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RESEARCH & RESOURCES

Featured Webinars

  • The Last (And Arguably Most Important) Mile of Machine Learning: Operationalizing Analytical Models

    Organizations are investing in model development but often overlook operationalizing models, which can cause projects to fail. Join us to learn why operationalizing analytics is so critical, who performs it, and what steps your organization can take to unlock the value of your analytical models. October 16, 2019 Register

  • The Data Warehouse Comeback: How the Cloud is Accelerating Insights

    In the webinar, The Data Warehouse Comeback: How the Cloud is Accelerating Insights, a guest speaker from Forrester, as well as speakers from TDWI and Attunity (a division of Qlik), will discuss the benefits of using cloud data warehouses to unlock analytical insights. October 22, 2019 Register

  • Best Practices for Cloud Data Pipelines

    Learn about the alternatives for developing and managing cloud data pipelines and what to expect of modern analytics environments. Explore real-time data ingestion and management, why custom-coded data pipelines are so complex (and ELT’s role), and what skills are needed to develop data pipelines. October 23, 2019 Register

Upcoming Webinars

  • Gain Control of Your Data Lake

    In this webinar we explore data governance concepts for the cloud-based data lake. You’ll also learn about data lake architecture alternatives, data sensitivity and the need for data protection, how to protect against unauthorized data exposure, and tips for encouraging governed data sharing. October 29, 2019 Register

  • The Operational Data Warehouse in Today’s Hybrid Cloud Environments

    Learn what a modern OpDW is and does, technologies and practices that can modernize it (including in-memory execution and distributed and cloud-based data management), and how and why the best OpDWs provide high performance for both operations and analytics workloads. October 30, 2019 Register

  • Migrating Analytics Systems from On Premises to the Cloud

    Are your on-premises data warehousing and analytics applications ready to move to the cloud? We’ll explore why different apps need different migration scenarios, alternatives for prioritizing eligible systems, migration challenges, and how to develop and implement successful migration plans. October 30, 2019 Register

  • Six Strategies for Simplifying Hybrid, Multicloud Data Integration: How to overcome pain points in providing governed views of diverse data

    Data integration in cloud and on-premises hybrid environments can be painful. Learn how you can overcome pain points in governed views of diverse data by using data virtualization to reduce data movement, provide comprehensive data views faster, and address governance challenges. October 31, 2019 Register

  • Cloud at Scale for Modern Data Warehousing - New Approaches for New Business Uses and Technical Requirements

    Data warehouse modernization and cloud adoption often go hand in hand; both seek to ensure extreme scalability, lower administrative and platform costs, and support new business analytics. This webinar will discuss both practices, with a focus on coordinating the two and present six key recommendations. November 6, 2019 Register

  • Data Management and Data Warehouse Requirements for Machine Learning and AI

    This webinar will define ML and related technologies and practices in AI and predictive analytics, discuss ML’s use cases, explain data management requirements users must address to successfully apply ML to their analytics, and explain the ML life cycle and the unique data requirements of each stage. November 20, 2019 Register

  • Best Practices for Cloud Data Pipelines

    Learn about the alternatives for developing and managing cloud data pipelines and what to expect of modern analytics environments. Explore real-time data ingestion and management, why custom-coded data pipelines are so complex (and ELT’s role), and what skills are needed to develop data pipelines. December 10, 2019 Register

  • Gain Control of Your Data Lake

    In this webinar we explore data governance concepts for the cloud-based data lake. You’ll also learn about data lake architecture alternatives, data sensitivity and the need for data protection, how to protect against unauthorized data exposure, and tips for encouraging governed data sharing. December 17, 2019 Register

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

What It Takes to Be Data-Driven: Technologies and Practices for Becoming a Smarter Organization

Join Fern Halper, TDWI VP and senior research director for advanced analytics, and David Stodder, senior research director for BI, to learn more about the research from their new Best Practices Report about what it takes to become data-driven. They will discuss their findings and present recommendations for organizational and technology best practices.

Fern Halper, Ph.D., David Stodder


Evolution of the Data Lake—Implementing Real-Time Change Data in Hadoop

A ten-fold increase in worldwide data by 2025 is one of many predictions about big data. With such growth rates in data, the “data lake” is a very popular concept today. Everybody touts their platform capabilities for the data lake, and it is all about Apache Hadoop. With its proven cost-effective, highly scalable, and reliable means of storing vast data sets on cost-effective commodity hardware regardless of format, it seems to be the ideal analytics repository. However, the power of discovery that comes with the lack of a schema also creates a barrier for integrating well-understood transaction data that is more comfortably stored in a relational database. Rapidly changing data can quickly turn a data lake into a data swamp.

Krish Krishnan


Up to the Minute: The Need for Rapid Adoption of Streaming Data

As Internet of Things (IoT) technologies become more common and web data grows in volume, there is growing evidence that the ability to analyze continuous data is not only valuable but necessary. In fact, those with the ability to capture and analyze massive numbers of independent continuous data streams will have a powerful capability that will help them to power operational intelligence and predictive analytics. A growing number of applications increasingly rely on fast analysis, but tomorrow’s world will be even more dependent on up-to-the-minute consumption of data streams.

David Loshin


Ask the Expert on the Roles and Construct of a Thriving Analytic Team
TDWI Members Only

Most organizations believe they will achieve better analytic results if they populate a deeper bench of experienced data scientists and machine learning practitioners. But this is akin to building a home exclusively with highly skilled framers, brick layers and cabinet makers. You’ll end up with a solid structure and great workmanship, but not a true functional home.

Keith McCormick


Building a Successful Data Lake in the Cloud

Data lakes on Hadoop have come on strong in recent years because they help many types of user organizations – from Internet firms to mainstream industries – capture big data at scale and analyze or otherwise process it for business value.

Philip Russom, Ph.D.


Location, Location, Location: How Geoenrichment Can Improve Business Intelligence and Analytics

Geospatial data is growing in importance for business intelligence as users seek to make sense of diverse data. One element that many types of data have in common is location. Critical attributes of human, machine, and application-generated data become clearer when the source’s location—or movement from one location to another—is known and incorporated into reporting and analysis. Business users can spot trends, patterns, gaps, and other data relationships more clearly if they are able to visually integrate different types of data with maps. If organizations can enrich demographic, behavioral, operational, and other data with location information, they will be on a faster path to generate breakthrough insights and make smarter decisions.

David Stodder


Using Design Thinking to Unleash Creativity in BI and Analytics Development

Design Thinking methods can help organizations overcome the limitations of traditional BI and analytics development. Design Thinking has enabled retail, banking, and other types of firms to revolutionize how they develop products and services to deliver exceptional customer experiences. These methods offer similar potential for unleashing your organization’s creativity in developing applications and services that delight internal users. With organizations under pressure to deliver higher ROI from data—and frustrated by BI and analytics applications and services that don’t meet users’ requirements or realize value from all the data that they have—now is the time to consider new approaches such as Design Thinking.

David Stodder


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