These metrics help laboratories in maintaining high-quality standards, complying with regulatory requirements, and ensuring customer trust. KPIs also facilitate continual improvement and international recognition. They are key for laboratories to demonstrate their capability in providing valid and reliable results.
KPI |
Definition
|
Business Insights [?]
|
Measurement Approach
|
Standard Formula
|
Analytical Method Validation Compliance More Details |
The degree of adherence to ISO 17025 standards in the validation of analytical methods used.
|
Indicates the laboratory's adherence to ensuring that methods are properly tested for reliability and accuracy before use.
|
Number of validated methods compared to total methods required for validation, considering both internal standards and external regulatory requirements.
|
(Number of Validated Methods / Total Number of Methods Required for Validation) * 100
|
- An increasing non-compliance in method validation may indicate a decline in quality control processes.
- A decreasing non-compliance can signal improved adherence to ISO 17025 standards and enhanced method validation procedures.
- Are there specific analytical methods that consistently show non-compliance in validation?
- How does our method validation compliance compare with industry benchmarks or regulatory requirements?
- Invest in training and development for laboratory staff to ensure proper understanding and execution of method validation protocols.
- Regularly review and update method validation procedures to align with the latest ISO 17025 standards and industry best practices.
- Implement a robust quality management system to monitor and address any non-compliance issues in method validation.
Visualization Suggestions [?]
- Line charts showing the trend of method validation compliance over time.
- Pareto charts to identify the most common reasons for non-compliance in method validation.
- Non-compliance in method validation can lead to inaccurate test results and compromised data integrity.
- Repeated non-compliance may result in regulatory sanctions and loss of accreditation.
- LIMS (Laboratory Information Management System) to track and manage method validation processes.
- Statistical analysis software for evaluating the effectiveness of analytical methods and identifying areas of non-compliance.
- Integrate method validation compliance data with quality control systems to ensure continuous improvement and corrective actions.
- Link method validation results with the overall laboratory management system for comprehensive data analysis and decision-making.
- Improving method validation compliance can enhance the reliability and accuracy of analytical results, leading to better decision-making based on the data.
- On the other hand, persistent non-compliance can erode trust in the laboratory's capabilities and impact its reputation in the industry.
|
Compliance with Data Retention Policies More Details |
The degree to which data retention practices align with the ISO 17025 requirements and other applicable regulations.
|
Reflects the lab's commitment to legal and regulatory standards, potentially reducing legal risks and maintaining data integrity.
|
Assesses adherence to policies regarding the length of time data must be kept, categorized by data type and regulatory requirements.
|
(Number of Data Sets Retained in Compliance / Total Number of Data Sets Required to be Retained) * 100
|
- An increasing non-compliance with data retention policies may indicate a lack of awareness or understanding of the requirements.
- A decreasing trend could signal improved data management practices and alignment with regulatory standards.
- Are there specific data types or categories that are frequently retained for longer or shorter periods than required?
- How does our data retention compliance compare with industry best practices or legal requirements?
- Regularly review and update data retention policies to ensure alignment with changing regulations.
- Implement automated data archiving and deletion processes to enforce retention policies consistently.
- Provide ongoing training and awareness programs for staff to understand the importance of data retention compliance.
Visualization Suggestions [?]
- Line charts showing the trend of compliance with data retention policies over time.
- Stacked bar charts comparing compliance levels across different data categories or departments.
- Non-compliance with data retention policies can lead to legal and regulatory penalties.
- Inconsistent data retention practices may result in data security and privacy risks.
- Data management platforms with built-in retention policy enforcement and monitoring capabilities.
- Compliance management software to track and report on adherence to data retention requirements.
- Integrate data retention compliance tracking with overall compliance management systems for a comprehensive view of regulatory adherence.
- Link data retention policies with data governance frameworks to ensure alignment with broader data management strategies.
- Improving data retention compliance can enhance data security and privacy protection.
- Non-compliance with data retention policies can lead to legal and regulatory penalties.
|
Data Analysis Turnaround Time More Details |
The time taken from data acquisition to the provision of analytical results, indicating the efficiency of the data analysis process.
|
Helps in identifying bottlenecks in the data analysis process and improving laboratory efficiency.
|
Measures the time taken from data receipt to when the analysis is completed and reported.
|
Total Time for Data Analysis / Total Number of Analyses
|
- Increasing data analysis turnaround time may indicate inefficiencies in the analytical process or resource constraints.
- Decreasing turnaround time can signal improved data management systems or enhanced analytical capabilities.
- Are there bottlenecks in the data analysis process that are causing delays?
- How does our data analysis turnaround time compare with industry benchmarks or best practices?
- Invest in advanced data analysis tools and software to streamline the analytical process.
- Implement training programs to enhance the skills of analysts and improve efficiency.
- Regularly review and optimize data management protocols to minimize delays in data acquisition.
Visualization Suggestions [?]
- Line charts to track the trend of data analysis turnaround time over different time periods.
- Stacked bar graphs to compare the distribution of turnaround times across different analytical tasks or departments.
- Extended data analysis turnaround time can lead to delayed decision-making and hinder the organization's responsiveness.
- Inaccurate or rushed analysis due to time constraints can result in flawed insights and poor decision-making.
- Utilize data management platforms like Microsoft Azure or Amazon Web Services for efficient data storage and retrieval.
- Implement data analysis tools such as Tableau or Power BI for streamlined and automated analytical processes.
- Integrate data analysis turnaround time tracking with project management systems to align analytical tasks with project timelines.
- Link data acquisition processes with data analysis to ensure a seamless flow of data for timely analysis.
- Improving data analysis turnaround time can lead to faster insights, better decision-making, and improved operational efficiency.
- However, rapid changes may require adjustments in resource allocation and potential trade-offs between speed and depth of analysis.
|
CORE BENEFITS
- 40 KPIs under ISO 17025
- 15,468 total KPIs (and growing)
- 328 total KPI groups
- 75 industry-specific KPI groups
- 12 attributes per KPI
- Full access (no viewing limits or restrictions)
FlevyPro and Stream subscribers also receive access to the KPI Library. You can login to Flevy here.
|
IMPORTANT: 17 days left until the annual price is increased from $99 to $149.
$99/year
Data Archiving Integrity Rate More Details |
The integrity of archived data, ensuring that it remains unaltered and accessible over time.
|
Ensures long-term data reliability and usability for future retrieval and compliance purposes.
|
Percentage of data sets with successful integrity checks after archiving, including checksums and verification processes.
|
(Number of Data Sets Passing Integrity Checks / Total Number of Archived Data Sets) * 100
|
- Monitoring data integrity over time to identify any potential degradation or loss of data.
- Implementing regular checks and audits to ensure the accessibility and unaltered state of archived data.
- Are there any specific data sets or types of data that are more prone to integrity issues?
- How does our data archiving integrity rate compare with industry standards or best practices?
- Establishing strict data archiving protocols and version control processes.
- Investing in robust data archiving and backup systems to prevent data loss or corruption.
- Regularly training and educating staff on the importance of maintaining data integrity and accessibility.
Visualization Suggestions [?]
- Line charts showing the trend of data archiving integrity rate over time.
- Stacked bar charts comparing the integrity rates of different types of archived data.
- Data integrity issues can lead to compliance violations and legal repercussions.
- Loss of accessibility to critical archived data can disrupt operations and decision-making processes.
- Data management software like Commvault or Veritas to ensure secure and reliable data archiving.
- Blockchain technology for immutable and tamper-proof data archiving.
- Integrating data archiving integrity with overall data governance and compliance processes.
- Linking data archiving systems with data analytics platforms to identify any anomalies or irregularities in archived data.
- Improving data archiving integrity can enhance trust in the organization's data and decision-making processes.
- However, the initial investment in robust data archiving systems and processes may increase operational costs.
|
Data Audit Trail Completeness More Details |
The comprehensiveness of documentation that records the sequence of data handling activities.
|
Reflects the lab's ability to provide complete historical records for data verifiability and regulatory compliance.
|
Proportion of records with fully documented audit trails, including user actions, timestamps, and data modifications.
|
(Number of Complete Audit Trails / Total Number of Audit Trails Required) * 100
|
- An increasing completeness of the data audit trail may indicate improved data management and compliance with regulatory requirements.
- A decreasing completeness could signal potential data integrity issues or gaps in documentation that may pose risks to the accuracy and reliability of the data.
- Are all data handling activities consistently documented and timestamped?
- How does the completeness of our data audit trail compare with industry best practices or regulatory standards?
- Implement automated data logging and tracking systems to ensure comprehensive documentation of data handling activities.
- Regularly review and update data management procedures to address any gaps in the audit trail.
- Provide training and resources for staff to understand the importance of maintaining a complete data audit trail.
Visualization Suggestions [?]
- Line charts showing the trend of data audit trail completeness over time.
- Pie charts illustrating the distribution of data handling activities and the corresponding documentation completeness.
- Incomplete data audit trails may lead to compliance violations and regulatory penalties.
- Data integrity and reliability may be compromised if key data handling activities are not properly documented.
- Data management software with built-in audit trail features, such as LabWare LIMS or Thermo Fisher SampleManager.
- Document management systems that facilitate version control and timestamping of data handling activities.
- Integrate data audit trail completeness with quality management systems to ensure that data integrity is maintained throughout the entire data lifecycle.
- Link data audit trail documentation with compliance and regulatory reporting systems for seamless monitoring and reporting.
- Improving data audit trail completeness can enhance the overall trustworthiness and credibility of the organization's data, leading to better decision-making and stakeholder confidence.
- Conversely, a lack of completeness in the data audit trail may result in decreased trust in the organization's data, potentially impacting business operations and relationships with stakeholders.
|
Data Availability Rate More Details |
The percentage of time that the data is accessible for use, indicating the effectiveness of data storage and backup systems.
|
Highlights system reliability and accessibility, critical for operational decision-making and continuity.
|
Percentage of time that data is accessible for use, relative to planned availability times.
|
(Time Data is Available / Total Planned Data Availability Time) * 100
|
- An increasing data availability rate may indicate improved data storage and backup systems or better data management practices.
- A decreasing rate could signal issues with data accessibility, potential data loss, or inadequate backup procedures.
- Are there specific data sets or types of data that are consistently inaccessible?
- How does our data availability rate compare with industry standards or best practices?
- Regularly test data backup and recovery processes to ensure data accessibility in case of system failures or data loss.
- Implement redundant data storage systems to minimize the risk of data unavailability due to hardware failures.
- Establish clear data management protocols and responsibilities to ensure data is consistently accessible to authorized users.
Visualization Suggestions [?]
- Line charts showing the trend of data availability rate over time.
- Pie charts illustrating the distribution of data accessibility across different data sets or storage systems.
- Low data availability rates can lead to operational disruptions, decision-making delays, and potential data loss.
- Inconsistent data accessibility may indicate vulnerabilities in data security and compliance risks.
- Data backup and recovery software such as Veeam or Commvault for reliable data protection and accessibility.
- Data management platforms like Microsoft Azure or AWS to ensure secure and accessible data storage.
- Integrate data availability tracking with IT service management systems to prioritize and address data accessibility issues.
- Link data availability metrics with compliance and risk management systems to ensure data accessibility aligns with regulatory requirements.
- Improving data availability can enhance operational efficiency and decision-making speed, but may require investment in robust data storage and backup infrastructure.
- Conversely, low data availability rates can lead to data-related disruptions, compliance issues, and potential reputational damage.
|
In selecting the most appropriate ISO 17025 KPIs from our KPI Library for your organizational situation, keep in mind the following guiding principles:
It is also important to remember that the only constant is change—strategies evolve, markets experience disruptions, and organizational environments also change over time. Thus, in an ever-evolving business landscape, what was relevant yesterday may not be today, and this principle applies directly to KPIs. We should follow these guiding principles to ensure our KPIs are maintained properly:
By systematically reviewing and adjusting our ISO 17025 KPIs, we can ensure that your organization's decision-making is always supported by the most relevant and actionable data, keeping the organization agile and aligned with its evolving strategic objectives.