RESEARCH & RESOURCES

Featured Webinars

  • 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

Upcoming Webinars

  • 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

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


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