
Seven Causes of Success in Enterprise Data Management
Many organizations consider Enterprise Information Management (EIM) to be too complex and not worth the design and implementation effort. Adhering to some best practices can
Many organizations consider Enterprise Information Management (EIM) to be too complex and not worth the design and implementation effort. Adhering to some best practices can
As the number of types and amount of unstructured data increase, organizations must develop and implement a consistent plan to manage content and records across
Data governance is much more than a rigid structure for ensuring data privacy. Here are six surprising things data governance initiatives can do for any
Metadata management has become increasingly important for transactional and analytical applications. Creating metadata silos causes numerous challenges; avoid creating siloed metadata Metadata management and its
Effective agile project management must include a focus on the people involved in a project, and the organization’s culture A common theme in society is
Every organization needs a plan for business oversight of all data warehouse / business intelligence / analytics efforts. There are certain areas that should be
Many data governance programs fail for a variety of reasons, most of them are able to be corrected. Review this list and ensure that your
Alaa Mahjoub, M.Sc., Eng. In many cases, for an enterprise to build its digital business technology platform, it must modernize its traditional data and analytics
Effective data integration is an important, precise, detailed, and rigorous analysis process that can offer business value to any organization Effective data integration is much
Data integration can present many challenges to any organization. Understand these situations before starting a data integration effort – success depends on it! Asking ten
Organizations should be aware that there are some simple steps for successful data stewardship Never before has the management of data been so important to
ETL is changing to ELT. This is important since data preparation remains one of the toughest obstacles that a data-aware organization must overcome. Consider these
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