A data doctor is a professional who works with data to improve its quality and integrity. They are responsible for identifying issues and inconsistencies in data sets through analysis, monitoring, and auditing. The primary goal of a data doctor is to “diagnose” problems with data and “prescribe” solutions to make the data more accurate, complete, and reliable for business uses.
Data doctors play a critical role in data-driven organizations by ensuring the foundational data used for reporting and analytics is of high quality. They work closely with data engineers, analysts, and scientists to understand how the data is consumed and where potential problems may exist. Their expertise in data quality helps drive better decision making across the business.
Data Analysis
A critical responsibility of the data doctor is analyzing healthcare data to identify issues and optimize operations. As noted by Coursera, the data analyst must “Review and analyze data to spot errors, inconsistencies.”
This involves reviewing large datasets from electronic health records, insurance claims, clinical trials, and other sources to find anomalies, mistakes, or areas for improvement (Coursera, 2022). The analyst may use statistical methods and data visualization tools to detect outliers or trends in the data.
For example, the analyst could analyze prescription records to find unusual prescribing patterns or patient billing data to identify potential fraud. Careful analysis helps ensure data integrity, clinical effectiveness, and operational efficiency.
Data Cleansing
Data cleansing, also known as data scrubbing or data cleaning, is the process of identifying and correcting corrupted or inaccurate records from a dataset. This is an important step in data analysis in order ensure high data quality and produce accurate insights. The data doctor will methodically review the data to identify incomplete, incorrect, inaccurate or irrelevant parts of the dataset. They utilize various tools and techniques to clean the data such as:
Identifying duplicate records and removing duplicates (TechTarget).
Fixing formatting issues – data may be entered incorrectly or inconsistent formats used (Inforwerks).
Finding and correcting errors in data using validation rules (LinkedIn).
Standardizing data by transforming it into a consistent format.
Enriching data by merging datasets or adding missing values.
Removing irrelevant obsolete data that is no longer needed.
The main goal is to have complete, consistent and accurate data that is ready for analysis. Proper data cleansing improves data quality which leads to reliable analytic results.
Monitoring
Data quality monitoring refers to the ongoing assessment, measurement, and management of an organization’s data in terms of accuracy, consistency, completeness, validity, and timeliness (https://www.ibm.com/blog/8-data-quality-monitoring-techniques/). The data doctor implements various techniques and metrics to monitor data quality on an ongoing basis.
The data doctor may perform regular audits and spot checks on the data to identify any issues with data quality. This can involve analyzing samples of data to check for anomalies, errors, or inconsistencies (https://www.precisely.com/blog/data-quality/how-to-measure-data-quality-7-metrics). Common data quality metrics that might be monitored include completeness, conformity, accuracy, duplication, integrity, and timeliness. The data doctor analyzes these metrics over time to identify any deterioration in data quality.
Data profiling tools may also be utilized to assess data by examining patterns, relationships and rules. The data doctor can monitor data quality dashboards and leverage automated alerts when data issues arise. Establishing data quality key performance indicators and benchmarks allows the data doctor to monitor performance against data quality targets.
Documentation
Thorough documentation is a critical responsibility of the data doctor role. The data doctor must document all data issues encountered and how they were resolved for future reference. This includes detailing any data errors, inconsistencies, missing information, duplication, formatting problems, outliers, and other anomalies.
According to Cornell University, key data documentation should include:
- Context of data collection
- Data collection methodology
- Structure and organization of data files
- Data validation and quality checks performed
Complete documentation allows others to understand the data and any potential limitations or errors. This supports proper data analysis and prevents misuse of flawed data. Thorough documentation also aids reproducibility and continuity if the data doctor role transitions to another team member.
Communication
Effective communication with stakeholders is critical for data doctors. They must be able to clearly convey insights from data analysis to various audiences in order to drive business decisions and outcomes. According to LinkedIn, data doctors should “Know your audience and choose the right format to craft your content.”
One of the main communication responsibilities is keeping stakeholders informed about data quality. Data doctors must provide regular status updates on metrics like completeness, accuracy, and consistency. They should outline plans for improving quality and highlight any issues that need stakeholder input. Clear communication builds trust and ensures stakeholders understand how data problems can impact analysis. According to research, involving stakeholders as communication partners enables their participation in data quality efforts.
Automation
Data quality professionals are increasingly turning to automation to streamline data cleansing, validation, and enrichment processes. According to Data Quality Automation: Introduction and Best Practices, automating data quality processes can significantly improve efficiency. Rather than relying solely on manual checks, automating rules and workflows allows large datasets to be scanned for errors and issues in a fraction of the time.
Some key data quality processes that can be automated include: data profiling to understand datasets, validation against business rules, matching records to eliminate duplicates, parsing and standardization to fix formatting inconsistencies, and enrichment by merging in external data. Effective tools exist to assist with these tasks without needing manual intervention.
By automating repetitive and routine data quality tasks, data professionals can focus their efforts on more complex data problems and analysis. Automated systems also reduce human error that could enter into manual processes. Overall data quality and throughput can improve dramatically with automation.
Training
Data doctors provide training to others on best practices for ensuring data quality. According to the online course Healthcare Data Quality and Governance from Coursera, data doctors help train healthcare professionals to properly monitor, audit, and improve data quality. This includes training on techniques for data cleansing, establishing governance policies, and implementing quality assurance processes. The data doctor acts as an educator to instill an organization-wide culture that values high-quality data. They can provide training through workshops, presentations, documentation, and online courses.
For example, the company Data Doctors offers comprehensive training for their franchises to learn data quality best practices, as mentioned on their franchise information page Computer Repair Franchise Information. Hands-on training ensures data doctors have expertise to then educate others in their organizations on maintaining reliable, accurate data.
Technology
The data doctor utilizes a variety of technologies and tools to monitor, analyze, and improve healthcare data quality. According to The use of Big Data Analytics in healthcare – PMC, big data analytics allows the analysis of large datasets from thousands of patients to identify correlations and insights. The data doctor may use business intelligence tools like Tableau for interactive data visualization and advanced analytics to uncover trends and patterns.
The data doctor also employs tools for data cleansing and standardization, such as Trifacta or Tamr. As noted in Electronic health record data quality assessment and tools, the multitude of data in EHRs makes them well-suited for high-dimensional analysis and machine learning, so the data doctor utilizes AI and ML to automate data quality processes. Overall, the data doctor is proficient with leading technologies to efficiently monitor, cleanse, analyze, and extract value from healthcare data.
Conclusion
In conclusion, the data doctor plays a critical role in the healthcare industry by leveraging data to uncover insights and guide decision making. As we’ve explored, data doctors are responsible for collecting, cleaning, analyzing, and interpreting healthcare data. Their specialized statistical and analytical skills allow them to transform raw data into meaningful information for diagnosis, treatment, quality improvement, and more. The data doctor collaborates with clinicians and healthcare administrators to ensure data accuracy and proper application. Their work delivers impactful results, from assisting in drug development to optimizing hospital operations. As data grows exponentially in healthcare, the data doctor will continue to be an invaluable asset. Their data interpretation and storytelling helps put complex information into context for clinicians and enables data-driven care and administration. The data doctor role showcases how properly utilized data can profoundly improve patient outcomes and experiences.