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.

Checklist cover Emerging Design Patterns

TDWI Checklist Report | Emerging Design Patterns for Data Management

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.

Checklist thumb Data Centic Security

TDWI Checklist Report | Data-Centric Security: Seven Best Practices for Protecting Your Most Sensitive Data

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.

Syncsort Checklist cover image

TDWI Checklist Report | Building a Data Lake with Legacy Data

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.

TDWI Checklist Report Series: Steps to Achieving Big Data Analytics Maturity

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.

Informatica Data Lake Management Checklist Cover

TDWI Checklist Report | Best Practices for Data Lake Management

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.

SAS Predictive Analytics for Marketing Checklist cover image

TDWI Checklist Report | Marketing Analytics Meets Artificial Intelligence: Six Strategies for Success

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.

Talend Governing Big Data Checklist thumb

TDWI Checklist Report | Governing Big Data and Hadoop

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.

Streaming Analytics Checklist Report Cover Image

TDWI Checklist Report | Seven Best Practices for Streaming Analytics for Real-Time Action

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.

Trillium Operational Data Quality Checklist Cover

TDWI Checklist Report | Assuring the Quality of Operational Data

June 22, 2016

Most definitions of operational excellence assume that localized improvements have a global impact. In that spirit, this TDWI Checklist Report provides recommendations for incrementally and continuously improving the quality of operational data, which in turn contributes to an organization’s drive toward operational excellence.

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