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

Big Data Management Best Practices for Data Lakes

Organizations are pursuing data lakes in a fury. Organizations in many industries are attempting to deploydata lakes for a variety of purposes, including the persistence of raw detailed source data, data landing and staging, continuous ingestion, archiving analytic data, broad exploration of data, data prep, the capture of big data, and the augmentation of data warehouse environments. These general design patterns are being applied to industry and departmental domain specific solutions, namely marketing data lakes, sales performance data lakes, healthcare data lakes, and financial fraud data lakes.

Philip Russom, Ph.D.


Seven Strategies for Achieving Big Data Analytics Maturity

Big data analytics is full of potential – but also fraught with pitfalls, obstacles, and a fog of hype surrounding the technologies. To be successful, organizations need to know where to begin with big data analytics and how to sustain progress so that they can achieve objectives. With key strategic initiatives hinging on success with big data analytics – including developing competitive innovations in customer intelligence and engagement, fraud detection, security, and product development – organizations need a roadmap for how to move ahead.

Fern Halper, Ph.D., David Stodder


Making Data Preparation Faster, Easier, and Smarter

Business users, business analysts, and data scientists have diverse data needs and specialties, but they all have one thing in common: they are tired of long, complicated, and tedious data preparation. Unfortunately, data preparation is getting even more difficult as users doing analytics and data discovery reach out to larger volumes of different types of data.

David Stodder


Discover 5 Keys to IoT Success: TDWI’s New IoT Readiness Assessment

The Internet of Things (IoT) is hot and getting hotter. Consumers use it for health monitoring and “smart” home devices, such as thermostats and appliances. On the business front, a piece of equipment—or any business asset, really—can be tagged, monitored, and analyzed. This might include a sensor-enabled pressure valve on a piece of drilling equipment, a tagged piece of construction material, food moving to market, or a chip placed in an employee badge, not to mention smart cities, smart power grids, and more.

Fern Halper, Ph.D.

Content Provided by IBM, Teradata, Tibco


The What, Why, When, and How of Data Warehouse Modernization

Despite their ongoing evolution, data warehouses (DWs) are more relevant than ever as they support operationalized analytics and wring business value from machine data and other new forms of big data. In the age of big data analytics, it’s important to modernize a DW environment to keep it competitive and aligned with business goals.

Philip Russom, Ph.D.


BI, Analytics, and the Cloud: Strategies for Business Agility

Cloud computing is a major trend that offers advantages in terms of flexibility, dynamic scalability, and agility. Even so, there’s been a lot of marketing hype. The reality is that, until recently, cloud has been slow to take off for business intelligence (BI) and analytics. Organizations have been concerned about security, performance, functionality, and other critical issues. TDWI Research is now seeing a significant shift as more organizations show willingness to experiment with BI and analytics in the cloud and are moving into deployment.

Fern Halper, Ph.D., David Stodder


Governing Big Data and Hadoop

Big data presents significant business opportunities, when leveraged properly. And yet, big data also presents significant business and technology risks, when it is poorly governed or managed.

Philip Russom, Ph.D.


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