Organizations dependent on big data for a wide range of business decisions need data quality management that can improve the data so it is fit for each desired purpose. This TDWI Checklist Report offers six strategies for improving big data quality.
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.
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.
Time spent cleaning data is eating away at the time available for analysis. What steps can your enterprise take to get analytics back on track?
For organizations struggling to modernize their DM efforts, the intelligent integration hub provides a flexible and scalable foundation. This TDWI report examines the attributes and use cases of the intelligent integration hub.
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