RESEARCH & RESOURCES

Featured Webinars

  • 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

  • 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

Upcoming Webinars

  • 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

  • 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

Data Warehouse Modernization in the Age of Big Data Analytics

Data warehouses (DWs) and requirements for them continue to evolve. 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 a DW environment, to keep it competitive and aligned with business goals, in the new age of big data analytics. Yet, user organizations struggle to stay educated about trends and take the right action to modernize their DW investments. Many users need to catch up by deploying a number of upgrades and extensions to their existing DW environments and by adopting modern development methods. Once caught up, they need a strategy for continuous modernization.

Philip Russom, Ph.D.

Content Provided by TDWI, IBM, Pentaho, SAP, SAS, TimeXtender


Empowering Business Users with Analytics and Data Discovery

Demand is accelerating across organizations for better and faster access to data. Business executives, managers, and frontline users in operations want the power to move beyond the limits of spreadsheets so they can engage in deeper analysis and use data insights to transform all types of decisions. Newer tools and methods are making it possible for organizations to meet the demands of nontechnical users by enabling them to access, integrate, transform, and visualize data without traditional IT hand-holding.

David Stodder


Rapid Deployment of Advanced Behavioral Analytics

The continued growth of interactive businesses combined with the explosive diffusion of online, mobile, and IoT (Internet of Things) touch points has enabled organizations to develop business applications involving millions, if not orders of magnitude more interactions and transactions. The success of the business, though, depends on driving the customers and users toward profitable transactions. Examples include purchasing products viewed on an eCommerce web site, recommending an article to a friend, or triggering automated controls within an industrial environment to avoid a part failure. These are examples of scenarios that are informed through behavioral analytics.

David Loshin


Are You Ready for Hadoop? Introducing the TDWI Hadoop Readiness Assessment

A recent TDWI survey shows that Hadoop clusters in production are up 60 percent over two years. This is no surprise because use cases for Hadoop in data warehousing, business intelligence, and analytics are well established. In addition, applications of Hadoop for archiving, content management, and operational applications are emerging into prominence. These developments show that Hadoop usage is diversifying broadly across and within mainstream enterprises, such that Hadoop will eventually be a common platform for many purposes in many IT portfolios.

Fern Halper, Ph.D., Philip Russom, Ph.D.

Content Provided by TDWI, IBM, Cloudera, MapR, MarkLogic, Teradata


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


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