Carol V. Brown

Organizations are becoming increasingly serious about the notion of "data as an asset" as they face increasing pressure for reporting a "single version of the truth." In a 2006 survey of 359 North American organizations that had deployed business intelligence and analytic systems, a program for the governance of data was reported to be one of the five success "practices" for deriving business value from data assets. In light of the opportunities to leverage data assets as well ensure legislative compliance to mandates such as the Sarbanes-Oxley (SOX) Act and Basel II, data governance has also recently been given significant prominence in practitioners' conferences, such as TDWI (The Data Warehousing Institute) World Conference and DAMA (Data Management Association) International Symposium. The objective of this article is to provide an overall framework for data governance that can be used by researchers to focus on important data governance issues, and by practitioners to develop an effective data governance approach, strategy and design. Designing data governance requires stepping back from day-to-day decision making and focusing on identifying the fundamental decisions that need to be made and who should be making them. Based on Weill and Ross, we also differentiate between governance and management as follows: • Governance refers to what decisions must be made to ensure effective management and use of IT (decision domains) and who makes the decisions (locus of accountability for decision-making). • Management involves making and implementing decisions. For example, governance includes establishing who in the organization holds decision rights for determining standards for data quality. Management involves determining the actual metrics employed for data quality. Here, we focus on the former. Corporate governance has been defined as a set of relationships between a company's management, its board, its shareholders and other stakeholders that provide a structure for determining organizational objectives and monitoring performance, thereby ensuring that corporate objectives are attained. Considering the synergy between macroeconomic and structural policies, corporate governance is a key element in not only improving economic efficiency and growth, but also enhancing corporate confidence. A framework for linking corporate and IT governance (see Figure 1) has been proposed by Weill and Ross. Unlike these authors, however, we differentiate between IT assets and information assets: IT assets refers to technologies (computers, communication and databases) that help support the automation of well-defined tasks, while information assets (or data) are defined as facts having value or potential value that are documented. Note that in the context of this article, we do not differentiate between data and information. Next, we use the Weill and Ross framework for IT governance as a starting point for our own framework for data governance. We then propose a set of five data decision domains, why they are important, and guidelines for what governance is needed for each decision domain. By operationalizing the locus of accountability of decision making (the "who") for each decision domain, we create a data governance matrix, which can be used by practitioners to design their data governance. The insights presented here have been informed by field research, and address an area that is of growing interest to the information systems (IS) research and practice community.