Data science uses systematic methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. Data science requires high-quality data that is trusted by the organization and data scientists. Many organizations focus their data governance programs on improving data quality resulting. These three concepts (governance, science, and quality) seem to be made…

A worthwhile data governance framework includes the core component of a successful program as viewed by the different levels of the organization. Each of the components is addressed at each of the levels providing insight into key ideals and terminology used to attract participation across the organization. A framework plays a key role in setting…

Organizations are investing heavily in becoming data-centric. Data governance practitioners must begin to deploy effective data governance techniques to support these investments. One of these techniques is to tackle the problem of convincing stakeholders that data governance is necessary. This webinar will help you address that challenge. Join Bob Seiner for this RWDG webinar where…

To take a “ready, aim, fire” tactic to implementing data governance, many organizations assess themselves against industry best practices. The process is not difficult or time consuming and can directly assure that your activities target your specific needs. Best practices are always a strong place to start. Join Bob Seiner for this popular RWDG topic…

Metadata is a tool that improves data understanding, builds end-user confidence, and improves the return on investment in every asset associated with becoming a data-centric organization. Metadata’s use has expanded beyond “data about data” to cover every phase of data analytics, protection, and quality improvement. Data governance are connected at the hip in every way…