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 1, 2017
Modern marketers are capturing success with an arsenal of advances in data management, software automation, and customer analytics that enable a single view of the customer. New sophisticated practices in omnichannel marketing leverage that view so marketers can serve burgeoning numbers of customers and channels. This Checklist Report drills into the data requirements of modern digital marketing, with a focus on the single customer view and omnichannel marketing.
April 17, 2017
Organizations need a strategy for a modern data platform that can support users who need more than traditional BI and OLAP provide but don’t have the specialized skills of advanced data scientists. This Checklist focuses on six key considerations for modern data platforms that enable more users to benefit from big data through easier to use, visual big data analytics.
March 29, 2017
In a recent TDWI survey, 51 percent of respondents said that enhancing business analysts’ skills was one of their top two strategies for growing their data science competencies. How do businesses get started with machine learning? This Checklist defines machine learning and discusses best practices for the business as it takes the next step on its analytics journey toward using machine learning.
March 15, 2017
Business intelligence (BI) is becoming increasingly democratized, and realizing value from BI tools is no longer limited to an exclusive group with technical expertise. This Checklist Report focuses on how organizations can revise and revitalize enterprise BI in the age of self-service technology.
March 1, 2017
Organizations can unleash the potential of business intelligence (BI) and analytics by empowering users with greater freedom and flexibility in how they work with data. However, it is critical for organizations to improve ROI. This TDWI Checklist covers six strategic issues to address to realize higher ROI with BI and analytics. In some cases, our recommendations focus on traditionally enterprise-level concerns, such as governance. In others, we look at overcoming challenges to enable more users to meet dynamic business demands.
December 2, 2016
Now is the time for the telecommunications industry to devise a strategy for modernization of the data management environment in ways that streamline the end-to-end processes for ingesting, transforming, loading, reporting, delivering, and analyzing data. This Checklist Report describes a vision for the future that includes using an architecture that leverages a hybrid environment combining traditional enterprise data warehouse (EDW) techniques with the ability to deploy augmentation tactics on big data environments, such as those built using the Hadoop ecosystem.
December 1, 2016
Forward-looking businesses need discovery-oriented analytics, but discovery analytics tends to work best with large volumes of raw source data. The data lake enables analytics with big data and other diverse sources. This TDWI Checklist Report discusses many of the emerging best practices for data lakes.
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.