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.
November 10, 2016
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.
November 8, 2016
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.
November 7, 2016
This checklist defines data security and data-centric security and discusses best practices and enabling technologies to help make data more secure.
November 3, 2016
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.
October 12, 2016
TDWI research indicates that organizations view analytics as an opportunity to help in better decision making, to better understand customers, and to improve business performance.
October 12, 2016
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.
October 10, 2016
Marketers today understand that they can no longer send out mass messaging to an entire customer list for a campaign. They need to be much more selective. This Checklist explores how artificial intelligence can enhance marketing analytics and to help companies both better understand their customers and deliver a great customer experience.
October 1, 2016
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.
July 21, 2016
This TDWI Checklist Report provides seven best practices for introducing, implementing, and leveraging streaming analytics as a component of an enterprise business intelligence and analytics architecture. These recommendations will help organizations understand where streaming analytics fits into their analytics frameworks and how it can be integrated with the complementary components of their organization’s analytics environment.