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.
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.
November 29, 2017
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.
November 14, 2017
This Checklist Report examines how seven industries are using analytics to drive value. These industries include finance, insurance, retail, healthcare,manufacturing, utilities, and technology/software/Internet.
November 10, 2017
When we design and develop data management solutions, one of the first and most important steps is to catalog the data that will be captured, managed, analyzed, and shared. This TDWI report will examine the many components and functions of a modern enterprise data cataloging facility.
November 1, 2017
Open source has become popular, especially for big data and data science, because it is a low-cost source community for innovation, which appeals to many data scientists and analytics application developers— especially those who like to code.This TDWI Checklist Report discusses some best practices for evaluating open source analytics.
September 29, 2017
It is a competitive advantage to know more about your customers and to apply this knowledge to marketing, sales, support, and the development of products and services. By gathering together the assortment of big data available to them and applying advanced analytics and data science techniques, organizations can gain a detailed, contextual understanding of customers’ paths to purchase, what types of marketing strategies are most effective, and how customers influence -- and are influenced by -- other customers.
September 26, 2017
The consistent demand for data quality software and new cloud implementation options indicates that more organizations are considering whether to use the cloud to introduce new data quality software, increase their data quality tool users, save on infrastructure costs, minimize the time to rollout of the tools, and build trust in their enterprise’s information assets—the ultimate goal of data quality efforts.