In most of the Database guide papers, eight performance-based metrics were used to communicate areas of concern to the data warehousing vendor. Also, the ninth one was developed as a weighted average measure using four of the eight metrics, which were deemed the most critical to the organization assessing data warehouse performance to determine how the vendor stacked up relative to data quality and systems quality.
The nine metrics that were developed to measure Solectron's, which is a major contract manufacturer in the electronic industry, data warehouse performance are;
1. Completeness
Total Complete = [((#Recordsi)/(Total #Records Expectedi) x (#Records Expectedi/Total #Records in all tables)) x 100.
This metric is supposed to serve the firm better than a simple binary yes/no in measuring the weighted average of each table in the data warehouse. It also measures the percentage completion during an Extract -Transfer- Load (ETL) and/or refresh process. This metric also, and helps identify any possible SQL problems within a particular table.
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Financial services data warehouse jon glassman
English: Federal funds paid for project2. Connectivity
Response Time = #Users x (RTD% + TR%)
Where Run Time Duration (RTD) in minutes = Hardware/DW size
Transfer Rate (TR) = Actual Transfer Rate/Optimal Transfer Rate
This metric measures connectivity by enabling the firm to identify and isolate underlying issues
including ISP transfer rates, data sources, and hardware issues.
3. Data Integrity
Data Quality Tool Rate/Manual = % Accuracy
This metric measures the avoidance of the tedious, resource intensive method done in the past.
4. Usage Data from the Data Warehouse
Usage Rate on Data = Most frequently used/Total
This metric tracks the usage rate of tables, reports, and the overall data warehouse to identify any trends and/or problems that may occur, particularly during peak operating times.
5. On-Time delivery
This current metric is defined as: tables are on time if they are delivered by 7:00AM...