Maximising secure data environments potential: Navigating the hidden costs of bad data
Data is the foundation for shaping strategies, operations, and research across healthcare organisations. It drives innovation, ultimately enhancing the quality, safety, and efficiency of services, as well as the advancement of medical research.
But it’s crucial to acknowledge that not all data is equal. An ominous spectre, often referred to as ‘poor data quality’ or ‘bad data’, casts a pervasive shadow over the utility of the data within healthcare organisations, presenting as one of the most pressing challenges.
Good data quality is vital for secure data environments (SDE). It ensures data integrity and confidentiality, meaning sensitive information is protected and trustworthy. It’s also crucial for medical research, leading to accurate and reliable findings, accelerating medical breakthroughs, and improving patient safety. Ultimately, good data quality supports the sectors goals of modernisation, innovation, and data-driven healthcare and research.
To achieve the optimum data quality, a robust data quality management framework is needed. Data quality management involves the systematic governance, stewardship, assessment, improvement, and monitoring of data quality throughout its lifecycle. Applying data management to a framework ensures that data can be used as required and meets user expectations. This is a key strategy for overcoming the challenges of bad data and enhancing SDEs and medical research for better patient care and public health. It ensures data is timely, accurate, complete, and consistent, making it useful for answering healthcare questions, securing SDEs, and advancing research.
Our method to ensure good data in SDEs follows a comprehensive four-step process:
1. Identify critical focus areas
The journey begins with a thorough assessment of the current state of data quality, which is particularly crucial when handling sensitive data for research. Start by identifying and quantifying sources, data types and the impact of poor data within datasets. By assigning numerical values to data elements or attributes based on their quality, researchers can effectively prioritise and monitor the most critical or problematic areas. This measurement and quantification not only aids prioritisation but also enables ongoing tracking, a critical element in continuous improvement, crucial for reliable research outcomes.
2. Refine processes to flag bad data fast
Next, address errors and inconsistencies at their source. This is vital for maintaining data quality within SDEs and for research validity. Correcting or eliminating errors within transformation layers can lead to multiple conflicting versions of truth, causing further confusion. By addressing errors at their source, data is validated against predefined rules and standards, such as data types, formats, ranges, or patterns, ensuring reliability for research purposes. When working with multiple data sources, harmonisation becomes necessary. Leveraging industry-standard values enhances consistency and understanding, enabling the reporting of anomalies while facilitating changes made by data owners. Developing a common data model creates a federated, unified data layer, improving the reliability of data for research and supporting secure data exchange.
3. Monitor and communicate
Continuous monitoring and tracking of changes in data quality over time are of paramount importance, particularly in the context of research and the SDE. By alerting users and data owners to emerging issues, we ensure that data quality remains a top operational priority. Dashboards display key data quality indicators, such as accuracy, completeness, consistency, timeliness, or relevance. Furthermore, visualising and communicating these scores to the researchers is essential, involving stakeholders through push reporting. This not only keeps them informed but also engages them in the data quality management process, making them aware of trends and anomalies in data quality. These scores can also be integrated into final reporting, providing end users with an indicator of the reliability of the presented analysis.
4. Educate on bad data’s impact
The final step focuses on developing and implementing best practices and policies to prevent the proliferation of poor data. Governance tools play a pivotal role in establishing and documenting data quality rules, standards and guidelines, including naming conventions, data definitions, and business rules. Assigning data owners and stewards to educate and enforce these standards and processes is imperative. Empowering these data stewards to educate stakeholders and users on the importance, benefits and best practices of data quality management significantly increases awareness and knowledge, reducing the likelihood of future issues related to poor data, thereby ensuring reliable research outcomes and data integrity within SDEs.
Within health research, the spectre of bad data stands as a formidable challenge, one that has the potential to inflict significant harm on both healthcare services and medical research. However, it is not an inevitability, and it is certainly not an insurmountable problem. By diligently adhering to a data quality management framework, you can ensure that your data within the SDE and the research being done using it remains accurate, complete, consistent, timely and relevant. This commitment, in turn, will lead to improved productivity in healthcare services, enhanced efficiency in SDEs, a fertile ground for innovative medical research, and informed, precise decision-making, thereby benefiting patients and the broader healthcare community.
It is imperative to remember the timeless adage: “garbage in, garbage out.” The quality of your data is intrinsically linked to the quality of healthcare outcomes and medical advancements, making the investment in data quality management an essential and non-negotiable component of the modern healthcare and research landscape.