high-quality.
In a rapidly evolving healthcare landscape, this flexibility represents a significant competitive advantage.
Achieving data quality requires more than technology—it demands organizational commitment and clear policies. As Lazarevich advises: “You need to implement certain policies for maintaining data quality. Every person must be aware that they have to fill in specific fields when they provide reports. Do not let people report as they wish.”
This structured approach is particularly important in healthcare, where consistency and accuracy directly impact patient care and research outcomes. However, establishing policies is only the first step. Lazarevich emphasizes that “you also need to regularly check whether the rules are being followed—because unenforced rules usually don’t work.”
By incorporating these policies into standard workflows and regularly auditing compliance, healthcare organizations can maintain high data quality over time—preserving the value of their data assets and ensuring continued returns on their data quality investments.
For healthcare decision-makers, the business case for data quality investments centers on three key benefits:
- Reduced strategic roadblocks: Lower effort required to implement new initiatives
- Dramatic efficiency improvements: Reduction in time for data-intensive tasks from days/weeks to minutes/hours
- Enhanced capabilities: New possibilities for analysis, collaboration, and innovation
By focusing on these tangible business outcomes rather than technical specifications, healthcare leaders can build compelling cases for investments in data quality. The potential return on investment is substantial—not just in direct cost savings, but in strategic agility, innovation capacity, and competitive advantage.
Ready to build your business case for data quality improvement? Download our comprehensive whitepaper: “How healthcare organizations can build an AI-ready data foundation” for a practical roadmap to data transformation that delivers measurable business results.
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