The goal of data governance is not just to clarify who “owns” data but also to optimize its value. The data itself
is merely the means to the desired end of improved business performance. Accordingly, the responsibility for
data governance efforts should fall at least as much on business as it does on IT—and preferably more.
This paper is intended to help data governance evangelists in both business and IT generate the momentum
they need to make data governance an enterprise-wide priority.
This report presents the findings of a survey jointly conducted by the International Association for Information and Data Quality (IAIDQ) and the Information Quality Program at the University of Arkansas at Little Rock (UALR-IQ) between March 19 and April 20, 2012. The purpose of the survey was to better understand the current state of information and data quality programs and practices in organizations around the world. The report provides valuable insights for information/data quality practitioners, managers and leaders, and the academic community. It is a great resource to help evaluate existing conditions, identify best practices to emulate, and set the agenda for future growth of the discipline.
This white paper discusses how the data conversation is changing from philosophical questioning to hard-core tactics, from “What do we need?” to “Where do we start?” It describes the components of information governance that inform the right strategy, and – more important – give companies a means of determining where and how to begin their information governance journeys.
The Data Governance Institute's (DGI) Data Governance Framework is a logical structure for classifying, organizing, and communicating complex activities involved in making decisions about and taking action on enterprise data. This whitepaper describes the framework in detail and describes how its components are used in different types of Data Governance programs.
Key to successful data governance is the management of metadata—the frame of reference giving data its
context and meaning. Effectively governed metadata provides a view into the flow of data, the ability to perform
an impact analysis, a common business vocabulary and accountability for its terms and definitions, and finally
an audit trail for compliance. The management of metadata becomes an important capability enabling IT
to oversee changes while delivering trusted, secure data in a complex data integration environment. Good
metadata management tools, then, play a central role in holistic data governance.
This paper helps inform those organizations interested in developing a master data management program regarding the methods that should be used to govern the program once it is in place.
Many of the challenges to master data management (MDM) are organizational and collaborative issues—not technical ones. Luckily, many of MDM’s challenges can be remedied by a well-designed and mature program for data governance (DG). There are good technology and business reasons why master data management needs data governance. This TDWI Checklist Report drills into seven of these reasons as well as use cases and organizational situations where DG and MDM work well together.
Using Data Quality to Start and Sustain Data Governance - By TDWI
This TDWI Checklist Report makes a case for applying data quality (DQ) techniques and best practices to data governance (DG) as a way of kick-starting and sustaining data governance.
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