RESEARCH & RESOURCES

Featured Webinars

  • Metadata Discovery Doesn’t Have to be Painful

    Today’s BI and analytics landscape is more complicated than it ever has been. Most organizations have repositories of data everywhere, many BI and analytics technologies, and multiple streams of data integration and/or data preparation – all with their own sets of metadata. No wonder BI implementers and business users are frustrated, confused, and lost when it comes to using these critical assets. June 6, 2017 Register

  • Ask the Expert: Should You Learn MapReduce or Spark?
    TDWI Members Only

    Want to become a data engineer but aren’t sure which technologies are the right fit for the job? People switching into big data are faced with a difficult decision—should you learn MapReduce or Spark? The answer seems simple, but requires more information and insight. Answering this and other questions correctly places you on the path to becoming a data engineer. June 19, 2017 Register

  • Machine Learning – What’s All the Hype About?

    Machine learning is the analytics buzz word of the day. While some of the techniques have been around for decades, what has changed is the volume and diversity of data as well as the compute power to find insights in that data faster. That means that machine learning against disparate and big data can be used to get to insight – and fast. Machine learning is being used in predictive analytics in numerous use cases from customer behavior analysis to predictive maintenance to image recognition and more. The value is real and growing. June 21, 2017 Register

Upcoming Webinars

  • Metadata Discovery Doesn’t Have to be Painful

    Today’s BI and analytics landscape is more complicated than it ever has been. Most organizations have repositories of data everywhere, many BI and analytics technologies, and multiple streams of data integration and/or data preparation – all with their own sets of metadata. No wonder BI implementers and business users are frustrated, confused, and lost when it comes to using these critical assets. June 6, 2017 Register

  • Ask the Expert: Should You Learn MapReduce or Spark?
    TDWI Members Only

    Want to become a data engineer but aren’t sure which technologies are the right fit for the job? People switching into big data are faced with a difficult decision—should you learn MapReduce or Spark? The answer seems simple, but requires more information and insight. Answering this and other questions correctly places you on the path to becoming a data engineer. June 19, 2017 Register

  • Machine Learning – What’s All the Hype About?

    Machine learning is the analytics buzz word of the day. While some of the techniques have been around for decades, what has changed is the volume and diversity of data as well as the compute power to find insights in that data faster. That means that machine learning against disparate and big data can be used to get to insight – and fast. Machine learning is being used in predictive analytics in numerous use cases from customer behavior analysis to predictive maintenance to image recognition and more. The value is real and growing. June 21, 2017 Register

  • Architecting a Hybrid Data Ecosystem: Achieving Technical Cohesion and Business Value in a Multi-platform Environment

    One of the strongest trends in data management today and into the future is the development of complex, multi-platform architectures that generate and integrate an eclectic mix of old and new data, in every structure imaginable, traveling in time frames from batch to real time. The data comes from legacy, mainstream enterprise, Web, and third-party systems, which may be home grown, vendor built, open source, or a mix of these. More sources are coming online from machines, social media, and the Internet of Things. These data environments are hybrid and diverse in the extreme, hence the name hybrid data ecosystems (HDEs). June 22, 2017 Register

  • Augmenting and Enriching Data Sets for Analytics Value

    As BI and analytics become more mainstream, organizations are realizing that it makes sense to both enrich and augment their data in order to gain more insight. Successful companies realize that utilizing traditional structured data only for analytics is a non-starter. Organizations are more often adding ‘new’ data sources to the mix, including demographic data, text data, and geospatial data to their data sets. They are also looking for external data, such as social media data, weather data, and other third-party sources. The demand from data consumers has also driven many new organizations to pursue sharing their data. Many of these data sources are cloud-based. June 29, 2017 Register

  • Augmenting and Enriching Data Sets for Analytics Value

    As BI and analytics become more mainstream, organizations are realizing that it makes sense to both enrich and augment their data in order to gain more insight. Successful companies realize that utilizing traditional structured data only for analytics is a non-starter. Organizations are more often adding ‘new’ data sources to the mix, including demographic data, text data, and geospatial data to their data sets. They are also looking for external data, such as social media data, weather data, and other third-party sources. The demand from data consumers has also driven many new organizations to pursue sharing their data. Many of these data sources are cloud-based. June 29, 2017 Register

  • Ask the Expert: Ask the Expert on Data Maturity
    TDWI Members Only

    An increase in data maturity correlates to an increase in business success. Yet though organizations gladly allocate budget to business projects, they neglect data maturity—even to the point of allowing it to deteriorate. July 17, 2017 Register

  • Accelerating the Path to Value with Business Intelligence and Analytics: A TDWI Best Practices Research Report

    Organizations of all sizes are in competition to realize value from data – and to realize it faster. To do so, they increasingly need flexible and agile business intelligence(BI), analytics, and data infrastructure, not systems that take too long to develop and do not give users the dynamic, iterative, and interactive access to data that they need. Fortunately, technology developments are trending in a positive direction for organizations seeking to accelerate their path to value with BI, analytics, and the critical supporting data infrastructure. These include self-service BI and visual analytics, self-service data preparation, cloud computing and software as a service(SaaS), and new data integration technologies. July 19, 2017 Register

  • Ask the Expert: Three Big Dilemmas of BI
    TDWI Members Only

    Most business intelligence (BI) systems were initially designed to support managed forms of reporting and simple analytics. Reports in these BI systems needed to be auditable, governable, tested, required high data quality, and so on. Now, however, organizations want to do more with their BI systems than reporting. October 26, 2017 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

Ask the Expert: Organizational Risk Factors for Achieving Data-Driven Success
TDWI Members Only

Modern organizations spend significant time and effort working to transform their operations into a data-driven culture enabled by big data and analytics. The true value offered from analytics should be measured by observed upticks in targeted and desired business outcomes.

Mark Peco


New Data Practices for a Single Customer View and Omnichannel Marketing

Marketing has been one of the top beneficiaries of significant advances in data management, software automation, and customer analytics that enable a single view of the customer and power omnichannel marketing. Customer views and channel marketing are now inherently scalable to vast amounts of data, which enables marketers to track customer behavior in unprecedented detail across multiple channel contexts.

Philip Russom


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


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


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


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


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