There is a repeatable series of phases that a data quality initiative will move through as an organization first attempts to achieve a corporate goal and ultimately implements a solution that rectifies the data quality problems that hinder the goal. The IQ Solution Cycle is the series of three phases, Awareness, Quantification, and Implementation that the data quality project team will implicitly or explicitly follow in the course of deploying a solution. From identifying the problem and making the decision to proceed, to solution research and project approval, organizations go through a number of stages before they reach the implementation phase of an information quality initiative. This paper discusses in depth each of the twenty-one stages aided by the actual experiences of Generico, Inc. the fictional name of a real firm. As with all good quality processes and methodologies, the IQ Solution Cycle is repeatable, and the paper concludes with an exposition on “round-tripping” through the cycle, and the benefits derived by the data quality practitioner for simply knowing the cycle exists. THE IQ SOLUTION CYCLE

Corporate household (CHH) refers to the organizational information about the structure within the corporation and a variety of inter-organizational relationships. Knowledge derived from this data is becoming increasingly important for improving data quality in applications, such as Customer Relationship Management (CRM), Enterprise Resource Planning (ERP), Supply Chain Management (SCM), risk management, and sales and market promotion. Extending the concepts from our previous CHH research, we exemplify in this paper the importance of improved corporate household knowledge and processing in various business application areas. Additionally, we provide examples of CHH business rules that are often implicit and fragmented - understood and practiced by different domain experts across functional areas of the firm. This paper is intended to form a foundation for further research to systematically investigate, capture, and build a body of corporate householding knowledge across diverse business applications.

INTRODUCTION: WHY HAVE A DATA QUALITY STRATEGY? Coursing through the electronic “veins” of organizations around the globe are critical pieces of information – whether they be about customers, products, inventories and transactions. While the vast majority of enterprises spend months and even years determining which computer hardware, networking, and enterprise software solutions will help them grow their business, few pay attention to the data that will support their investments in these systems. In fact, Gartner, Inc. contends, “By 2005, Fortune 1000 enterprises will lose more money in operational inefficiency due to data quality issues than they will spend on data warehouse and CRM initiatives (0.9 probability).” (Gartner Inc. T. Friedman April 2004).

Corporate household data refers to both the strict hierarchical structure about and within the corporation, and a variety of interorganizational relationships. Knowledge derived from this data is becoming increasingly important for many purposes ranging from Customer Relationship Management (CRM), Enterprise Resource Planning (ERP), Supply Chain Management (SCM), risk management, to sales and market promotion. Extending the concepts that we have proposed in our previous research, we exemplify in this article how corporate household knowledge and processing are important for various business application areas. Additionally, we illustrate how various business rules may be relevant and identified to capture corporate household knowledge that is implicit, fragmented, and illdefined often understood and practiced by domain experts across functional areas of the firm. This paper has formed a foundation for further research to systematically investigate, capture, and build a body of knowledge for various business applications.

Corporate household data not only refers to the strict hierarchical structure about and within the corporation, but also the variety of inter-organizational relationships. It is becoming increasingly important for many purposes ranging from CRM and ERP applications, to risk management, supply chain management, and marketing. We propose conceptual definitions for corporate household, corporate household knowledge, and corporate household knowledge processor. After describing research challenges and conceptual definitions, we summarize current practices and approaches. We then present a two-part plan: (1) continue our qualitative research to describe the various different sources, views, and purposes for corporate household data, including the rules used in each case; (2) apply the context interchange theory to represent the corporate household data and underlying knowledge and enable the context mediation technology to correctly understand and reason about both the context of the sources and the context of the user's query about corporate household data.