RESEARCH & RESOURCES

Featured Webinars

  • Analytics Everywhere: Building Analytics Applications for Driving Business Value

    Analytics has become mainstream, and TDWI research indicates that the vast majority of organizations have adopted technologies such as dashboards and visual analytics. However, as organizations mature along their analytics journey, they are looking to embed their analytics into devices, applications, and systems. Embedding analytics layers analytics into another application or process and brings the results of analysis to the decision maker through applications that run the business. The result is opening up analytics to more users and making analytics relevant, actionable, and more valuable. May 30, 2018 Register

  • The Automation and Optimization of Advanced Analytics based on Machine Learning

    However, embracing machine learning successfully is challenged by ML’s serious data requirements. In development, designing an analytic model depends on very large volumes of diverse data. In production, an analytic model created via machine learning again needs voluminous data, so it can learn and improve over time. In turn, managing big data for machine learning demands a substantial data management infrastructure and tool portfolio. May 31, 2018 Register

  • 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. June 5, 2018 Register

Upcoming Webinars

  • Analytics Everywhere: Building Analytics Applications for Driving Business Value

    Analytics has become mainstream, and TDWI research indicates that the vast majority of organizations have adopted technologies such as dashboards and visual analytics. However, as organizations mature along their analytics journey, they are looking to embed their analytics into devices, applications, and systems. Embedding analytics layers analytics into another application or process and brings the results of analysis to the decision maker through applications that run the business. The result is opening up analytics to more users and making analytics relevant, actionable, and more valuable. May 30, 2018 Register

  • The Automation and Optimization of Advanced Analytics based on Machine Learning

    However, embracing machine learning successfully is challenged by ML’s serious data requirements. In development, designing an analytic model depends on very large volumes of diverse data. In production, an analytic model created via machine learning again needs voluminous data, so it can learn and improve over time. In turn, managing big data for machine learning demands a substantial data management infrastructure and tool portfolio. May 31, 2018 Register

  • 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. June 5, 2018 Register

  • 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. June 6, 2018 Register

  • 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. June 14, 2018 Register

  • 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

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

GDPR: What It Means for Analytics and Data Management

The deadline for complying with the European Union’s General Data Protection Regulation (GDPR) is fast approaching. The EU calls it “the most important change in data privacy regulation in 20 years” – and that’s no exaggeration. Beginning May 25, 2018, organizations that are in non-compliance may face heavy fines, not to mention damage to their reputations. How does this regulation affect the way your organization uses data for analytics and business intelligence? What do you need to do from a data management perspective to ensure compliance – not just by May 25, but into the future?

David Stodder


Ask the Expert: Demystifying Semantics and Ontologies
TDWI Members Only

We hear more and more about semantics these days, but what does it mean? What is an ontology and how does it relate to a data model? Do semantics and ontologies have a role to play in data architecture and data modeling?

Ted Hills


Six Strategies for Balancing Risk with Data Value

Managing data for value is a business-oriented focus on the potential of data. It complements the all-too-common obsession with data’s technical requirements. Data value recognizes that data is a valuable business asset and should be leveraged accordingly. If you are managing data for value, your asset portfolio of data should be protected, grown, and governed.

Philip Russom, Ph.D.


Building Trusted Data Through Deep Profiling and Analysis with Machine Learning

Data environments are becoming increasingly complex. Many organizations are employing what TDWI terms a multiplatform data architecture to manage new and disparate data sources. This might include the data warehouse along with other platforms, such as a data lake or a streaming platform. The cloud might be an important part of this architecture as well. This data is often used to gain insights as organizations become more analytically sophisticated. In fact, analytics is the top driver for modern data architectures.

Fern Halper, Ph.D.


Applying the Power of AI to Data Catalogs: How artificial intelligence can make it easier for users to share knowledge and collaborate

Data catalogs are increasingly critical as more users engage in self-service analytics and business intelligence and seek access to a wider variety of data types and sources. Many organizations fear that allowing users more self-service access and analysis will result in data chaos, especially if those sources exist outside IT-managed platforms such as an enterprise data warehouse. Data catalogs help organizations combat data chaos by enabling them to manage and govern distributed data assets more effectively. With an effective data catalog, organizations curate data so that users can trust sources and gain insights faster.

David Stodder


Modernizing Your Data Pipeline for Better Analytics

Organizations are excited about analyzing ever-increasing amounts of disparate data, and they are often looking at advanced analytics to do so. Underpinning this move toward better insight and action is an evolving data infrastructure that is multiplatform in nature. TDWI sees increasing interest in the cloud, streaming platforms, and data lakes to support diverse data types for analysis. These platforms and others are coming together in modern data architectures that enable analytics and drive analytics success.

Fern Halper, Ph.D.


Ask the Expert about the Role of Data Visualization on Data Validation
TDWI Members Only

Data visualization has become a standard part of the business intelligence fair. It is now expected that a business intelligence team include a rich set of graphics in the tooling used across the business. In the real-time world, we are faced with the challenge of handling data streams directly from operational tools. This real-time data when presented visually tend to immediately skew to highlight outliers and exceptions in the data.

Andrew Cardno


TDWI Membership

Get immediate access to training discounts, video library, BI Teams, Skills, Budget Report, and more

Individual, Student, & Team memberships available.