RESEARCH & RESOURCES

Featured Webinars

  • 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

Upcoming Webinars

  • 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

  • Defining a Multiplatform Data Architecture and What It Means to You

    A revolution is occurring in modern analytics, driven by our ability to capture new sources of information at a detail previously too complex and costly to imagine. As more data comes from new sources (from machines to social media) and is applied to new applications, data is evolving into greater diversity, including every variation of data type from unstructured to multistructured. Even as new tools to analyze and manipulate this newly available resource come online, it is not enough to look at the data manipulation layer alone. July 20, 2017 Register

  • Making Multiplatform Data Architectures Work for You: Common Use Cases and Reference Architectures

    To leverage the new wave of advanced data sources available, users and architects are turning to a multiplatform data architecture (MDA), where numerous diverse data platforms and tools are integrated in a multiplatform, distributed architecture. An MDA is typified by an extreme diversity of platform types that may include multiple brands of relational databases, NoSQL platforms, in-memory functions, and tools for data integration, analytics, and stream processing. Any of these may be on premises, in the cloud, or in hybrid combinations of the two. August 17, 2017 Register

  • IoT’s Impact on Data Warehousing: Defining IoT in Terms of Its Data Requirements

    The Internet of Things (IoT) is a computing paradigm where a widening range of physical devices—including smartphones, vehicles, shipping pallets, kitchen appliances, manufacturing robots, and anything fitted with a sensor—can transmit data about their location, state, activity, and surroundings. Depending on the device type, some may also receive data and instructions that control device behavior. September 14, 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

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).

Philip Russom, Ph.D.


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.

Fern Halper, Ph.D.


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.

Jesse Anderson


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.

Claudia Imhoff, Ph.D.


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, Ph.D.


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.


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