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 22, 2017
This TDWI Best Practices Report examines how organizations become data-driven, including patterns for building out infrastructure for managing data and driving analytics. It also examines the best practices of those organizations that are data-driven across three areas we believe are important: technology, analytics, and organization.
December 22, 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.
December 8, 2017
User organizations facing new and future requirements for big data, analytics, and real-time operation need to start planning today for the data warehouse of the future. This Checklist Report drills into seven key recommendations for solution design, listing and discussing many of the new vendor and open source product types, functionality, and user best practices that will be common in the near future, along with the business case and technology strengths of each.
This Checklist Report drills into some of the emerging design patterns and platforms for data that modern data-driven organizations are embracing. The goal of the report is to accelerate users’ understanding of new design patterns and data platforms so they can choose and use the ones that best support the new data-driven goals of their organizations.
This checklist defines data security and data-centric security and discusses best practices and enabling technologies to help make data more secure.
This checklist will help you and your team plan and launch successful data lake projects with your legacy data sources. It reviews the critical success factors for these projects as well as the risks and issues to mitigate.
A data lake ingests data in its raw, original state, straight from data sources, with little or no cleansing, standardization, remodeling, or transformation. These and other data management best practices can then be applied flexibly as diverse use cases demand.
Most data lakes are built atop Hadoop, which enables a data lake to capture, process, and repurpose a wide range of data types and structures with linear scalability and high availability.
The cloud is becoming a mature platform for data management, integration, business intelligence (BI), and analytics. Download this report for an examination of organizations’ experiences with and plans for cloud BI and analytics, new cloud models, and what organizations should consider when moving to the cloud.
Big data presents significant business opportunities when leveraged properly, yet it also carries significant business and technology risks when it is poorly governed or managed.
Individual, Student, & Team memberships available.