After exploring various articles on the six data quality dimensions and data governance, I realized these principles are fundamental to driving success in regulated industries, especially in the context of FDA 21 CFR Part 11. At the heart of these concepts is data—ensuring it remains accurate, trustworthy, and secure. High-quality data is the foundation for precise analysis, which, in turn, leads to informed and reliable business decisions. So, let us explore these six data quality dimensions and data governance in the context of FDA 21 CFR Part 11.
Introduction to FDA 21 CFR Part
FDA 21 CFR Part 11, established in 1997, sets the regulatory framework for using electronic records and electronic signatures in place of traditional paper records and handwritten signatures. This regulation ensures that electronic records maintain the same reliability, authenticity, and integrity as paper records.
The Code of Federal Regulations (CFR) is the official compilation of permanent rules and regulations issued by U.S. federal agencies, published in the Federal Register. The CFR is divided into 50 titles, each covering a broad topic. Title 21 specifically relates to Food and Drugs.
Several key agencies, including the Food and Drug Administration (FDA), Drug Enforcement Administration (DEA), and Office of National Drug Control Policy (ONDCP), are responsible for enforcing Title 21 of the CFR. This title governs various aspects of food and drug safety and is divided into three chapters. Chapter 1 pertains to the FDA, Chapter 2 to the DEA, and Chapter 3 to the ONDCP.
Chapter 1 of Title 21 consists of 99 parts, with Part 11 focusing on the use of electronic records and electronic signatures.
Key Requirements of FDA 21 CFR Part 11:
Security: Electronic records must be safeguarded against unauthorized access, modification, or deletion.
Validation: Electronic records and signatures must be validated to ensure their reliability, accuracy, and security.
Audit Trails: Every action taken on electronic records must be documented through an audit trail.
Electronic Signatures: Signatures must be unique to each individual, securely managed, and linked to the associated electronic record.
Retention and Retrieval: Electronic records must be retained and easily retrievable throughout their designated retention period.
These requirements ensure that electronic records are trustworthy and compliant with FDA regulations, maintaining the integrity of the data for audits and regulatory reviews.
What are the six data quality dimensions?
Data can only deliver business value when it upholds a high level of integrity. This starts with strong data quality, contextual richness, seamless integration, and effective data governance tools and processes.
In this blog, we will focus on the dimension of data quality. The term “data quality dimension” has long been used to describe how data quality is measured. However, even among experts, there is no universal consensus on which dimensions are most critical.
The first step in understanding any topic is to clearly define the key terms involved.
Accuracy – Does the data reflect the data set?
The term “accuracy” refers to how well information correctly represents an event, location, person, or other entity. In other words, accurate data reliably reflects the real-world situation it is meant to capture. For example, in a manufacturing facility, sensors on a production line monitor the temperature of a machine that is critical to product quality. The sensor is programmed to measure and report the temperature every minute. Accurate data means that the sensor’s readings match the actual temperature of the machine at the time of measurement.
In the context of FDA 21 CFR Part 11, the accuracy of electronic records is essential for compliance. The regulation mandates that electronic records precisely reflect the original data and that any changes are properly captured and traceable.
Completeness – Are all data sets and data items recorded?
Completeness evaluates whether the data is sufficient to support meaningful decisions and inferences. It ensures that the data collected fully covers the scope of the issue being addressed, and identifies any gaps, missing values, or biases that could impact the accuracy or outcomes of the analysis. For example, in a predictive maintenance system that monitors various parameters such as vibration, temperature, pressure, and machine operating hours, complete data means the system must capture all relevant sensor readings at appropriate intervals. If only vibration and temperature are recorded, while pressure and operating hours are missing, the dataset is considered incomplete.
In the context of FDA 21 CFR Part 11, completeness is critical for compliance. Incomplete records can create gaps in audit trails and lead to non-compliance. It is essential that all relevant data, including metadata such as timestamps and user IDs, is captured to maintain regulatory standards.
Consistency – Can we match the data set across data stores?
Data consistency refers to the quality of data being uniform, accurate, and coherent across various databases, systems, and applications within an organization. It ensures that data remains the same across different platforms, such as databases or HMI/SCADA systems. In many companies, the same information may be stored in multiple locations, and if that information matches, it is considered “consistent.”
For instance in a factory, a manufacturing execution system (MES) may monitoring, tracking, documenting, and controlling the entire production lifecycle while a Supervisory Control and Data Acquisition (SCADA) system monitors production processes. If the SCADA system records the production of 100 units, the MES system should reflect the same increase of 100 units in inventory. Any discrepancy would indicate a lack of consistency, potentially leading to operational inefficiencies or errors.
From the perspective of FDA 21 CFR Part 11, data consistency across different systems and throughout the record lifecycle is essential for maintaining reliable records. Part 11 requires that electronic records be consistent with their paper counterparts and remain uniform and trustworthy over time.
Timeliness – Is the data being captured and made available in real-time or within an acceptable timeframe for effective decision-making?
Data timeliness refers to how up-to-date and readily available data is for its intended use. It plays a crucial role in enabling businesses to make quick and informed decisions based on the most current information. For example, real-time data from sensors is used to monitor key parameters such as temperature, pressure, and vibration levels in turbines. Timely data ensures that this information is continuously and promptly transmitted to the Supervisory Control and Data Acquisition (SCADA) system, allowing operators to take immediate action if any readings suggest potential issues.
In the context of FDA 21 CFR Part 11, timely recording and availability of data are critical for ensuring that records are complete and auditable. 21 CFR Part 11 mandates that data be recorded at the time of the event and be readily available for review during audits, ensuring traceability and compliance.
Validity – Does the data match the rules?
Data validity ensures that data adheres to the required formats, rules, or constraints. Data validation rules or tests can be developed to maintain data quality and integrity as it is transformed and transferred from its source to its target destination. Invalid data can impact the completeness of a dataset, so rules can be defined to either ignore or resolve invalid data to ensure data completeness.
For example, in a manufacturing setting, each part on the production line is assigned a unique barcode, which is scanned at different stages to track its progress. For the data to be valid, the barcode must follow a specific format (e.g., a 12-digit numeric code) and correspond to an existing part in the system’s database. If an operator scans a barcode that doesn’t match the required format or references a nonexistent part, the system’s data validation process will flag and reject it as invalid.
In the context of FDA 21 CFR Part 11, validity ensures that electronic records and signatures meet the required formats and comply with regulatory standards. FDA 21 CFR Part 11 mandates that electronic systems ensure records are valid and conform to predefined standards and formats to maintain compliance.
Uniqueness – Is there a single view of the data set?
Uniqueness means that each data entity is represented only once in the dataset, with no duplicate entries. It is a critical dimension for preventing duplication or overlap. Data duplication can occur frequently, but ensuring uniqueness is essential for accurate tracking and management. For instance, in a parts manufacturing environment, each product or component is assigned a unique serial number to allow individual identification and tracking throughout the production process and beyond. Data uniqueness ensures that each serial number is distinct, preventing two products from sharing the same identifier.
In this scenario, data uniqueness allows for individual traceability of each product throughout its entire lifecycle, from assembly to distribution, supporting both quality assurance and regulatory compliance.
Within the context of FDA 21 CFR Part 11, uniqueness guarantees that each electronic record or signature is identifiable and cannot be duplicated or tampered with. Part 11 emphasizes that electronic records and signatures must be both unique and traceable to specific individuals. Ensuring uniqueness is crucial for maintaining the integrity of records, as it helps ensure traceability and prevents data duplication, which could compromise audit trails and regulatory compliance.
Some data accuracy frameworks also encompass additional factors such as reliability, precision, and integrity. These elements ensure not only that the data is accurate but also that it is consistent over time (reliability), measured with exactness (precision), and protected from unauthorized changes or corruption (integrity).
ADISRA SmartView FDA 21 CFR Part 11
When evaluating the six data quality dimensions—accuracy, completeness, consistency, timeliness, validity, and uniqueness—in the context of FDA 21 CFR Part 11 compliance, it’s evident that these are more than theoretical concepts; they are practical guidelines essential for regulatory success. ADISRA SmartView, as a complete HMI/SCADA solution, plays a vital role in assisting industries in meeting these stringent regulations, while safeguarding the integrity of electronic records and signatures.
ADISRA SmartView offers features that align with the core requirements of FDA 21 CFR Part 11, ensuring that businesses in highly regulated sectors such as pharmaceuticals, biotechnology, and food production maintain the quality, accuracy, and security of their electronic records. For a more detailed exploration of these capabilities, you can access an in-depth whitepaper that covers how to leverage ADISRA SmartView’s FDA 21 CFR Part 11 features.
While the SCADA/HMI software itself cannot be certified as compliant with FDA 21 CFR Part 11, ADISRA SmartView is specifically engineered to provide users with the necessary tools to develop applications that meet the regulation’s requirements. This ensures that businesses can design systems that support compliance efforts effectively.
Conclusion:
In regulated industries where compliance with FDA 21 CFR Part 11 is non-negotiable, ADISRA SmartView solution for ensuring data integrity, accuracy, and security is essential. By aligning its features with the six data quality dimensions, ADISRA SmartView not only helps businesses achieve regulatory compliance but also enhances their operational efficiency. The platform’s ability to deliver real-time monitoring, complete audit trails, and secure electronic records enables organizations to maintain the trustworthiness and authenticity of their data, driving success in the face of stringent regulations.
Ultimately, ADISRA SmartView ensures that businesses remain compliant with FDA 21 CFR Part 11 by providing the necessary tools to maintain high-quality, reliable, and secure electronic records, making it an asset in any regulatory framework.