Creating a framework to ensure the confidentiality, quality, and integrity of data – the core meaning of data governance – is essential to meet both internal and external requirements, such as financial reporting, regulatory compliance, and privacy policies. At its best, data governance roots out risk – both business and compliance risk – by increasing oversight. This white paper provides seven steps for taking such an approach, concluding with a real world example, taking an incremental approach using a repeatable framework that is a practical, proven strategy that any size organization can implement to suit their immediate and long-term needs and budget.
Data quality – the measure of data accuracy, completeness, and consistency across a business – has
become the core focus of information management efforts among many of today’s organisations.
Problems with data quality continue to plague corporations of all types and sizes. In this paper, we will discuss some techniques companies can implement to enhance data quality across the entire enterprise. We will also highlight data quality management solutions, which provide businesses with the ability to effectively and economically enhance the correctness, completeness, and consistency of information in each and every system within their technology infrastructure.
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