Organizations today want to anchor daily and strategic decisions in a bedrock of solid, extensive, and timely analysis and reporting. Personal, self-service BI and analytics tools are now available that can give users more control over how they view and access the information they need to perform their roles in the enterprise. This TDWI Checklist Report details seven steps toward personal BI and analytics success.
This report presents a requirements checklist for analytic DBMSs with a focus on their use with big data. You will also learn definitions for the many techniques and tool types involved. The report will help you evaluate analytic databases and/or develop strategies for big data analytics.
In the age of "big data," high-performance data warehousing is primarily about achieving speed and scale while also coping with increasing complexity and concurrency. The tips and strategies presented in this research can help user organizations prioritize their acquisition of vendor tools and their adoption of design best practices.
This TDWI Checklist Report presents tips for aligning geospatial information with the business intelligence environment, empowering analytics to use location data to yield operational efficiencies, revenue growth, and other potential benefits.
This TDWI Checklist Report examines the business drivers and technology requirements for deploying operational analytics. The main objectives of the Checklist are to identify specific business benefits of operational analytics, review the ways these benefits can be achieved, and explain the potential trade-offs or IT costs involved.
Ultimately, the effective utilization of predictive analytics is the goal-driven analysis of data to enhance business performance metrics. This TDWI Checklist Report examines the strategic steps that predictive analytics project teams must take in order to define, design, and implement successful projects.
Mounds of structured data, unstructured data, big data, and advancements in cloud technology are imposing new requirements for data integration. This TDWI Checklist Report will explore some of the key drivers these new requirements are intended to address. Whether you are looking to support the performance needs of big data applications, filter concepts from unstructured data, monitor hundreds of data feeds for unexpected behavior, export data across enterprise boundaries, or provide real-time reporting and analysis, there is a rapidly expanding need for data integration competency that extends well beyond traditional ETL.
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