Typical software metrics and measurements (e.g. code coverage, McCabe complexity, OO metrics, etc) target the technical professionals in a software development organization. While this is useful to the practitioners in the trenches, the managers and executives of these organizations face a broader set of issues. In order to make better decisions, the collection of business and software ... [Show full abstract] engineering data and its regular use throughout the product life cycle is essential. The product life cycle typically consists of requirements, design and development, service and support during which various pieces of information are collected. This paper presents (i) examples of data and the metrics currently available in a typical large software organization (ii) highlights some of the issues in integrating these data sources and the lessons learned (iii) analyzes the data and presents models that enable management to derive relationships among the inhibitors and drivers of these metrics and (iv) presents a framework for a decision support system.