RESEARCH & RESOURCES

Featured Webinars

  • Enabling Data Science to Be Data Science: Strategies for Increasing Self-Service Data Science

    Data science offers great potential for what it can contribute to business strategy and operations—that is, if data scientists are actually able to do data science rather than spend most of their time on data management and preparation. TDWI finds that most data science projects spend the majority of time on these areas rather than on development of analytics, models, and algorithms. To increase business value, organizations need solutions that will flip this ratio. January 23, 2018 Register

  • Ask the Expert on The UX Guide to Analytics
    TDWI Members Only

    Enterprise analytics spans a wide array of categories but they all have one thing in common, they require human interaction to realize value. However, much of that value is often left on the table. Factors such as user interviews, persona design, stakeholder buy in, wireframing, iteration, adoption and feedback are underutilized and greatly increase the risk of user disengagement and stakeholder frustration. Analytics managers and dashboard creators can miss the opportunity to leverage user motivations to drive success. January 25, 2018 Register

  • Making Predictive Analytics Work – 5 Keys to Successful Model Deployment and Management

    Organizations are excited about predictive analytics and machine learning for a number of reasons. Companies want to better understand customer behavior. They want to better predict failures in their infrastructure. The uses for predictive analytics are extensive and growing. February 8, 2018 Register

Upcoming Webinars

  • Enabling Data Science to Be Data Science: Strategies for Increasing Self-Service Data Science

    Data science offers great potential for what it can contribute to business strategy and operations—that is, if data scientists are actually able to do data science rather than spend most of their time on data management and preparation. TDWI finds that most data science projects spend the majority of time on these areas rather than on development of analytics, models, and algorithms. To increase business value, organizations need solutions that will flip this ratio. January 23, 2018 Register

  • Ask the Expert on The UX Guide to Analytics
    TDWI Members Only

    Enterprise analytics spans a wide array of categories but they all have one thing in common, they require human interaction to realize value. However, much of that value is often left on the table. Factors such as user interviews, persona design, stakeholder buy in, wireframing, iteration, adoption and feedback are underutilized and greatly increase the risk of user disengagement and stakeholder frustration. Analytics managers and dashboard creators can miss the opportunity to leverage user motivations to drive success. January 25, 2018 Register

  • Making Predictive Analytics Work – 5 Keys to Successful Model Deployment and Management

    Organizations are excited about predictive analytics and machine learning for a number of reasons. Companies want to better understand customer behavior. They want to better predict failures in their infrastructure. The uses for predictive analytics are extensive and growing. February 8, 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

Demystifying Elastic Data Warehousing: Perceptual Barriers versus Real-World Benefits

We all know that data warehouses and users’ best practices for them are changing dramatically today. As users build new data warehouses and modernize established ones, they are turning to cloud-based elastic data warehousing, because the automation of elasticity yields agility, ease of use, scalability, and performance, while reducing maintenance, tuning, capital investments, and other costs.

Philip Russom, Ph.D.


Are you sure that your data is protected? How data-centric security is critical to your business

Are access and authentication enough when it comes to securing your data, especially an organization’s most critical data? The short answer is no. In 2015, many customers of large and small companies including T-Mobile, Excellus Blue Cross Blue Shield, UCLA Health, Scottrade, and more fell victim to data breaches. No industry is immune. TDWI has noted for years that most data warehouses rely on user-centric authorization almost exclusively, with little or no use of data-centric security. Given the ever increasing number of data breaches, security upgrades are certainly needed for data warehouses and the larger evolving data ecosystem.

Fern Halper, Ph.D.


Enabling the Citizen Data Scientist: How to Build a Bigger Business Impact with Analytics

Data science is becoming essential to organizations seeking to gain greater business value from data. Yet, finding and keeping dedicated, high-pedigree data scientists is not easy; some even say it’s like “chasing unicorns.” A better strategy is to develop data science teams and empower business users – executives, marketing decision-makers, line of business (LOB) managers, and more – to engage in data exploration, experimentation, and development of insights that they can apply to improving business outcomes. This requires not just technology but training, attending to people, process, and governance issues, and helping personnel to define the right questions so that they can apply the most relevant analytic methods and technologies.

David Stodder


Data Warehouse Automation: Driving Business Value for the Future of Data Warehousing

While many believe that the maturation of end-user tools supporting visualization, reporting, and analytic signals the imminent demise of the data warehouse, nothing could be farther from the truth. The increasing business user demand for information highlights the need for a centralized nerve center provided by the organization’s data warehouse. In turn, the future data warehouse requires technologies that accelerate design and development, improve cycle time in producing reports and analyses, and enhance the IT-business collaboration.

David Loshin


Operationalizing and Embedding Analytics for Action

What good is analytics if no one takes action on it? Operationalizing and embedding analytics is about integrating actionable insights into systems and business processes used to make decisions—at the point of decision making. These systems might be automated or provide manual, actionable insights. Analytics is currently being embedded into dashboards, applications, devices, systems, and databases. Examples run from simple to complex, and organizations are at different stages of operational deployment. Newer examples of operational analytics include support for logistics, asset management, customer call centers, and recommendation engines—to name just a few.

Fern Halper, Ph.D.


Seven Steps to Faster Analytics Processing with Open Source

Excellence in analytics is a competitive advantage in nearly all industries. For this reason, organizations need their data scientists, business analysts, and business users to be able to access and interact with more sources and more types of data than ever before. The Hadoop ecosystem is flourishing, producing innovative technologies and frameworks such as Apache Spark, Apache Apex, and more that are becoming important for providing processing speed and power as well as data integration and preparation capabilities for fast, visual, and interactive analytics.

David Stodder


Modernizing Data Integration to Accommodate New Big Data and New Business Requirements

There’s a lot of buzz currently about how many types of IT systems need updates, upgrades, extensions, and replacements, due to recent changes in business and technology requirements. Current parlance refers to these collectively as “modernization” projects.

Philip Russom, Ph.D.


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