Data Governance Metrics and Measurement
Expert-defined terms from the Professional Certificate in Data Governance course at London School of Planning and Management. Free to read, free to share, paired with a professional course.
Accountability in data governance refers to the responsibility of individ… #
This concept is closely related to data stewardship and data quality, as it involves identifying and assigning roles and responsibilities for data management and ensuring that data is properly maintained and secured. For example, in a financial institution, the chief data officer may be accountable for ensuring that customer data is handled in accordance with relevant laws and regulations.
Accuracy in data measurement refers to the degree to which data values ar… #
This concept is important in data governance as it ensures that data is reliable and trustworthy. For instance, in a healthcare setting, accurate data on patient outcomes is crucial for making informed decisions about treatment and care. Related terms include precision and validity, which also impact the overall quality of data.
Adherence in data governance refers to the degree to which an organizatio… #
This concept is closely related to compliance and regulatory requirements, as it involves ensuring that data is handled in accordance with relevant laws and regulations. For example, in a retail organization, adherence to data governance policies may involve ensuring that customer data is properly secured and protected.
Aggregate data refers to combined data from multiple sources, which is of… #
This concept is important in data governance as it involves ensuring that data is properly integrated and managed. For instance, in a marketing organization, aggregate data on customer behavior may be used to inform targeted advertising campaigns.
Anomaly detection in data measurement refers to the process of identifyin… #
This concept is closely related to data quality and data validation, as it involves ensuring that data is accurate and reliable. For example, in a financial institution, anomaly detection may be used to identify suspicious transactions or fraudulent activity.
Audit in data governance refers to the process of examining and evaluatin… #
This concept is important in data governance as it involves identifying and addressing potential risks and vulnerabilities. For instance, in a healthcare setting, an audit may be conducted to ensure that patient data is properly secured and protected.
Authentication in data security refers to the process of verifying the <b… #
This concept is closely related to authorization and access control, as it involves ensuring that only authorized individuals or systems can access sensitive data. For example, in a financial institution, authentication may involve using passwords or biometric data to verify user identities.
Authorization in data security refers to the process of granting or denyi… #
This concept is important in data governance as it involves ensuring that sensitive data is properly protected and secured. For instance, in a retail organization, authorization may involve granting access to customer data only to authorized personnel.
Automated data processing in data governance refers to the use of tech… #
This concept is closely related to efficiency and productivity, as it involves streamlining data management processes and reducing manual errors. For example, in a marketing organization, automated data processing may be used to segment customer data and inform targeted advertising campaigns.
Availability in data measurement refers to the degree to which data is <b… #
This concept is important in data governance as it involves ensuring that data is properly managed and maintained to support business operations. For instance, in a healthcare setting, availability of patient data is crucial for making informed decisions about treatment and care.
Benchmarking in data governance refers to the process of comparing an org… #
This concept is closely related to best practices and continuous improvement, as it involves identifying areas for improvement and implementing changes to optimize data management. For example, in a financial institution, benchmarking may involve comparing data security practices to those of other organizations in the industry.
Bias in data measurement refers to the presence of systematic errors</… #
This concept is important in data governance as it involves ensuring that data is properly collected, managed, and analyzed to support informed decision-making. For instance, in a marketing organization, bias in customer data may impact the effectiveness of targeted advertising campaigns.
Business intelligence in data governance refers to the use of data ana… #
This concept is closely related to data visualization and data mining, as it involves using data to identify trends, patterns, and insights that can inform business strategy. For example, in a retail organization, business intelligence may be used to analyze customer behavior and inform targeted marketing campaigns.
Classification in data governance refers to the process of categorizin… #
This concept is important in data security as it involves ensuring that sensitive data is properly protected and secured. For instance, in a financial institution, classification of customer data may involve categorizing it as public, private, or confidential.
Compliance in data governance refers to the degree to which an organizati… #
This concept is closely related to adherence and audit, as it involves ensuring that data is handled in accordance with relevant laws and regulations. For example, in a healthcare setting, compliance with HIPAA regulations is crucial for protecting patient data.
Confidentiality in data security refers to the protection of sensitive… #
This concept is important in data governance as it involves ensuring that sensitive data is properly secured and protected. For instance, in a financial institution, confidentiality of customer data is crucial for maintaining trust and preventing identity theft.
Consistency in data measurement refers to the degree to which data values… #
This concept is closely related to accuracy and precision, as it involves ensuring that data is reliable and trustworthy. For example, in a marketing organization, consistency in customer data is crucial for informing targeted advertising campaigns.
Continuous improvement in data governance refers to the ongoing process o… #
This concept is closely related to benchmarking and best practices, as it involves identifying areas for improvement and implementing changes to optimize data management. For instance, in a financial institution, continuous improvement may involve regularly reviewing and updating data security policies and procedures.
Data architecture in data governance refers to the overall design … #
For example, in a retail organization, data architecture may involve designing a data warehouse to integrate and manage customer data.
Data asset in data governance refers to any collection of data tha… #
This concept is closely related to data management and data security, as it involves ensuring that data assets are properly protected and secured. For instance, in a financial institution, data assets may include customer account information or transactional data.
Data backup in data governance refers to the process of copying an… #
This concept is important in data security as it involves ensuring that data is properly protected and secured. For example, in a healthcare setting, data backup may involve regularly backing up patient data to a secure server.
Data classification in data governance refers to the process of catego… #
This concept is closely related to data security and compliance, as it involves ensuring that sensitive data is properly protected and secured. For instance, in a financial institution, data classification may involve categorizing customer data as public, private, or confidential.
Data cleansing in data governance refers to the process of identifying… #
This concept is important in data quality as it involves ensuring that data is accurate and reliable. For example, in a marketing organization, data cleansing may involve removing duplicate records or invalid data from a customer database.
Data discovery in data governance refers to the process of identifying… #
This concept is closely related to data management and data security, as it involves ensuring that data is properly managed and maintained. For instance, in a retail organization, data discovery may involve identifying and mapping customer data across different systems and applications.
Data encryption in data security refers to the process of converting</… #
For example, in a financial institution, data encryption may involve using SSL or TLS to secure online transactions.
Data governance in data governance refers to the overall framework … #
This concept is closely related to data management and data security, as it involves ensuring that data is properly managed and maintained to support business operations. For instance, in a healthcare setting, data governance may involve establishing policies for managing patient data and ensuring compliance with regulatory requirements.
Data integration in data governance refers to the process of combining… #
This concept is important in data management as it involves ensuring that data is properly managed and maintained to support business operations. For example, in a retail organization, data integration may involve integrating customer data from different systems and applications into a single customer database.
Data management in data governance refers to the overall process a… #
This concept is closely related to data governance and data security, as it involves ensuring that data is properly managed and maintained to support business operations. For instance, in a financial institution, data management may involve managing customer account information and transactional data.
Data mapping in data governance refers to the process of creating … #
This concept is important in data discovery and data management, as it involves identifying and understanding the relationships between different data assets and systems. For example, in a retail organization, data mapping may involve creating a visual map of customer data flows across different systems and applications.
Data mining in data governance refers to the process of analyzing … #
This concept is closely related to business intelligence and data analysis, as it involves using data to inform business strategy and decision-making. For instance, in a marketing organization, data mining may involve analyzing customer data to identify trends and patterns that can inform targeted advertising campaigns.
Data profiling in data governance refers to the process of analyzing</… #
This concept is important in data quality and data validation, as it involves ensuring that data is accurate and reliable. For example, in a financial institution, data profiling may involve analyzing customer data to identify credit risk or fraudulent activity.
Data protection in data security refers to the process of safeguarding… #
This concept is closely related to data encryption and access control, as it involves ensuring that sensitive data is properly secured and protected. For instance, in a healthcare setting, data protection may involve using firewalls and intrusion detection systems to prevent cyber attacks.
Data quality in data governance refers to the degree to which data… #
This concept is important in data management as it involves ensuring that data is reliable and trustworthy. For example, in a marketing organization, data quality may involve ensuring that customer data is accurate and up-to-date.
Data recovery in data governance refers to the process of restoring</b… #
This concept is closely related to data backup and data security, as it involves ensuring that data is properly protected and secured. For instance, in a financial institution, data recovery may involve restoring customer account information from a backup system after a cyber attack.
Data retention in data governance refers to the period of time dur… #
For example, in a healthcare setting, data retention may involve storing patient data for a minimum of 10 years to comply with regulatory requirements.
Data security in data governance refers to the practices and pr… #
This concept is closely related to data protection and access control, as it involves ensuring that sensitive data is properly secured and protected. For instance, in a financial institution, data security may involve using firewalls and intrusion detection systems to prevent cyber attacks.
Data storage in data governance refers to the physical or virtu… #
For example, in a retail organization, data storage may involve using a cloud-based storage system to store customer data.
Data validation in data governance refers to the process of checking</… #
This concept is closely related to data quality and data profiling, as it involves ensuring that data is reliable and trustworthy. For instance, in a financial institution, data validation may involve checking customer data for errors or inconsistencies before using it for credit scoring or fraud detection.
Data visualization in data governance refers to the process of present… #
For example, in a marketing organization, data visualization may involve creating dashboards to display customer behavior and sales trends.
Data warehouse in data governance refers to a centralized repository</… #
For instance, in a retail organization, a data warehouse may be used to integrate and manage customer data from different systems and applications.
Disaster recovery in data governance refers to the process of restorin… #
This concept is closely related to data backup and data recovery, as it involves ensuring that data is properly protected and secured. For example, in a financial institution, disaster recovery may involve restoring customer account information from a backup system after a cyber attack.
Encryption in data security refers to the process of converting da… #
For instance, in a healthcare setting, encryption may involve using SSL or TLS to secure online transactions.
ETL (Extract, Transform, Load) in data governance refers to the process o… #
This concept is closely related to data integration and data management, as it involves ensuring that data is properly managed and maintained to support business operations. For example, in a retail organization, ETL may involve extracting customer data from different systems and applications and loading it into a data warehouse.
Governance in data governance refers to the overall framework and… #
For instance, in a healthcare setting, governance may involve establishing policies for managing patient data and ensuring compliance with regulatory requirements.
Information lifecycle management in data governance refers to the process… #
For example, in a financial institution, information lifecycle management may involve managing customer account information from creation to deletion.
Metadata in data governance refers to data that describes o… #
This concept is closely related to data management and data quality, as it involves ensuring that data is properly documented and understood. For instance, in a retail organization, metadata may involve creating a data dictionary to describe customer data and its attributes.
Metrics in data governance refer to the measurements or indicat… #
For example, in a marketing organization, metrics may involve tracking customer engagement or sales conversions to evaluate the effectiveness of targeted advertising campaigns.
Monitoring in data governance refers to the process of tracking an… #
This concept is closely related to metrics and reporting, as it involves using data to inform business strategy and decision-making. For instance, in a financial institution, monitoring may involve tracking data security metrics to identify potential vulnerabilities or threats.
Normalization in data governance refers to the process of transforming… #
This concept is important in data quality and data management, as it involves ensuring that data is reliable and trustworthy. For example, in a retail organization, normalization may involve transforming customer data into a standardized format to improve its quality and consistency.
Policy in data governance refers to a statement or document … #
This concept is closely related to governance and compliance, as it involves ensuring that data is handled in accordance with regulatory requirements and internal policies. For instance, in a healthcare setting, policy may involve establishing policies for managing patient data and ensuring compliance with regulatory requirements.
Procedure in data governance refers to a step #
by-step guide for managing data within an organization, often used to support policies and standards. For example, in a financial institution, procedure may involve establishing procedures for managing customer account information and ensuring compliance with regulatory requirements.
Quality in data governance refers to the degree to which data is <… #
This concept is closely related to data quality and data validation, as it involves ensuring that data is reliable and trustworthy. For instance, in a marketing organization, quality may involve ensuring that customer data is accurate and up-to-date.
Reporting in data governance refers to the process of presenting d… #
This concept is closely related to data visualization and business intelligence, as it involves using data to inform business strategy and decision-making. For example, in a retail organization, reporting may involve creating dashboards to display customer behavior and sales trends.
Retention in data governance refers to the period of time during w… #
For instance, in a healthcare setting, retention may involve storing patient data for a minimum of 10 years to comply with regulatory requirements.
Risk management in data governance refers to the process of identifyin… #
This concept is closely related to data security and compliance, as it involves ensuring that data is handled in accordance with regulatory requirements and internal policies. For example, in a financial institution, risk management may involve identifying and mitigating risks associated with customer data and transactional data.
Security in data governance refers to the practices and procedu… #
This concept is closely related to data protection and access control, as it involves ensuring that sensitive data is properly secured and protected. For instance, in a healthcare setting, security may involve using firewalls and intrusion detection systems to prevent cyber attacks.
Standard in data governance refers to a document or guideline</… #
This concept is closely related to policy and procedure, as it involves ensuring that data is handled in accordance with regulatory requirements and internal policies. For example, in a financial institution, standard may involve establishing standards for managing customer account information and ensuring compliance with regulatory requirements.
Stewardship in data governance refers to the responsibility of ind… #
This concept is closely related to accountability and data quality, as it involves ensuring that data is reliable and trustworthy. For instance, in a healthcare setting, stewardship may involve ensuring that patient data is handled in accordance with regulatory requirements and internal policies.
Validation in data governance refers to the process of checking an… #
For example, in a financial institution, validation may involve checking customer data for errors or inconsistencies before using it for credit scoring or fraud detection.
Value in data governance refers to the importance or worth … #
This concept is closely related to data asset and data management, as it involves ensuring that data is properly managed and maintained to support business operations. For instance, in a retail organization, value may involve measuring the financial value of customer data and using it to inform targeted marketing campaigns.