This first in a new series of reports offers focused research and analysis of trending analytics, business intelligence, and
data management issues facing organizations. TDWI Pulse Reports are designed to educate technical and business professionals and aid them in developing strategies for improvement.
Users ignore the modernization of deep warehouse infrastructure at their peril. Without it, they may achieve complete, clean, and beautifully modeled data, but without the ability to scale to big data, iterate data models on the fly, enable flexible self-service access, operate continuously and in real-time (as warehouses must in global businesses), and handle new data types and workflows for advanced analytics.
The foundation of a successful IoT implementation is a technical architecture that blends network connectivity with an information architecture for streaming, ingesting, filtering, and capturing data. This checklist explores some fundamental aspects of the data architecture necessary for IoT success.
As organizations collect and analyze increasing amounts of data, they are turning to the data lake as the platform to perform more advanced analytics such as machine learning. This TDWI Checklist Report presents best practices for advanced analytics on a data lake.
This past year saw BI salaries continue their steady rise. Read more in the 2018 TDWI Salary, Roles, and Responsibilities Report.
Businesses can only seize new data-driven opportunities if they recognize sensitive data and handle it responsibly. This report focuses on how targeted improvements to specific data management best practices and technology can contribute significantly to your success with GDPR compliance, as well as data governance and data-driven programs in general.
A lake or cloud can breathe new life into established enterprise data architectures (data warehouses, marketing channel data, digital supply chains) or create new and different ones (analytics labs and sandboxes, ecosystems of cloud-based operational applications). This report discusses the leading data management (DM) best practices you need for data lakes to be successful when deployed in the cloud.
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