RESEARCH & RESOURCES

Featured Webinars

  • 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. February 28, 2017 Register

  • Ask the Expert about Data Warehouse Modernization

    Despite their ongoing evolution, data warehouses (DWs) are more relevant than ever, as they support operationalized analytics and wring business value from machine data and other new forms of big data. Hence, it’s important to modernize an existing DW environment, to keep it competitive and aligned with business goals, as well as to grow into new data-driven practices and technologies, while also keeping and improving the old ones. March 13, 2017 Register

  • 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. March 15, 2017 Register

Upcoming Webinars

  • 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. February 28, 2017 Register

  • Ask the Expert about Data Warehouse Modernization

    Despite their ongoing evolution, data warehouses (DWs) are more relevant than ever, as they support operationalized analytics and wring business value from machine data and other new forms of big data. Hence, it’s important to modernize an existing DW environment, to keep it competitive and aligned with business goals, as well as to grow into new data-driven practices and technologies, while also keeping and improving the old ones. March 13, 2017 Register

  • 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. March 15, 2017 Register

  • 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. March 21, 2017 Register

  • 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. March 29, 2017 Register

  • Data Lakes: Purposes, Practices, Patterns, and Platforms

    We’re experiencing a time of great change, as data evolves into greater diversity (more data types, sources, schema, and latencies) and as user organizations diversify the ways they use data for business value (via advanced analytics and data integrated across multiple analytic and operational applications). To capture new big data, to scale up burgeoning traditional data, and to leverage both fully, users are modernizing their portfolios of tools, platforms, best practices, and skills. April 13, 2017 Register

On Demand Webinars

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

Ask the Expert About Data Science

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


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


Big Data and Data Science: Enterprise Paths to Success

Big data and data science can provide a significant path to value for organizations. These technologies, methodologies, and skills can help organizations gain additional insight about customers and operations; they can help make organizations more efficient, be a new source of revenue, and make organizations more competitive.

Fern Halper

Content Provided by TDWI and IBM, MapR, OpenText, Snowflake


Maximizing the Value of Your IoT Data: How to Utilize Data Virtualization to Provide Value and Context to Your Sensor Data

Sensor data from Internet of Things (IoT) devices is becoming more pervasive throughout the world of data management, but it can be both an opportunity and a challenge to existing platforms, integration, and best practices. Your organization needs to understand how its existing integration and data management tools can help with the introduction of sensor data, as well as how business stakeholders, in particular from operations teams, will be using that data to impact revenue and costs. In addition, your organization must enable the speed of performance required in operational and analytics use cases, including productivity to improve organizational performance, process efficiency to streamline company activities, new product development to better meet customer expectations and experiences, new business models for revenue generation and supply chain monitoring, and inventory and cost reduction.

Philip Russom


Emerging Best Practices for Data Lakes

It’s no surprise that data warehouse professionals are quickly adopting Hadoop. According to a recent TDWI survey, the number of deployed Hadoop clusters is up 60% over two years. While Hadoop is an effective design pattern for capturing and quickly ingesting a wide range of raw data types, there have been a number of challenges organizations have faced in realizing the true business value from their Hadoop-based data lakes.

Philip Russom


Accelerating the Path to Value with Hybrid Analytics Architecture

In today’s demanding economic environment, companies that can develop and deploy analytics faster have a significant competitive edge. They can use analytics to detect patterns and changes in markets, learn customer preferences, be alert to fraudulent activity, and more. With the advent of cloud computing, users quickly gain access to new data sources and analytic techniques, enabling companies to finally unleash their analytics – they are no longer constrained by the limits of their on-premises computing, database platform, data warehouse, and data storage capacity. However, to avoid even more data siloes, data governance issues, and more, organizations should consider a hybrid analytics architecture that brings together on premises and cloud, enabling a more controlled journey to the cloud, while enjoying the flexibility, power, and speed they need to handle a range of analytics demands.

David Stodder

Content Provided by TDWI, IBM


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