RESEARCH & RESOURCES

Featured Webinars

  • Supercharging Operational Analytics with Spark and Change Data Capture

    Organizations can gain competitive advantages if they can eliminate the latency that prevents them from getting a real-time view of operations. In many cases, operational reporting and analytics are still tied to traditional business intelligence, ETL, and data warehousing that can only deliver historical views of the data. Executives and managers have to wait – and the wait is getting longer and data volumes rise and analytics grow more complex. August 28, 2018 Register

  • The Three Pillars of Data Governance: Compliance, Trust, and Transformation

    Many organizations initiate data governance programs because of pressing compliance issues that impact data usage. At the other end of the spectrum, some organizations begin by governing data that’s shared broadly through a community of consumers across the enterprise, each with their own perspective on what constitutes quality and usability. August 30, 2018 Register

  • Improving Data Sharing Through Modernized Data Accessibility

    Organizations have struggled to easily provide access to sharable data assets ever since the early days of decision support systems. Traditional methods of data sharing are slow and cumbersome. Simple data sharing methods—such as emailing extracted files, opening up unsecured access to critical databases, or file exchanges via FTP or EDI—not only pose risks of inadvertent data exposure, they also fundamentally require manual steps for both the individual sending the data and the individual receiving the data. September 12, 2018 Register

Upcoming Webinars

  • Supercharging Operational Analytics with Spark and Change Data Capture

    Organizations can gain competitive advantages if they can eliminate the latency that prevents them from getting a real-time view of operations. In many cases, operational reporting and analytics are still tied to traditional business intelligence, ETL, and data warehousing that can only deliver historical views of the data. Executives and managers have to wait – and the wait is getting longer and data volumes rise and analytics grow more complex. August 28, 2018 Register

  • The Three Pillars of Data Governance: Compliance, Trust, and Transformation

    Many organizations initiate data governance programs because of pressing compliance issues that impact data usage. At the other end of the spectrum, some organizations begin by governing data that’s shared broadly through a community of consumers across the enterprise, each with their own perspective on what constitutes quality and usability. August 30, 2018 Register

  • Improving Data Sharing Through Modernized Data Accessibility

    Organizations have struggled to easily provide access to sharable data assets ever since the early days of decision support systems. Traditional methods of data sharing are slow and cumbersome. Simple data sharing methods—such as emailing extracted files, opening up unsecured access to critical databases, or file exchanges via FTP or EDI—not only pose risks of inadvertent data exposure, they also fundamentally require manual steps for both the individual sending the data and the individual receiving the data. September 12, 2018 Register

  • Modernizing Data Architecture With Streaming CDC

    Expanding analytics requirements have increased the appetite for massive data volumes. However, these data flows can create bottlenecks, preventing timely and modern analytics innovations such as machine learning. September 13, 2018 Register

  • Better Business Decisions Through an AI-Enabled Data Catalog

    Today, there are many opportunities to use data in new and exciting ways to drive business decisions. They range from better treatment plans for patients to improved logistics to better targeting display ads to potential customers. Organizations are looking to make better decisions using analytics. In fact, TDWI routinely sees that the principle driver for any data technology, be it data warehouse modernization or predictive analytics, is better decision making. September 18, 2018 Register

  • Getting Started with Data Integration in the Cloud

    Cloud continues to rise in importance as a platform for many IT systems, including those for data integration. Many organizations have now achieved a maturity level where they are using multiple cloud-based applications and online data sources. These users now need data integration tool platforms that support hybrid data environments so they can unify on-premises and cloud-based data sources and targets. Similarly, users increasingly need data integration processing to run natively on clouds (not just on premises), so that data integration functions and related capabilities are closer to software-as-a-service (SaaS) applications, Web data sources, multiple clouds, and increasingly popular cloud-based databases, data lakes, and data warehouses. September 19, 2018 Register

  • New Practices in Data Cataloging

    Are you tired of starting with a blank slate every time you begin a new analytics assignment? That’s what happens when you spend your precious time researching the same data sources as last time and assembling yet another aggregated data set prior to doing what you really need to do. Creating a new analysis can have a positive business impact on your enterprise. September 25, 2018 Register

  • Modern Metadata Management: Boosting BI capacity with automation and machine learning

    Metadata management continues to be a powerful enabler for mission-critical data-driven business activities, including operations, analytics, and compliance. That’s because metadata is the golden thread that stitches together enterprise-wide landscapes, even those that are heavily distributed and heterogeneous, with hybrid mixes of on-premises and cloud systems. September 27, 2018 Register

  • Multiplatform Data Architectures

    A multiplatform data architecture (MDA) contains data distributed across multiple databases, open source or big data platforms, file systems, clouds, and other data platforms. An MDA is characterized by its large number and diversity of data persistence platforms, as well as its broad range of data structures, types, and containers. Equally important, however, is the MDA’s substantial data management infrastructure, which unifies the MDA’s architecture by integrating, synchronizing, cleansing, mastering, and documenting data across the MDA’s many platforms and beyond. October 10, 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

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.

Fern Halper, Ph.D.


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.

David Stodder


Cloud or On Premises for Analytics: Why Not Choose Hybrid?

Business users today are clamoring for more analytics so they can explore data in detail and build business-driven analytic models. However, many organizations are on the horns of a dilemma: Where should they put the data and process analytics workloads? The cloud offers tempting flexibility and price points—plus the “gravity” of cloud-based applications is pulling more data generation into the cloud, and it can make sense to do analytics processing on cloud platforms. On the other hand, on premises is where many organizations feel more comfortable and have more experience from data management, governance, and security perspectives. It’s a big decision.

David Stodder


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?

Philip Russom, Ph.D.


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.

Fern Halper, Ph.D.


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.

Fern Halper, Ph.D.


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.

David Stodder


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