This guidebook provides the answers with a series of detailed sections including essential activities such as planning, forecasting, risk management, team selection, communication and collaboration. 

Banks need to get smarter, attract the right customers, implement customer level risk management, and implement risk adjusted customer relationship pricing and all of this is dependent on trusted data. This paper examines the impact of unreliable data on retail banks. It then defines the requirements needed to guarantee data reliability in banking. 

This paper examines the impact of unreliable data on manufacturing companies. It then defines the requirements needed to guarantee data reliability in manufacturing and offers a practical approach to creating and governing that data. 

Too often, data governance teams rely on existing measurements as the metrics used to populate a data quality scorecard. But without a defined understanding of the relationship between specific measurement scores and the business's success criteria.. 

Operational data governance is the process of evaluating data to see if data effectively satisfies the organization's business needs. This is measured with a "data quality scorecard", a management tool for monitoring organizational performance. 

This paper discusses the role of data governance programs in supporting compliance efforts, and presents an ROI formula you can use in those circumstances where it's important to quantify the value of data governance efforts. 

Read how one of the largest banks in the southeast chose DataFlux dfPower® Studio to review and compare multiple data sources simultaneously through data quality and data integration workflows. 

Operational data governance is the manifestation of the actionable processes and protocols necessary to ensure that an acceptable level of confidence in the data effectively satisfies the organization's business needs. 

In this program Gartner Research Vice President Ted Fredman and DataFlux President and CEO Tony Fisher discuss the data quality issues facing business today as well as the solutions that can solve these problems. 

This paper focuses on what is master data, why it is needed, how to get started in managing it, and methodologies for implementing master data management.