TDWI Checklist Reports
TDWI Checklist Reports provide an overview of success factors for a specific project in business intelligence, data warehousing, or a related data management discipline. Companies may use this overview to get organized before beginning a project or to identify goals and areas of improvement for current projects.
May 8, 2018
Machine learning is being used today to solve well-bounded tasks such as classification and clustering. Note that a machine learning algorithm learns from so-called training data during development; it also learns continuously from real-world data during deployment so the algorithm can improve its model with experience. This report will drill into the data, tool, and platform requirements for machine learning with a focus on automating and optimizing ML's development environment, production systems, voracious appetite for data, and actionable output.
March 30, 2018
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.
March 30, 2018
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.
March 30, 2018
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.
March 6, 2018
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.
February 21, 2018
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.
February 1, 2018
Data products are not a new idea; data aggregators have been producing purchasable data sets for decades. However, as organizations have become motivated to be “data driven,” the concept of a “data product” has rapidly morphed into different shapes, including packaged data sets, lightweight API-based services, directly connected end-user visualizations, and full-blown access to hosted reporting and analytics dashboards.
December 22, 2017
This TDWI Checklist Report discusses the ways in which design thinking can produce more effective BI and analytics solutions and reduce user frustration with ineffective tools.
December 8, 2017
As the importance of self-service solutions for BI, analytics, and data preparation continues to grow, the emphasis is no longer only on centralized, full-time data professionals and their institutional knowledge. We also must find a way to support the business’ needs with better-documented data assets. For many organizations, this is a data catalog.