RESEARCH & RESOURCES

Featured Webinars

  • 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. June 19, 2018 Register

  • 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. June 20, 2018 Register

  • 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. June 21, 2018 Register

Upcoming Webinars

  • 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. June 19, 2018 Register

  • 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. June 20, 2018 Register

  • 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. June 21, 2018 Register

  • Getting Started with Data Integration in the Cloud

    Cloud continues to rise in importance as a platform for many IT systems, including those for data integration. Many organizations have now achieved a maturity level where they are using multiple cloud-based applications and online data sources. These users now need data integration tool platforms that support hybrid data environments so they can unify on-premises and cloud-based data sources and targets. Similarly, users increasingly need data integration processing to run natively on clouds (not just on premises), so that data integration functions and related capabilities are closer to software-as-a-service (SaaS) applications, Web data sources, multiple clouds, and increasingly popular cloud-based databases, data lakes, and data warehouses. July 10, 2018 Register

  • 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

Take a Dive into the Data Lake

Many organizations have a serious interest in data lakes, at the moment, because of the business analytics and new data-driven practices that lakes promise. Yet, these organizations still aren’t quite ready to take a dive into a data lake. Whether they are unable to define standard structures, align and maintain business meanings, or create a governance strategy, these companies struggle to anticipate what truly lies beneath the surface of the data lake.

Philip Russom, Ph.D.


Rethinking Enterprise BI in a Self-Service World: Balancing User Freedom with Enterprise IT Responsibilities

Both product and tech leaders have always recognized that business intelligence (BI) is most valuable when it is pervasive, contextual, and actionable. A new generation of solutions -- embedded BI – provides unprecedented power to weave reporting and analytics into the fabric of apps and business processes.

David Stodder


Achieving Integration Agility, Scale, and Simplicity via Cloud-Based Integration Platform-as-a-Service

Many firms have mandates to move to clouds, control IT costs, integrate disparate applications, deliver data-driven solutions faster, and provide integration infrastructure for hybrid data ecosystems.

Philip Russom, Ph.D.


Embedded BI: Why It Works and How to Do It

Both product and tech leaders have always recognized that business intelligence (BI) is most valuable when it is pervasive, contextual, and actionable. A new generation of solutions -- embedded BI – provides unprecedented power to weave reporting and analytics into the fabric of apps and business processes.

David Stodder


Ask the Expert About Data Science
TDWI Members Only

The data and analytics landscape is changing. Although many organizations are still analyzing structured data from their data warehouse, TDWI research indicates organizations have increasing interest in analyzing disparate kinds of data. This data is often large in volume and can require modernizing data infrastructures and platforms. The industry around big data and data science and the emerging role of the data scientist is one result of this evolution/revolution.

Fern Halper, Ph.D.


Extending BI and Analytics to the Mobile Workforce

Users of all types are spending more and more time on mobile devices, whether they are business executives, a line-of-business (LOB) managers, retail inventory clerks, or frontline service technicians. While engaged with customers, managing operations, or strategizing about new products, they need access to critical business intelligence (BI) reports and analytics. For an increasing number of organizations, it is now a high priority to extend BI and analytics to the mobile workforce.

David Stodder


The Future of IT Management – IT Operations Analytics

IT operations management (ITOM) deals with monitoring and controlling IT infrastructure and services such as networks, servers, and help desk. Today, IT management typically relies on “swivel chair” monitoring between unrelated reactive monitoring tools. However, this is changing. Modern, interrelated IT departments can benefit from a single view across IT to improve root cause analysis and reduce meantime to resolution.

Fern Halper, Ph.D.


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