RESEARCH & RESOURCES

Featured Webinars

  • Six Steps for Generating Business Value from Operational IoT Data

    Learn how to accelerate your organization’s ability to realize business value from operational IoT data, plus best practices for reducing data prep, eliminating data latency, and making operational IoT data actionable. We’ll offer tips for balancing data governance, security, and accessibility. February 26, 2019 Register

  • Cloud Data Warehouses: Are They Right for Your Organization?

    Is the cloud right for your organization? Learn about the state of on-premises versus cloud data warehousing and analytics, use cases for each, and the challenges and best practices for moving to the cloud. A demonstration of a cloud-based data warehouse and analytics solution will also be offered. February 28, 2019 Register

  • Modernizing Metadata

    Learn about innovations in metadata tools, including smart tool automation to improve developer productivity (thanks to smart algorithms and AI) and address data lineage as well as new data semantics to enhance business metadata and improve self-service data exploration and analytics. March 7, 2019 Register

Upcoming Webinars

  • Six Steps for Generating Business Value from Operational IoT Data

    Learn how to accelerate your organization’s ability to realize business value from operational IoT data, plus best practices for reducing data prep, eliminating data latency, and making operational IoT data actionable. We’ll offer tips for balancing data governance, security, and accessibility. February 26, 2019 Register

  • Cloud Data Warehouses: Are They Right for Your Organization?

    Is the cloud right for your organization? Learn about the state of on-premises versus cloud data warehousing and analytics, use cases for each, and the challenges and best practices for moving to the cloud. A demonstration of a cloud-based data warehouse and analytics solution will also be offered. February 28, 2019 Register

  • Modernizing Metadata

    Learn about innovations in metadata tools, including smart tool automation to improve developer productivity (thanks to smart algorithms and AI) and address data lineage as well as new data semantics to enhance business metadata and improve self-service data exploration and analytics. March 7, 2019 Register

  • How to Use Data Prep to Accelerate Cloud Data Lake Adoption

    Learn how you can address data prep challenges as you move to the cloud for data management and how you can turn raw, cloud-based source data into output suited for analytics and ML. Explore the tech trends shaping data prep for cloud data lakes and data warehouses. March 12, 2019 Register

  • Cloud Data Lakes: Enabling New Business Analytics at High Scale and Low Cost

    This webinar will discuss the business benefits of data lakes, the benefits of deploying a data lake in the cloud (and how use cases and technology requirements affect your platform choice), and why substantial data integration is necessary. March 20, 2019 Register

  • Modern Data Analytics in the Cloud: Achieving an End-to-End Strategy

    Learn how your organization can create an end-to-end strategy that aligns analytics in the cloud with data management. We’ll discuss key issues in supporting today’s analytics workloads, including cloud data warehousing, data preparation and transformation, and visual data interactivity. March 21, 2019 Register

  • Embedded Analytics: Unleashing Insights Everywhere

    Embedded analytics brings the results of analysis to more decision makers quickly, resulting in smarter decisions faster. No wonder it’s a top strategy for 2019. Learn what embedded analytics is, how it makes such a big impact, how it’s used in applications, and the move toward analytics automation. April 2, 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

Putting Machine Learning to Work in Your Enterprise

Everyone is talking about machine learning—software that can learn without being explicitly programmed, machine learning (and deep learning) can access, analyze, and find patterns in big data in a way that is beyond human capabilities. The technology is being used in a wide range of industries for use cases including fraud prevention, predicting crop yields, preventing and mitigating natural disasters, predictive maintenance of enterprise assets, and improving supply chain efficiencies.

Fern Halper, Ph.D.


Navigating the Predictive Analytics Market

Predictive analytics is on the verge of widespread adoption. Enterprises are extremely interested in deploying predictive capabilities. In a recent TDWI survey about data science, about 35 percent of respondents said they had already implemented predictive analytics in some way. In a 2017 TDWI education survey, predictive analytics was the top analytics-related topic respondents wanted to learn more about.

Fern Halper, Ph.D.


Making Multiplatform Data Architectures Work for You: Common Use Cases and Reference Architectures

To leverage the new wave of advanced data sources available, users and architects are turning to a multiplatform data architecture (MDA), where numerous diverse data platforms and tools are integrated in a multiplatform, distributed architecture. An MDA is typified by an extreme diversity of platform types that may include multiple brands of relational databases, NoSQL platforms, in-memory functions, and tools for data integration, analytics, and stream processing. Any of these may be on premises, in the cloud, or in hybrid combinations of the two.

Philip Russom, Ph.D.


Ask the Expert: Data Science
TDWI Members Only

It’s hard to find a topic out there hotter than Data Science right now; and can be equally hard to find one more confusing. Data Science techniques have revolutionized nearly any industry you can imagine, and in some cases created whole new ones from thin air. Despite this, much of Data Science remains couched in mystery--a magic black box that is supposed to solve all of our problems.

Frank Evans


Get More Business Value from a Data Lake via Data-as-a-Service (DaaS)

Data lakes are coming on strong as a modern and practical way of managing the large volumes and broad range of data types and sources that enterprises are facing today. TDWI sees data lakes managing diverse data successfully for business-driven use cases, such as omni-channel marketing, multi-module ERP, the digital supply chain, and data warehouses extended for business analytics. Yet, even in business-driven examples like these, user organizations still haven’t achieved full business value and return on investment from their data lakes.

Philip Russom, Ph.D.


Use Big Data Analytics and Geoenrichment to Drive Better Business Outcomes

The volumes of data and speed at which data is produced continually increases on an exponential scale. Consumer transaction data, client records and data in motion from mobile devices, IoT sensors and other sources usually contains associated geographic coordinates that require geospatial processing to extract value. With the volume and variety of this data, organizations need to have a location strategy that includes big data technology that can join disparate data sets (geoenrichment) and perform location analytics to reveal actionable business and operational insights.

David Stodder


Defining a Multiplatform Data Architecture and What It Means to You

A revolution is occurring in modern analytics, driven by our ability to capture new sources of information at a detail previously too complex and costly to imagine. As more data comes from new sources (from machines to social media) and is applied to new applications, data is evolving into greater diversity, including every variation of data type from unstructured to multistructured. Even as new tools to analyze and manipulate this newly available resource come online, it is not enough to look at the data manipulation layer alone.

Philip Russom, Ph.D.


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