RESEARCH & RESOURCES

Featured Webinars

  • 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? April 30, 2018 Register

  • 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

Upcoming Webinars

  • 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? April 30, 2018 Register

  • 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

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 Management for Big Data, Hadoop, and Data Lakes

A perfect storm of data management trends is converging. First, organizations across many industries are experiencing the big data phenomenon, which forces them to capture and leverage data from new sources, in structures and velocities that are new to them, in unprecedented volumes. Second, technical users are scrambling to learn new data platforms like Hadoop and their evolving best practices. Third, the data lake arose suddenly in 2016 as the preferred approach to managing very large repositories of raw source data. Fourth, business managers have attained a new level of sophistication in their use big data for business value and organizational advantage.

Philip Russom, Ph.D.


Between a Rock and a Hard Place: How to Modernize Legacy Middleware for an Evolving, Data-driven World

In support of daily operations, many organizations depend heavily on systems for enterprise application integration (EAI), enterprise service bus (ESB), and other approaches to middleware. Yet, these infrastructures are today legacy technologies that predate the rise of big data and unstructured data, as well as modern sources and targets for integration, such machines, devices, clouds, social media, and the Internet of Things (IoT). Furthermore, many middleware vendor tools are still optimized for the on-premises ERP-dominated applications world of twenty years ago; others are in legacy mode, with no future upgrades coming.

Philip Russom, Ph.D.


Database Strategies for Modern BI and Analytics

The data universe has changed. Big data, cloud computing, and open source have dramatically expanded the number of data warehousing offerings available to today’s businesses. An increasing number of companies are implementing self-service business intelligence (BI) and visual analytics tools to access and make sense of all of the new and diverse sources of data their teams are consuming. Data literacy is changing equally fast as an increasing number of “data consumers” want to interact with data on their own rather than through IT.

David Stodder


End Your Data Struggle: How to Seamlessly Analyze Disparate Data

Many organizations today are struggling to get value from their data and advanced analytics initiatives. The struggle begins with data diversity, as organizations are trying to support new apps, customer channels, sensors, and social media outlets. Each source may have its own data structure, quality, and container (in the form of files, documents, messages). The struggle is exacerbated by the exploding volume of data that must be captured, processed, stored, and delivered to the right users in a state that is fit for their own individual needs.

Philip Russom, Ph.D.


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.

Philip Russom, Ph.D.


Creating Value with Unified Governance

Certainly, every business leader wants to have trusted, secure, consistent and usable information. But data volumes and systems complexity has been increasing for years and most organizations rarely prioritize data governance, so why care now? We’re at the brink of a perfect storm of unprecedented IT megatrends. The convergence of Cloud, Social, Mobile and Big Data foreshadows the upcoming tsunami of data ripe with potential business value. But it will also make the frustrating complexity of your traditional on-premises transactional data management challenges appear amazingly “manageable” in contrast.

Claudia Imhoff, Ph.D.


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.

Philip Russom, Ph.D.


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