RESEARCH & RESOURCES

Featured Webinars

  • Modern Data Architectures to Support Modern Analytics

    Many organizations today are scrambling to meet the needs of new data types and analytics. TDWI research shows that companies are often analyzing data from multiple sources, including structured data, unstructured data, real-time streaming data, location data, and transactional data. They are making use of new techniques such as text analytics and machine learning, and they are moving towards self-service analytics. The traditional data warehouse or data mart is often limited in its ability to support these modern analytics in a fast and friendly way. July 12, 2018 Register

  • How to Design a Data Lake with Business Impact in Mind

    A quarter of organizations surveyed by TDWI in 2017 say they already have a data lake in production, while another quarter say their lake will be in production within 12 months. Although data lakes are still rather new, user organizations have adopted them briskly. Why has the data lake gotten so popular, so fast? July 24, 2018 Register

  • Achieving High-Value Analytics with Data Virtualization

    Analytics projects are critical to business success, and as a result, they are growing in size, number, complexity, and perhaps most important, in their data requirements. TDWI finds that data scientists, business analysts, and other personnel need to view and access data that resides in multiple sources, both on premises and in the cloud, to draw insights from data relationships and discover important patterns and trends. July 31, 2018 Register

Upcoming Webinars

  • Modern Data Architectures to Support Modern Analytics

    Many organizations today are scrambling to meet the needs of new data types and analytics. TDWI research shows that companies are often analyzing data from multiple sources, including structured data, unstructured data, real-time streaming data, location data, and transactional data. They are making use of new techniques such as text analytics and machine learning, and they are moving towards self-service analytics. The traditional data warehouse or data mart is often limited in its ability to support these modern analytics in a fast and friendly way. July 12, 2018 Register

  • How to Design a Data Lake with Business Impact in Mind

    A quarter of organizations surveyed by TDWI in 2017 say they already have a data lake in production, while another quarter say their lake will be in production within 12 months. Although data lakes are still rather new, user organizations have adopted them briskly. Why has the data lake gotten so popular, so fast? July 24, 2018 Register

  • Achieving High-Value Analytics with Data Virtualization

    Analytics projects are critical to business success, and as a result, they are growing in size, number, complexity, and perhaps most important, in their data requirements. TDWI finds that data scientists, business analysts, and other personnel need to view and access data that resides in multiple sources, both on premises and in the cloud, to draw insights from data relationships and discover important patterns and trends. July 31, 2018 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

Practical Predictive Analytics – Results of New TDWI Best Practices Research

Predictive analytics is now part of the analytics fabric of organizations. TDWI research indicates that it is in the early mainstream phase of adoption. Yet, even as organizations continue to adopt predictive analytics and machine learning, many are struggling to make it stick. Challenges include lack of skills, executive and organizational support, and data infrastructure issues.

Fern Halper, Ph.D.


Achieving Business Value Using Hybrid Analytics

As companies progress in their analytics efforts, they often look to leverage a hybrid cloud analytics model—one where data from both on-premises and cloud sources is analyzed seamlessly. This approach makes sense especially when analyzing data from diverse sources using more advanced analytics such as machine learning and predictive analytics. Data that is generated both in the cloud and on-premises often needs to be analyzed together.

Fern Halper, Ph.D.


AI for BI: Tapping Into the Potential of AI and Machine Learning for Business Intelligence

Business intelligence (BI) has much to gain from one of today’s most exciting trends: the infusion of artificial intelligence (AI) practices and techniques such as machine learning into BI. AI is important for supporting imperatives to make better and faster decisions, particularly as part of daily operations decisions and business processes that cannot wait long for accurate insights.

David Stodder


Strategies for Solving Business Problems Faster with Visualytics

While visualization is about telling a story with data for consumption, visualytics is visually understanding your data while you work and model it. Not waiting until the output to see and understand your data can give business users and analysts a faster path to uncovering critical insights for addressing business challenges and answering questions. Technologies that enable data visualization and analytics have previously been evolving separately, but leading solutions today have merged them together to give users new and easier ways of drawing insights from data and putting them into action for smarter decisions.

David Stodder


Modernizing Data Architecture With Streaming CDC

Expanding analytics requirements have increased the appetite for massive data volumes. However, these data flows can create bottlenecks, preventing timely and modern analytics innovations such as machine learning.

David Loshin


Data Architecture for IoT Communications and Analytics

The Internet of Things (IoT) is an architectural paradigm combining an exploding number of different types of connected sensors and devices continuously generating and broadcasting data. The data can be processed to create integrated analytics models that can enhance and optimize new business initiatives.

David Loshin


Modernizing Data Analytics: Moving Beyond Hadoop

As an open source platform that simplified the ability to develop distributed and parallel applications, Hadoop lowered the barrier to entry for many smaller organizations interested in big data analytics. Some people have gone as far to suggest that Hadoop be used to replace their existing data warehouse.

David Loshin


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