Investment Metrics

Typical Types of Performance Measures for Technical Projects

This document includes a description of metrics that can be used to measure the investment and payback impacts of your data warehouse efforts. There are two tables included in this document. The first table includes metrics used to manage data warehousing development projects investments. The second table contains sample metrics to assist in measuring the payoffs and returns from the use of data warehouse applications. The tables include the metrics, a description of how the metrics are used and the formula for how the metrics are calculated.

Examples of Development Project Performance Metrics for use in Controlling Investments

This table represents typical performance measurements for technical projects that enable better management and control of the project. These performance measures can be used to improve processes, guide decisions and management priorities and manage the daily operations of the project. This table provides examples of technical, schedule and cost performance metrics that help control the project investments.

Investment Metrics	Description/ Comments	Formula
Technical Performance Metrics
Employee turnover rate	This metric is important to aid in determining the extent to which the project is or is not retaining development staff knowledge.	Employee turnover rate = # Start Employees at End of Project x 100 # Employees at Start Project

Percentage of (%) Requirements Met	This metric is used to track incremental increases in the number of software requirements that are being met by the system. By observing cumulative changes you can analyze, over time, the extent to which the project is progressing in meeting user’s needs.	% Requirements Met = Cumulative Total # Requirements Met x 100 Cumulative Total # Requirements

Percentage of (%) Cumulative Software Defects Corrected	This metric represents the number of software defects that were corrected in comparison to the actual number of software defects found and reported.	% Cumulative Software Defects Corrected = Cumulative Total # Software Defects Corrected x 100 Cumulative Total # Software Defects
Schedule Performance Metrics
Schedule Performance Index (SPI)	This metric measures the efficiency of schedule control on development projects. Measured on a scale from 0 to 1.0, perfect schedule control is reflected in a SPI of 1.0. For example a SPI of .89 means that for every dollar spent on the project 89 cents of the value was derived with the remaining 11 cents reflecting schedule overrun.	Schedule Performance Index (SPI) = Earned Value ($) = from 0 to 1 Planned Value ($)

Percentage of (%) On Time Deliveries	This metric measures the percentage of deliverables met in accordance with schedules agreed to between the customer and the development team in a project plan or updates to the plan.	% On Time Deliveries = Actual Deadlines Met During a Given Timeframe x 100 Expected Deadlines Planned for the Same Timeframe

Cycle time for new product development	This metric measures how many products were completed within a certain timeframe based upon expected development plans of new products in comparison to actual development of new products. The expected development cycle should factor in products that can be easily updated to incorporate additional tasks, revise existing tasks and remove tasks no longer required by the customer.	Cycle time for new product development: Actual Development = from 0 to 1 Expected Development
Cost Performance Metrics
Cost Performance Index	The CPI index measures the efficiency of cost control on development projects. Measured on a scale from 0 to 1.0, perfect cost control is reflected in a CPI of 1.0. For example a CPI of .89 means that for every dollar spent on the project 89 cents of the value was derived with the remaining 11 cents reflecting cost overrun.	Cost Performance Index (CPI) Formula = Earned Value = BCWP Actual Costs ACWP Perfect Cost performance = 1.0 [$1.00 actual costs = $1.00 earned value]

Estimated Costs at Completion (EAC)	This metric statistically forecasts the final estimated costs at completion (EAC) on a project using earned value data. This is derived from three variables: The value of the project work remaining. This is measured as the total project budget minus the earned value accomplished. The performance efficiency factor that can be represented by the cumulative CPI, if you are viewing the project from a cost perspective, or the CPI times SPI, if you are viewing the project from a schedule perspective. The addition of the total of actual costs incurred to date.	Estimated Costs at Completion (EAC) Formulas: Cumulative CPI EAC: (Total Budget – Earned Value) + Actual Costs = EAC$ Cumulative CPI Cumulative CPI x SPI EAC: (Total Budget – Earned Value) + Actual Costs = EAC$ Cumulative CPI x SPI

To Complete Performance Index (TCPI)	This metric is used to determine what cost performance factor will be needed to complete all the remaining work according to a financial goal set by management. The value of the funds remaining can be a variable amount reflecting the financial goals set by management. What-if scenarios can be explored to set realistic or ambition financial goals for the project. The value for the funds can be represented by either the: Total Budget at Completion (BAC) Estimate at Completion (EAC) or Fixed Price Ceiling of a contract, or for any value, always less the actual costs that have incurred to date (Ceiling – Actual Costs).	To Complete Performance Index (TCPI): Work Remaining = TCPI Funds Remaining Work Remaining = Total Budget – Earned Value Funds Remaining = BAC or EAC or (Ceiling – Actual Costs)

Productivity Constant	This metric measures the overall productivity of the entire team during the software development. The productivity constant is an overall number that measures the effectiveness of the organization structure, such as the ability of management, the skill and experience of the team members, the effectiveness of the methods, tools, techniques and software employed, the programming language used, the availability and response time of the computer resources and the complexity of the application. The productivity constant provides a way to measure if money spent has become a productive investment. If the productivity constant is measured over the investment cycle you can find out whether the investments are paying proper dividends. This is defined by the equation for Product.	Productivity Constant = Source Statements Effort x Development Time Product = Productivity constant x Effort x Time Software Development: Product (measured in source statements) = Productivity constant x person-years of Effort x Years of Development Time

Product Integrity (PI) Index	This metric can be used to quantify customer satisfaction using multiple metrics. The PI Index ranges from zero to one. This index is typically used on a 0 to 1 measurement scale as follows: 0 = Poor Performance .5 = Below expected performance 1.0 = At or above expected performance In this example five attributes are measured to quantify customer satisfaction during the development phase: A1 = Fulfills customer needs A2 = Can be easily and completely traced through its life cycle A3 = Meet specified criteria A4 = Meets cost expectations A5 = Meets delivery expectation When analyzing performance measures in a project you should look at multiple measures together in order to get the whole picture.	Product Integrity (PI) Index = n å w_i ² at_i ² i =1 n å w_i ² (maximum [at_i}² i =1 at_i = product integrity attribute n = number of product integrity attributes w_i = weighting factor for attribute at maximum [at_i} = maximum value of at_i. For example if: PI = (A1 + A2 + A3+ A4 +A5) N N = Normalization Factor A = Attribute (Metrics)

Examples of Business Performance Metrics for use in Controlling Business Performance Returns

This table represents typical business performance measurements for technical projects that control business performance returns of the project. These Performance measures can be used to measure the successes and failures of the project based upon customer feedback and overall performance. This table provides examples of business metrics used during the system implementation phase that impact decisions, actions and business outcomes.

Business Metrics	Description /Comments	Formula
Return on Management	This metrics measures how technology is benefiting the management decisions. ROM assumes that managers are the processors of all of a company’s inputs and ultimately account for returns instead of capital investments or technology.	Return on Management for Commercial Entity = Revenues – [Management Cost + Operations Cost + Shareholder Value Added + Purchases and Taxes Return on Management for Public Sector Entity = Revenues – [Management Cost + Operations Cost + Purchases]

Percentage of (%) Reduction in Labor Costs	This metric measures the amounts of labor costs saved as a result of the data warehouse system post implementation.	% Reduction in Labor Costs = LCBI - LCAI x 100 LCBI LCBI = Labor Cost Before Implementation LCBA = Labor Cost After Implementation

Increases in Revenue	Demonstrated by increase in demand and usage of the system developed. For example, figures show that there is an increase in the revenue based on more users purchasing the system.	Increases in Revenue= Current Revenue – Initial Revenue x 100 Current Revenue