This resource provides a library of useful Key Performance Indicators for a variety of typical groups and functions within a company — more than 1,000 KPIs in all.
These are organized by department — you can click any department to go directly there.
You can also download an Excel Spreadsheet of all the KPIs.
Departments
- Data
- Finance
- General Counsel
- Human Resources
- Information Technology
- Marketing
- Operations
- Product
- Research and Development
- Sales
- Supply Chain
Note: Some KPIs are used in more than one department.
Download the Library of Key Performance Indicators
Data
Click on any group within the Data department to view its KPIs:
Big Data Team
Data accuracy rate
The accuracy of data collected and processed by the Big Data Team. It could be calculated as the percentage of errors found in the data.
Data completeness rate
How complete the data is. It could be calculated as the percentage of missing data points or fields.
Data processing time
The amount of time it takes the Big Data Team to process and analyze data from various sources.
Data quality score
The overall quality of data collected and processed by the Big Data Team. It could be calculated based on various factors such as accuracy, completeness, and consistency.
Data storage capacity
The amount of data that can be stored by the Big Data Team and their ability to manage and optimize storage resources.
Data usage rate
How frequently the data collected by the Big Data Team is used by other teams or departments within the organization.
Data visualization and reporting
The ability of the Big Data Team to generate meaningful visualizations and reports based on the data they have collected and analyzed.
Machine learning model accuracy
The accuracy of machine learning models developed by the Big Data Team for predictive analytics or other purposes.
Number of data sources
Tthe number of sources from which the Big Data Team collects data. It could include sources such as social media, websites, and internal databases.
Return on investment (ROI)
The financial benefits that the Big Data Team generates for the organization through their data collection and analysis efforts. It could be calculated as the ratio of the financial benefits to the cost of data collection and analysis.
Business Intelligence Team
Dashboard adoption rate
The percentage of users who have adopted and regularly use business intelligence dashboards.
Data accuracy rate
The percentage of accurate data used in business intelligence reporting.
Data completeness rate
The percentage of complete data used in business intelligence reporting.
Data consistency rate
The percentage of consistent data used in business intelligence reporting.
Query response time
The time it takes to respond to queries from business intelligence users.
Report delivery time
The time it takes to deliver business intelligence reports to users.
Report generation time
The time it takes to generate business intelligence reports.
Self-service usage rate
The percentage of users who utilize self-service business intelligence tools.
User satisfaction rate
The level of satisfaction that business intelligence users have with the accuracy, completeness, and usefulness of the data.
Visualization adoption rate
The percentage of users who adopt and regularly use data visualizations in business intelligence reporting.
Data Analytics Team
Average time to complete data analysis projects
The average time it takes for the data analytics team to complete a project. It is a good indicator of the team’s efficiency and productivity.
Business impact of analytics projects
The actual impact of the team’s analytics projects on the business. It could include metrics such as revenue growth, cost savings, or customer satisfaction.
Data accessibility
The ease with which team members can access the data they need for their analysis. It is important to ensure that the team has the necessary data access to perform their tasks.
Data accuracy rate
The accuracy of the data analytics team’s analysis. It is important to ensure that the team is working with accurate data to avoid incorrect conclusions.
Data visualization quality
The quality of the data visualizations produced by the team. It is important to ensure that the team is producing clear, concise, and informative data visualizations.
Number of data sources used
The number of different data sources that the team uses for their analysis. A diverse range of data sources can lead to better insights and more accurate conclusions.
Number of insights generated
The number of new insights that the team generates through their analysis. It is important to ensure that the team is generating valuable insights that can be used to drive business decisions.
Percentage of projects completed on time
The percentage of analytics projects that are completed on time. It is important to ensure that the team is meeting project deadlines and delivering their work in a timely manner.
Predictive accuracy rate
The accuracy of the team’s predictive models. It is important to ensure that the team is producing accurate models that can be used to make informed business decisions.
Staff retention rate
The rate at which the team retains its staff. High staff retention is an indication that the team is working well and that team members are satisfied with their work.
Data Engineering Team
Data processing time
The time it takes to process data from its source and make it available for consumption by other teams within the organization.
Data quality index
The accuracy, completeness, consistency, and validity of the data being processed by the data engineering team.
Data storage capacity
The amount of data that the data engineering team can store and manage effectively.
Data throughput
The amount of data processed by the data engineering team within a given time frame.
ETL job success rate
The percentage of Extract, Transform, Load (ETL) jobs that are successfully completed by the data engineering team.
Infrastructure uptime
The amount of time the data engineering team’s infrastructure is available and operational.
Query response time
The time it takes to execute queries on the data stored by the data engineering team.
Resource utilization
How efficiently the data engineering team is using its resources, including hardware and software.
System availability
The percentage of time the data engineering team’s systems are available for use.
Unprocessed data backlog
The amount of unprocessed data that is waiting to be processed by the data engineering team.
Data Governance Team
Data accuracy rate
The accuracy of the data that is being managed by the data governance team. It is calculated as the percentage of accurate data out of the total data processed.
Data compliance rate
How well the data governance team is complying with relevant data regulations and policies. It is calculated as the percentage of compliant data out of the total data processed.
Data lineage completeness
The extent to which the data governance team can trace the origin and movement of data throughout the organization. It is calculated as the percentage of data lineage completeness out of the total data processed.
Data quality score
The overall quality of the data managed by the data governance team. It is calculated by assessing various data quality dimensions, such as completeness, consistency, and validity.
Data security incidents
The number of security incidents that occur within the data managed by the data governance team. It is calculated as the number of incidents per month or year.
Data usage frequency
The frequency with which the data managed by the data governance team is being used by other teams in the organization. It is calculated as the number of times the data is accessed or used per month or year.
Data value realization
The extent to which the data managed by the data governance team is adding value to the organization. It is calculated as the monetary value of the data used by other teams in the organization.
Policy adoption rate
The extent to which relevant data policies are being adopted by teams within the organization. It is calculated as the percentage of teams that have adopted the policies out of the total number of teams in the organization.
Timeliness of data delivery
The speed with which the data managed by the data governance team is delivered to other teams in the organization. It is calculated as the time taken to deliver the data from the point of request.
User satisfaction rate
The satisfaction level of the users who are accessing the data managed by the data governance team. It is calculated as the percentage of satisfied users out of the total number of users who have accessed the data.
Data Quality Team
Accuracy rate
The percentage of accurate data within the organization’s database. It helps to assess the level of data integrity maintained by the team.
Data completeness
The percentage of complete data that is available in the organization’s database. It helps to assess if the data quality team is collecting all required data fields and if there are any gaps in data collection.
Data consistency
The consistency of data across various sources or data sets. It helps to assess the level of consistency maintained by the team in data entry and data processing.
Data integrity
The accuracy and consistency of data in the organization’s database. It helps to assess the overall health of the database.
Data profiling
The percentage of data that has been profiled by the data quality team. It helps to assess the level of analysis and monitoring done by the team.
Data quality index
The overall quality of the data in the organization’s database. It helps to assess the effectiveness of the team’s efforts to maintain data quality.
Duplicate rate
The percentage of duplicate data within the organization’s database. It helps to assess the level of data duplication and if the team is effectively identifying and removing duplicates.
Response time
The time taken by the data quality team to respond to data quality issues or requests. It helps to assess the team’s efficiency in handling data quality issues.
Timeliness
The percentage of data that is updated in a timely manner. It helps to assess the team’s ability to maintain data quality by updating the database regularly.
Validity rate
The percentage of valid data within the organization’s database. It helps to assess the level of accuracy maintained by the team in data entry and data processing.
Data Science Team
Accuracy rate
How often the predictions made by data models are correct. This KPI helps to ensure that the data science team is producing accurate and reliable results.
Customer retention
The percentage of customers who continue to use a product or service. This KPI can indicate how well the data science team’s work is supporting customer needs and contributing to the company’s overall success.
Data quality score
The quality of data used for analysis, including completeness, accuracy, consistency, and relevance. This KPI helps to ensure that data used by the data science team is reliable and of high quality.
Model building efficiency
The amount of time it takes to build predictive models from data. This KPI helps to identify bottlenecks and inefficiencies in the model-building process and improve overall efficiency.
Model performance improvement
How much the accuracy of predictive models improves over time. This KPI can help to identify areas where the data science team is making progress and where additional improvement is needed.
New data sources
The number of new data sources identified and integrated into existing data sets. This KPI can help to ensure that the data science team is continuously exploring new sources of data and incorporating them into analysis.
Project completion rate
The percentage of data science projects completed on time and within budget. This KPI helps to ensure that the team is delivering results in a timely and efficient manner.
ROI of data science projects
The financial return on investment of data science projects. This KPI can help to identify areas where the data science team is making significant contributions to the company’s bottom line.
Time to insights
The amount of time it takes to generate insights from data. This KPI helps to ensure that the data science team is delivering timely and actionable insights to the organization.
Visualization effectiveness
How well data visualizations communicate complex information to stakeholders. This KPI helps to ensure that the data science team is effectively communicating insights to business leaders and other stakeholders.
Data Security Team
Data Breaches
Number of data breaches that occur in a given period of time.
Data Loss Prevention
Number of data loss prevention incidents that have been detected and prevented in a given period of time.
Encryption Usage
Percentage of data that is encrypted to ensure confidentiality and prevent unauthorized access.
Incident Response Time
Time taken to respond to a data security incident from the time of detection to resolution.
Malware Infections
Number of malware infections that occur in a given period of time.
Patching Cadence
Percentage of systems and software that have been patched and updated in a timely manner to mitigate known vulnerabilities.
Phishing Susceptibility
Percentage of employees who fall for phishing attacks in simulated scenarios.
Security Awareness Training Completion Rate
Percentage of employees who have completed security awareness training.
Vulnerability Scans
Number of vulnerability scans conducted in a given period of time.
Zero-day Exploits
Number of zero-day exploits that have been detected and mitigated in a given period of time.
Data Visualization Team
Average time to create and publish a new visualization
The amount of time it takes the Data Visualization Team to create and publish a new visualization from the time the request is received.
Click-through rates (CTR)
The number of clicks a visualization generates as a percentage of the total views. It helps to identify how engaging the visualizations are.
Completion rates
The percentage of users who complete a particular task or action after viewing a visualization.
Data accuracy rates
The accuracy of the data used in the visualizations, as well as the team’s ability to maintain high data quality standards.
Error rates
The number of errors found in the visualizations that are created by the Data Visualization Team. It helps identify areas that need improvement.
Interactive element usage
The usage rate of interactive elements such as filters, tooltips, and drilldowns within the visualizations.
Response time
The time it takes for the Data Visualization Team to respond to new requests for visualizations.
Share rates
The number of times visualizations are shared with others. It helps identify which visualizations are most effective and what types of visualizations are most often shared.
Time on page
The amount of time users spend viewing a particular visualization. It helps to identify which visualizations are most engaging and effective.
Visualization usage rates
The usage rate of different visualizations, helping to identify which visualizations are most popular with users.
Database Administration Team
Backup success rate
The percentage of successful database backups relative to total attempted backups.
Database uptime
The percentage of time that databases are available and accessible to users.
Database response time
The time it takes for the database to respond to user queries or commands.
Error rate
The percentage of errors occurring during database transactions.
Growth rate
The percentage of increase in database size over a period of time.
Maintenance completion rate
The percentage of scheduled database maintenance tasks that are completed on time.
Query optimization rate
The percentage of database queries that are optimized for improved performance.
Recovery time objective (RTO)
The time it takes to recover the database in the event of a system failure or outage.
Security compliance
The level of compliance with industry and company data security policies and standards.
User satisfaction
The level of user satisfaction with the database performance and accessibility.
