RESEARCH & RESOURCES

Featured Webinars

  • 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

  • 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. July 26, 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

Upcoming Webinars

  • 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

  • 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. July 26, 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

  • 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

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

Accelerating the Path to Value with Hybrid Analytics Architecture

In today’s demanding economic environment, companies that can develop and deploy analytics faster have a significant competitive edge. They can use analytics to detect patterns and changes in markets, learn customer preferences, be alert to fraudulent activity, and more. With the advent of cloud computing, users quickly gain access to new data sources and analytic techniques, enabling companies to finally unleash their analytics – they are no longer constrained by the limits of their on-premises computing, database platform, data warehouse, and data storage capacity. However, to avoid even more data siloes, data governance issues, and more, organizations should consider a hybrid analytics architecture that brings together on premises and cloud, enabling a more controlled journey to the cloud, while enjoying the flexibility, power, and speed they need to handle a range of analytics demands.

David Stodder

Content Provided by TDWI, IBM


Emerging Design Patterns for Data Management

Organizations that seek to be data-driven are experiencing considerable change of late, because data itself, the management of data, and the ways businesses leverage data are all evolving at accelerated rates. These changes sound like problems, but they are actually opportunities for organizations that can embrace new big data, implement new design patterns and platforms for data, scale to greater volumes and processing loads, and react accordingly via analytics for organizational advantage.

Philip Russom, Ph.D.


SQL for Hadoop: When to Use Which Approach

In a 2015 survey by TDWI, 69% of respondents identified SQL on Hadoop as a must-have for making Hadoop ready for enterprise use. This is not surprising because both technical and business users know and love SQL, plus have portfolios of tools that rely on it. The catch is that early versions of Hadoop were devoid of ANSI-standard SQL.

Philip Russom, Ph.D.

Content Provided IBM, Looker, Teradata


The Modern Data Warehouse: What Enterprises Must Have Today and What They’ll Need in the Future

Many organizations need a more modern data warehouse platform to address a number of new and future business and technology requirements. Most of the new requirements relate to big data and advanced analytics, so the data warehouse of the future must support these in multiple ways, while still supporting older data types, technologies, and business practices. Hence, a leading goal of the modern data warehouse is to enable more and bigger data management solutions and analytic applications, which in turn help the organization automate more business processes, operate closer to real time, and through analytics learn valuable new facts about business operations, customers, products, and so on.

Philip Russom, Ph.D.


Data-centric Security- Seven Best Practices for Protecting Your Most Sensitive Data

As organizations incorporate newer data strategies, they also need to consider data-centric security. Data-centric security focuses security controls on the data, rather than perimeter servers or other infrastructure or the network. The goal is to protect sensitive data where it is stored and where it moves. This is becoming increasingly important as organizations start to deal with big data and newer data management platforms and hybrid architectures that include Hadoop and the cloud. Yet, TDWI research suggests that organizations still seem to focus on perimeter security and on application centric security for sensitive data. They think they are focused on protecting their data, but the reality is that many organizations don’t classify their data or know where their sensitive data lives, much less how to protect it.

Fern Halper, Ph.D.


Peering Under the Hood: Fine-Tuning Solutions for Hard Operational Data Governance

Many organizations are responding to their raised awareness of the need for data governance by introducing data governance programs, hiring Chief Data Officers, and forming a data governance council. And while there are numerous guidelines and methods for the operating models for a data governance practice, recommendations regarding its day-to-day operationalization are much harder to come by. Specifically, how does an organization design an operational environment for instituting business data policies for usability and enforcing those policies consistently across the enterprise? Answering this question is necessary for achieving the data governance discipline without getting in the way of the business.

David Loshin


Dynamic Metadata: Enabling Modern BI Architecture

In a highly competitive market, today’s forward-looking organizations are seeking to optimize and modernize their IT investments, specifically in enterprise business intelligence (BI). There’s a strong push to capitalize on newer features such as self-service BI, advanced analytics, and customized visualizations—all of which relinquish the centralized data governance necessary for corporate and regulatory compliance.

David Stodder


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