Download TDWI Infographics for illustrated snapshots of the latest pertinent findings and vital statistics from TDWI Research. Then access the associated Best Practices Reports for more in-depth information on how to take advantage of these trends in your organization.
June 20, 2019
This TDWI Best Practices Report explains in detail what cloud data management (CDM) is and does so data professionals and their business counterparts can understand what CDM can do for them and how they might organize a successful program.
February 13, 2018
This TDWI Best Practices Report examines how organizations become data-driven, including patterns for building out infrastructure for managing data and driving analytics. It also examines the best practices of those organizations that are data-driven across three areas we believe are important: technology, analytics, and organization.
September 7, 2017
Organizations need to accelerate the pace at which they can realize business value from data. Their focus is on improving “time to value,” which is the length of time it takes from the beginning of a project to the delivery of anticipated business value. In TDWI Best Practices Report: Accelerating the Path to Value with Business Intelligence and Analytics, we look at multiple factors impacting the ability of organizations to quickly derive greater value from data and analytics, including the organizational issues, practices, and development methods that are often just as important as keeping pace with technological innovation. Here are several of the key survey results.
June 14, 2017
The TDWI 2017 Salary, Roles, and Responsibilities Report captures key information from the people and teams who built and maintained business intelligence, analytics, and data management solutions during the 2016 calendar year.
May 8, 2017
When designed well, a data lake is an effective data-driven design pattern for capturing a wide range of data types, both old and new, at large scale. Organizations are adopting the data lake design pattern (whether on Hadoop or a relational database) because lakes provision the kind of raw data that users need for data exploration and discovery-oriented forms of advanced analytics. In the recent TDWI Best Practices Report: Data Lakes – Purposes, Practices, Patterns, and Platforms, we define data lake types, discuss emerging best practices, and take a look at user trends and readiness for data lakes. Here are several of the key survey results.
February 17, 2017
Organizations increasingly value big data and data science and embrace more diverse data sources to gain insight about customers, increase efficiency, and generate new revenue streams. Developing an effective analytics and data science strategy, however, is often a struggle. In the recent TDWI Best Practices Report: Data Science and Big Data – Enterprise Paths to Success, we take a look at organizations’ experiences with and plans for big data and data science and offer best practices for successfully implementing big data programs. Here are several of the key survey results.
December 1, 2016
The cloud is becoming a mature platform for data management, integration, business intelligence (BI), and analytics. Business leaders understand that the cloud can provide flexibility, scalability, and agility for their BI and analytics projects. Instances of the cloud can quickly spin up (or down) without the cost and delays of installing on-premises applications. In the recent TDWI Best Practices Report: BI, Analytics, and the Cloud, we take a look at organizations’experiences with and plans for cloud BI and analytics, including how satisfied organizations are with the cloud and why, overcoming cloud adoption challenges, and what your organization should consider when moving to the cloud. Here are several of the key survey results.
August 16, 2016
Business users want the power of analytics—but analytics can only be as good as the data. The biggest challenge nontechnical users are encountering is the same one that has been a steep challenge for data scientists: slow, difficult, and tedious data preparation. The increasing volume, variety, and velocity of data is putting pressure on organizations to rethink traditional methods of preparing data for reporting, analysis, and sharing. In the recent TDWI Best Practices Report: Improving Data Preparation for Business Analytics, we take a look at experiences with data preparation, discuss goals and objectives, and uncover how to take advantage of the rapidly evolving technology and self-service capabilities that allow business users to realize insights from data faster. Here are several of the key survey results.
April 22, 2016
No matter the vintage or sophistication of your organization’s data warehouse (DW) and the environment around it, it probably needs to be modernized to remain competitive, relevant, growing, and aligned with new business and technology requirements. User organizations, however, struggle to understand the trends and take the right action. In the recent TDWI Best Practices Report: Data Warehouse Modernization in the Age of Big Data Analytics, we take a look at the many issues and categories of modernization in data warehouse environments (DWEs), plus the strategies, methods, and enabling technologies that lead to success. Here are several of the key survey results.
February 5, 2016
Forward-looking organizations are beginning to take action on their data by systematically operationalizing and embedding their analytics—that is, integrating actionable insights into systems and business processes used to make decisions. In the recent TDWI Best Practices Report Operationalizing and Embedding Analytics for Action, we explore current strategies, challenges, and future trends for embedded analytics across both organizational and technical dimensions. Here are several of the key survey results.