RESEARCH & RESOURCES

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.


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TDWI Checklist Report | Modern Data Integration and Data Quality Practices for Digital Business Requirements

August 2, 2017

Data integration and data quality are technical disciplines, but there’s more required than technology. DI/DQ must also coordinate with and align to business processes and goals. The assumption is that businesspeople should be involved in defining quality metrics, standards, and process rules. This report will drill down into the modern best practices associated with emerging data sources, data platforms, and business use cases.


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TDWI Checklist Report | Six Strategies for Enabling Users to Advance Faster with BI and Analytics

July 27, 2017

All organizations, no matter how big their budget, must overcome barriers in order to realize value from data faster. Those that have historically experienced centralized, IT-centric BI implementations must transition to flexible environments that embrace the increased use of self-service technologies.


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TDWI Checklist Report | Using Hadoop for Data Warehouse Optimization

June 29, 2017

Augmenting the conventional EDW design with Hadoop and Hive can help optimize your EDW by expanding usability, improving performance, improving results, and reducing overall costs.


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TDWI Checklist Report | Architecting a Hybrid Data Ecosystem

June 2, 2017

When numerous diverse data platforms are integrated for multiple use cases, it is called a hybrid data ecosystem (HDE), a concept invented and popularized by Enterprise Management Associates (EMA) in 2012. An HDE provides options for rapidly diversifying data and its business use. Despite the extreme complexity and challenges, users are succeeding with HDEs. This TDWI Checklist Report drills into the data requirements of the HDE with a focus on the role of data virtualization.


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TDWI Checklist Report | Speeding the Path to Visual, Easier-to-Use Business Analytics

June 1, 2017

The growing adoption of cloud computing and software-as-a-service (SaaS) is merging with easier-to-use business intelligence and analytics tools to allow more users to interact with and analyze data. This Checklist Report discusses seven recommendations to help organizations be successful sooner in enabling a data-driven culture through visual, easier-to-use business analytics.


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TDWI Checklist Report | New Data Practices for a Single Customer View and Omnichannel Marketing

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.


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TDWI Checklist Report | New Strategies for Visual Big Data Analytics

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.


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TDWI Checklist Report | Machine Learning for Business: Eight Best Practices to Get Started

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.


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