Data stewardship

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  • Data governance and data stewardship strategies and best practices

    This chapter examines key steps of a generic data governance strategy program as it may apply to the CDI Data Hub and discusses the concept of data stewards and their role in assessing, improving and managing data quality. 

  • Data governance: Information ownership policies and roles explained

    Who owns the data? This chapter defines information ownership boundaries and best practices, and outlines the specific roles and responsibilities of all involved in the battle for data governance. 

  • Data quality management: Problems and horror stories

    Data quality management is not easy, as is evidenced by these true horror stories of data quality gone wrong. Learn from (and wince at) these mistakes to avoid problems with your own data quality projects. 

  • Top five data management buzzwords

    Here you can find clear definitions of the top buzzwords in enterprise data management, including service-oriented architecture (SOA), master data management (MDM), customer data integration (CDI), data governance and compliance. 

  • Data quality management: Follow the doctor's orders

    Get expert recommendations for data quality management in business intelligence and data warehousing projects. 

  • DAMA keynote: Five data management trends

    Jill Dyche, keynote speaker at the annual DAMA conference, called this "the decade of the data steward" and outlined five trends changing the industry. 

  • Data quality issues management

    Identifying data quality issues is only the first step in solving them. In this chapter, Jack Olson explains the need for a data quality assurance management system -- one that asesses the impact and causes of poor data quality. 

  • Data quality assurance

    This chapter outlines the basic elements of a data quality assurance program. It focuses on data accuracy, a single dimension of data and information quality. 

  • Data cleansing: The business impact of dirty data

    Look at the business impact of leaving less-than-ideal data in place to help you determine what data should be cleansed. 

  • Data quality management begins with data governance

    Data quality management begins with data governance -- that means having the right strategy, people and application.