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RESEARCH & RESOURCES


Featured Webinars

Upcoming Webinars

  • Expert Panel: Real-Time Analytics Use Cases and Architectures

    In this expert panel, TDWI senior research director James Kobielus will discuss the chief enterprise use cases for real-time analytics and the principal architectural considerations for data, analytics, and IT professionals seeking to optimize their infrastructures for these applications. December 9, 2024 Register

International Broadcasts

TDWI Webinars on Big Data, Business Intelligence, Data Warehousing & Analytics

TDWI Webinars deliver unbiased information on pertinent issues in the big data, business intelligence, data warehousing, and analytics industry. Each live Webinar is roughly one hour in length and includes an interactive question-and-answer session following the presentation.


On Demand

Is Hadoop-as-a-Service the Right Choice for Your Business?

As the volume and business uses of data continue to expand, there is growing interest in leveraging cloud computing for data warehouse and business analytics projects. Responding to this interest, both established and new vendors are offering cloud-based Hadoop services for blending, managing, and processing large volumes of both traditional and new sources of data.

Colin White


TDWI’s Top 10 Trends in BI and Analytics for 2015: Setting Your Technology Strategy

Organizations of all shapes and sizes today are focused on how they can realize higher value from data. With insights from business intelligence (BI) and analytics, firms can improve customer experiences, make smarter decisions about how to allocate resources, and develop strategies to improve business performance. Technologies and practices for getting the most out of data with BI and analytics are changing rapidly, however, so organizations cannot stand still. You need to stay abreast of the changes.

Fern Halper, Ph.D., David Stodder


Enabling Business Productivity by Uprooting Data Complexity in the BI/Analytics Environment

Because BI/DW architectures have historically been engineered to maintain the performance of OLTP systems, implementation decisions of the past have forced analysts to sacrifice data accessibility and flexibility in return for reasonable reporting and analytics performance. The reliance on ERP and other operational systems to run the business negatively impacts the agility of the data analyst. Limited data accessibility and increased latency are effectively engineered into the analytics environment, creating complexities that preclude the rapid discovery of actionable insights.

David Loshin


Analytics at Scale: What You Need to Know

Much of the attention on big data has focused on the infrastructure—data warehouses, Hadoop, appliances—needed to support big data initiatives. However, because data is only useful if you can analyze and act on it, the question becomes: What does it mean to analyze data at scale?

Fern Halper, Ph.D.


Making Data Work for You: Data Warehousing in the Cloud

Whether driven by cost, the need to deliver new capabilities, or a deluge of new and varied data of value to the business, organizations are re-thinking the data warehouse. As boundaries become more fluid to support emerging mobile, social, and cloud-based services, key challenges emerge: how to be flexible and agile in taking appropriate advantage of cloud-based solutions without compromising on security or service quality.

Colin White


Embedded Analytics: From Dashboards to Wearables and Beyond

Analytics is undergoing a renaissance. Its value is understood. It is becoming easier to use, even by the non-statistician. Organizations are gathering ever increasing volumes of disparate data to use for analytics. Forward-looking organizations are even using real-time data and beginning to embed analytics against this data.

Fern Halper, Ph.D.


Moving Forward with Analytics: Introducing TDWI’s New Maturity Model Assessment Tool

Analytics is hot and getting hotter. As its value becomes better understood, many organizations are looking to expand their analytics efforts. To discover new opportunities, serve customers more effectively, reduce fraud, and improve operations, firms want to get to the next level with predictive and other forms of advanced analytics. They want to build a broader analytics culture that includes more types of users.

Fern Halper, Ph.D., David Stodder


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