 Master Data Management KPIs List

• Average Database Availability Time – We strive to increase average database availability time by minimizing unexpected system malfunctions and ensuring the database remains functional.
• Mean Time to Repair – We strive to reduce mean time to repair by resolving system interruptions more quickly and efficiently after an unexpected failure.
• Database Query Response Time – We strive to reduce database query response time by optimizing performance to handle queries under heavy load and eliminating bottlenecks.
• Backup and Recovery Time – We strive to reduce backup and recovery time by improving disaster recovery processes to minimize downtime after data loss or system failure.
• Database Downtime (Planned vs Unplanned) – We strive to reduce unplanned database downtime by maintaining a lower ratio of unplanned to planned downtime, ensuring a more stable and reliable database system.
• Storage Utilization Efficiency – We strive to increase storage utilization efficiency by optimizing the use of total database storage, reducing the need for additional storage investments.
• User Connection Time – We strive to reduce user connection time by improving network configuration and database scalability for faster user access.

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• Percentage of Accounts with Incomplete or Missing Data – We strive to reduce the percentage of accounts with incomplete or missing data by ensuring better data collection and accuracy in company systems.
• Percentage of New Accounts Setup with Missing or Incomplete Data – We strive to reduce the percentage of new accounts setup with missing or incomplete data by improving data entry processes for new clients.
• Percentage of New Products Setup with Missing or Incomplete Data – We strive to reduce the percentage of new products setup with missing or incomplete data by streamlining product registration and ensuring data completeness.
• Percent of New Vendors Setup with Missing or Incomplete Data – We strive to reduce the percentage of new vendors setup with missing or incomplete data by improving the accuracy of merchant account entries.
• New Account Setup Cycle Time – We strive to reduce the new account setup cycle time by speeding up the registration and approval process for new client accounts.
• New Accounts Setup per FTE (Monthly) – We strive to increase the number of new accounts setup per FTE (monthly) by optimizing the efficiency of the account creation staff.
• New Product Setup Cycle Time – We strive to reduce the new product setup cycle time by minimizing the time required to register and approve new products.
• New Products Setup per FTE (Monthly) – We strive to increase the number of new products setup per FTE (monthly) by improving the productivity of the product setup staff.
• New Vendor Setup Cycle Time – We strive to reduce the new vendor setup cycle time by speeding up the process from data collection to approval for new merchant accounts.
• New Vendors Setup per FTE (Monthly) – We strive to increase the number of new vendors setup per FTE (monthly) by enhancing the efficiency of the vendor setup team.
• Total Number of Customer Accounts Serviced – We strive to increase the total number of customer accounts serviced by improving the correction of inaccuracies in client account data within a month.

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• Dashboard Usage Percentage – We strive to increase dashboard usage percentage by encouraging more active access and utilization of control panels by management staff.
• Report Production Cycle Time – We strive to reduce report production cycle time by minimizing the number of business days needed to fulfill report requests and generate accurate reports.
• Data Accuracy Rate – We strive to increase the data accuracy rate by ensuring a higher percentage of data in BI reports and dashboards is free from errors, making it reliable for decision-making.
• Self-Service BI Adoption Rate – We strive to increase self-service BI adoption rate by empowering more users to generate their own reports and analyses independently, reducing reliance on IT or analysts.
• Time to Insight – We strive to reduce time to insight by enabling faster delivery of actionable insights from BI reports and dashboards, leading to more agile decision-making.
• Data Latency – We strive to reduce data latency by shortening the time between data updates in source systems and its availability for analysis, improving the ability to make real-time decisions.
• Report/Dashboard Error Rate – We strive to reduce the report/dashboard error rate by minimizing inaccuracies and technical issues, thereby enhancing the trust and usability of BI reports and dashboards.

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• Collaboratively establish and define the Master Data decision rights and accountability framework – This KPI tracks and measures the Master Data manager’s ability to collaboratively establish and define the Master Data decision rights and accountability framework. This framework is designed to ensure appropriate behavior in the valuation, creation, storage, use, archival, and deletion of information – including process, roles, standards, and metrics.
• Define, develop, clearly and effectively communicate, implement and maintain and, above all, champion the IT Master Data Management (MDM) program at an enterprise level – This KPI tracks and measure’s the Master Data manager’s ability to define, develop, clearly and effectively communicate, implement and maintain and, above all, champion the IT Master Data Management (MDM) program at an enterprise level. The higher this metric, the greater the manager’s ability to advocate for the MDM program.
• Take ownership of the organization’s Data Governance Committee (DGC) and its processes to work with the critical functional system and data ownership – This KPI tracks and measures the Master Data manager’s ability to take ownership of the organization’s Data Governance Committee (DGC) to work with the critical functional system and data ownership for the continued review and confirmation of the data asset catalog and integration roadmap.
• Align integration and data needs with IT Security & Compliance’s IAM platform and compliance oversight responsibilities. – This KPI tracks and measures the Master Data manager’s ability to align integration and data needs with the IT Security & Compliance’s Identity Access Management (IAM) platform. Secondly, the master data integration must comply with Corp GxP Compliance oversight responsibilities and all other Master Data management related efforts.
• Demonstrate flexible, strong, influential, and effective leadership traits – The Master Data Manager must demonstrate flexible, powerful, and influential leadership traits. This KPI tracks the extent to which the manager shows these traits. The higher this metric, the greater the extent to which the manager can implement the compliance requirements needed to protect and secure the master data.

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