They act as a communication tool to align stakeholders across the organization on priorities and expectations for data handling and usage. Furthermore, KPIs facilitate the demonstration of compliance with data regulations and standards, ensuring data practices meet legal and ethical requirements. In the context of analytics, KPIs help to ensure that the data used is of high quality and relevant, which is critical for generating accurate insights and making informed decisions.
KPI |
Definition
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Business Insights [?]
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Measurement Approach
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Standard Formula
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Cost of Poor Data Quality More Details |
The estimated costs associated with the impact of poor data quality.
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Helps identify the financial impact of data quality issues and prioritize areas for improvement.
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Direct and indirect costs incurred due to inaccurate, incomplete, or inaccessible data.
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Total Costs Related to Data Errors (e.g., operational inefficiencies, missed opportunities) / Total Number of Data Errors Detected
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- Increasing costs of poor data quality may indicate a growing impact on business operations and decision-making.
- Decreasing costs could signal improvements in data management processes and a reduction in the negative effects of poor data quality.
- What specific areas or processes are most affected by poor data quality?
- How does the cost of poor data quality compare to the overall budget for data management and analytics?
- Invest in data quality tools and technologies to identify and rectify data issues at the source.
- Implement data governance policies and procedures to ensure data accuracy, consistency, and completeness.
- Train and educate employees on the importance of data quality and their role in maintaining it.
Visualization Suggestions [?]
- Line charts showing the trend of costs associated with poor data quality over time.
- Pareto charts to identify the most significant contributors to the overall cost of poor data quality.
- High costs of poor data quality can lead to financial losses and missed business opportunities.
- Persistent data quality issues may erode trust in data-driven decision-making and hinder organizational performance.
- Data quality management software such as Informatica or Talend for identifying and resolving data quality issues.
- Analytics platforms like Tableau or Power BI for visualizing the impact of poor data quality on business costs.
- Integrate cost of poor data quality analysis with financial reporting systems to understand its impact on the bottom line.
- Link data quality metrics with operational systems to address data issues at the source and prevent downstream impacts.
- Reducing the cost of poor data quality can lead to more accurate decision-making and improved business performance.
- However, investing in data quality improvements may initially increase costs before delivering long-term benefits.
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Cross-Functional Data Collaboration Rate More Details |
The rate at which different departments collaborate on data-related projects and initiatives.
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Reflects the degree of cross-departmental cooperation in data management and utilization.
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Percentage of collaborative initiatives involving multiple departments or teams focused on data-related projects.
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(Number of Cross-Functional Data Collaborations / Total Number of Data Projects) * 100
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- An increasing cross-functional data collaboration rate may indicate improved communication and alignment between departments, leading to more comprehensive and accurate data-driven decisions.
- A decreasing rate could signal siloed behavior and lack of cooperation, potentially resulting in data inconsistencies and missed opportunities for leveraging insights across the organization.
- Are there specific data-related projects where collaboration is lacking, and what barriers exist?
- How does the cross-functional data collaboration rate align with the overall organizational culture and communication channels?
- Establish clear communication channels and protocols for sharing data and insights across departments.
- Encourage cross-functional teams to work together on data-related initiatives to foster a culture of collaboration.
- Invest in training and development programs to enhance data literacy and understanding across different business functions.
Visualization Suggestions [?]
- Network diagrams to visualize the connections and interactions between different departments in data-related projects.
- Line charts to track the trend of collaboration rates over time and identify any spikes or dips in performance.
- A low cross-functional data collaboration rate may lead to data silos, inconsistent reporting, and missed opportunities for leveraging insights across the organization.
- High collaboration rates without clear objectives and alignment may result in data overload and inefficiencies in decision-making processes.
- Data governance platforms that facilitate collaboration, data sharing, and access control across different departments.
- Project management tools with integrated data management capabilities to streamline collaboration on data-related initiatives.
- Integrate cross-functional data collaboration metrics with performance management systems to align incentives and recognition with collaborative behaviors.
- Link collaboration rates with data quality and integrity assessments to ensure that collaborative efforts result in accurate and reliable insights.
- Improving cross-functional data collaboration can lead to more holistic and informed decision-making, potentially impacting overall organizational performance and competitiveness.
- However, changes in collaboration dynamics may also require adjustments in organizational structures, processes, and roles, impacting the overall work environment and culture.
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Data Access Control Violations More Details |
The number of times unauthorized access to data is attempted or occurs.
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Indicates the effectiveness of data access policies and the potential for unauthorized data exposure.
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Count of incidents where data access controls are bypassed or violated.
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Total Number of Access Control Violations / Total Number of Data Access Requests
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- An increasing number of data access control violations may indicate weaknesses in the security protocols or an uptick in unauthorized access attempts.
- A decreasing trend could signal improved security measures or heightened awareness among users about the importance of data access control.
- Are there specific data sets or systems that are frequently targeted for unauthorized access?
- How does the frequency of access control violations compare with industry benchmarks or compliance requirements?
- Regularly review and update user access permissions to ensure they align with job roles and responsibilities.
- Implement multi-factor authentication to add an extra layer of security for accessing sensitive data.
- Provide ongoing training and awareness programs to educate users about the importance of data access control and the potential risks of unauthorized access.
Visualization Suggestions [?]
- Line charts showing the trend of access control violations over time.
- Bar graphs comparing the frequency of violations across different data sets or user groups.
- Unauthorized access to sensitive data can lead to data breaches, regulatory non-compliance, and reputational damage.
- Frequent access control violations may indicate systemic weaknesses in data security that need to be addressed promptly.
- Data loss prevention (DLP) tools to monitor and prevent unauthorized access to sensitive data.
- Security information and event management (SIEM) solutions to track and analyze access control violations in real-time.
- Integrate access control violation data with incident response and security operations systems to enable swift and coordinated responses to security incidents.
- Link with identity and access management (IAM) systems to ensure consistent enforcement of access policies across the organization.
- Improving data access control can enhance data security and compliance, but may require investment in security technologies and training.
- On the other hand, frequent access control violations can erode trust in the organization's ability to protect sensitive data, impacting customer confidence and business partnerships.
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CORE BENEFITS
- 57 KPIs under Data Governance
- 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)
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Data Accuracy Rate More Details |
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.
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Provides an indication of the reliability and trustworthiness of data assets.
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Percentage of data records that are free from significant errors.
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(Number of Accurate Records / Total Number of Records) * 100
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- Increasing data accuracy rate may indicate improved data management processes or better data quality controls.
- A decreasing rate could signal issues with data entry, data integration, or data validation processes.
- Are there specific data sources or data entry points that consistently result in inaccurate data?
- How does our data accuracy rate compare with industry standards or best practices?
- Implement data validation checks at the point of data entry to catch and correct errors early.
- Regularly audit and clean up existing data to remove inaccuracies and inconsistencies.
- Invest in training for data entry personnel to improve accuracy and attention to detail.
Visualization Suggestions [?]
- Line charts showing the trend of data accuracy rate over time.
- Pie charts comparing accurate data versus inaccurate data by source or department.
- Inaccurate data can lead to flawed analysis, poor decision-making, and potential compliance issues.
- Consistently low data accuracy rates may indicate systemic issues that require significant resources to rectify.
- Data quality management software like Informatica or Talend to automate data validation and cleansing processes.
- Master data management tools to establish and maintain a single, trusted view of data across the organization.
- Integrate data accuracy rate tracking with data governance and compliance systems to ensure alignment with regulatory requirements.
- Link with business intelligence and analytics platforms to understand the impact of data accuracy on decision-making and insights.
- Improving data accuracy can enhance the reliability and trustworthiness of all downstream processes and analyses that rely on the data.
- Conversely, inaccurate data can lead to costly errors, rework, and damage to organizational reputation.
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Data Archiving Efficiency More Details |
The efficiency with which data is archived, including the speed and availability of archived data.
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Highlights the effectiveness of data archiving processes and potential for cost savings.
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Measures the time and resources required to archive data.
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Total Data Archived / Total Time and Resources Spent on Archiving
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- Increasing data archiving efficiency may indicate improved data management processes or better utilization of storage resources.
- Decreasing efficiency could signal issues with data retrieval speed, storage capacity, or data accessibility.
- Are there specific types of data that are frequently accessed from archives?
- How does our data archiving efficiency compare with industry benchmarks or best practices?
- Implement data compression techniques to reduce the storage space required for archived data.
- Regularly review and update data retention policies to ensure only necessary data is being archived.
- Invest in high-speed storage solutions to improve data retrieval times from archives.
Visualization Suggestions [?]
- Line charts showing the trend of data archiving efficiency over time.
- Stacked bar charts comparing the efficiency of archiving different types of data.
- Low data archiving efficiency can lead to increased costs for storage and retrieval.
- Inefficient archiving may result in delays in accessing critical historical data for analysis or compliance purposes.
- Data archiving software like Veritas Enterprise Vault or Commvault for efficient and secure archiving.
- Data management platforms with built-in archiving capabilities, such as IBM InfoSphere or Informatica.
- Integrate data archiving efficiency with data governance processes to ensure compliance with regulatory requirements.
- Link archiving efficiency with data analytics platforms to understand the impact of archived data on analytical insights.
- Improving data archiving efficiency can lead to cost savings in storage and retrieval, but may require initial investment in new technologies.
- Conversely, low efficiency can result in increased operational costs and potential risks related to data accessibility and compliance.
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Data Asset Lifecycle Stage Awareness More Details |
The awareness among stakeholders of the lifecycle stages of various data assets.
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Reveals how well the organization understands and manages the lifecycle of its data assets.
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Percentage of data assets for which the lifecycle stage (creation, storage, usage, archiving, deletion) is known.
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(Number of Data Assets with Known Lifecycle Stage / Total Number of Data Assets) * 100
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- Increasing awareness of data asset lifecycle stages may indicate a growing emphasis on data governance and management.
- Decreasing awareness could signal a lack of focus on data quality and governance, leading to potential data-related issues.
- Do stakeholders understand the different stages of the data asset lifecycle and their importance?
- Are there specific data assets that are frequently mishandled or mismanaged at certain lifecycle stages?
- Provide regular training and education on data asset lifecycle stages and best practices for each stage.
- Implement clear documentation and communication channels for stakeholders to understand the lifecycle stage of each data asset.
Visualization Suggestions [?]
- Flowcharts or process diagrams to visually represent the stages of the data asset lifecycle.
- Stacked bar charts showing the distribution of data assets across different lifecycle stages.
- Low awareness of lifecycle stages can lead to mishandling of data assets, potential data breaches, or compliance issues.
- Inadequate understanding of lifecycle stages may result in inefficient data usage and decision-making.
- Data governance platforms like Collibra or Informatica for tracking and managing data asset lifecycle stages.
- Data catalog tools to provide visibility into the lifecycle stage of each data asset.
- Integrate data asset lifecycle awareness with data quality management systems to ensure that data assets are appropriately managed at each stage.
- Link lifecycle stage awareness with data access and usage policies to enforce proper handling of data assets.
- Improving awareness of data asset lifecycle stages can lead to better data quality, improved decision-making, and enhanced regulatory compliance.
- Conversely, a lack of awareness can result in data-related issues, increased risk, and potential regulatory penalties.
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In selecting the most appropriate Data Governance 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 Data Governance 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.