RESEARCH & RESOURCES

Featured Webinars

  • Getting Data In: Answering the Challenge of Growing Sources of BI Data

    Data, data everywhere… Today’s BI and analytics implementation experts are faced with increasing volumes and sources of data – on premises and off – new and innovative technologies, more complex data integration and quality issues, and difficulties in maintaining and enhancing these diverse BI architectures. October 17, 2017 Register

  • Considerations for Cloud Data Quality Tool Solutions

    Cloud software offerings have exploded in the data management and governance scene in a big way. Longstanding leaders in the data quality tool market are releasing cloud versions of their DQ platforms while upstart cloud-only competitors attempt to gain market share by selling more lightweight toolsets, often directly to business divisions rather than IT. Interesting hybrid architectures are also being tested, sometimes with multiple vendors and sometimes with multiple types of implementations of the same vendors’ tools. October 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 20, 2017 Register

Upcoming Webinars

  • Getting Data In: Answering the Challenge of Growing Sources of BI Data

    Data, data everywhere… Today’s BI and analytics implementation experts are faced with increasing volumes and sources of data – on premises and off – new and innovative technologies, more complex data integration and quality issues, and difficulties in maintaining and enhancing these diverse BI architectures. October 17, 2017 Register

  • Considerations for Cloud Data Quality Tool Solutions

    Cloud software offerings have exploded in the data management and governance scene in a big way. Longstanding leaders in the data quality tool market are releasing cloud versions of their DQ platforms while upstart cloud-only competitors attempt to gain market share by selling more lightweight toolsets, often directly to business divisions rather than IT. Interesting hybrid architectures are also being tested, sometimes with multiple vendors and sometimes with multiple types of implementations of the same vendors’ tools. October 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 20, 2017 Register

  • Advanced Analytics: Moving Toward Machine Learning, Natural Language Processing, and AI

    There is a lot of excitement in the market about machine learning, natural language processing, and AI. Although many of these technologies have been available for decades, new advancements in compute along with some new algorithmic developments are making these technologies more attractive. More organizations are embracing these advanced technologies for a number of reasons, including improving operational efficiencies, better understanding behaviors, and to gain competitive advantage. October 24, 2017 Register

  • Three Ways to Succeed with Embedded Analytics

    One of the most effective ways to spread the value and accelerate the adoption of business intelligence (BI) and analytics is to embed it into operational applications. End users and customers value the ability to model, monitor, ask, and answer questions throughout the workflow of familiar business applications. In this webinar, you will learn three ways BI and analytics are typically embedded into operational applications, new embedded use cases, and what to consider in your embedded analytics evaluation. October 26, 2017 Register

  • Big Data in the Cloud: Strategies for Analytics Success

    Big data is becoming the norm for many organizations, which is a good thing because it can provide a great deal of insight. Big data includes large volumes of disparate data types: structured data as well as “newer” data such as text, images, geospatial and streaming data. Analyzing newer kinds of data is becoming mainstream. November 7, 2017 Register

  • Ask the Expert on Determining the Economic Value of Data (EvD)
    TDWI Members Only

    Most organizations lack a road map for leveraging data and analytics to optimize key business processes, uncover new business opportunities or deliver a differentiated customer experience. They do not understand what’s possible with respect to integrating data and analytics into the business model. And the Internet of Things only exacerbates the volume and variety of data that organizations could be capturing. November 9, 2017 Register

  • Empowering the Citizen Data Scientist: How to You Can Expand the Business Impact of Analytics

    With analytics becoming increasingly essential to all aspects of business, many organizations are facing a crisis. They need data science so that they can extract maximum value from data, but there still too few data scientists to go around. Some even say that trying to hire them is like “chasing unicorns” because they are so rare. A better strategy is to take advantage of newer technologies and practices aimed at enabling more personnel in organizations to engage in data science themselves rather than be dependent on rarified experts. While data scientists continue to be important for advanced requirements, organizations can now democratize data science so that more executives, managers, and other business users can take advantage. They can empower a new breed of “citizen data scientists.” November 16, 2017 Register

  • Location Analytics for your Data Lake: Driving New Business Insights and Outcomes

    Location information has been a growth area in recent years in data management, as user organizations of many sizes and industries have realized how location information can inspire new business insights, practices, and outcomes. In response, many users have reworked older enterprise data environments, to enrich the data with more location information. At the same time they have begun capturing data from new sources that include location information, especially from sensors, machines, devices, vehicles, and the Internet of Things (IoT). Much of this new data is being managed in data lakes, which in turn are usually deployed atop Hadoop. November 30, 2017 Register

  • Ask the Expert on the Roles and Construct of a Thriving Analytic Team
    TDWI Members Only

    Most organizations believe they will achieve better analytic results if they populate a deeper bench of experienced data scientists and machine learning practitioners. But this is akin to building a home exclusively with highly skilled framers, brick layers and cabinet makers. You’ll end up with a solid structure and great workmanship, but not a true functional home. December 14, 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

Understanding and Overcoming Challenges in Data Warehouse Modernization

As the rate of data management innovation accelerates, many data warehouse professionals are beginning to identify where gaps in the conventional data warehouse architecture prevent the organization from getting the best advantage from its information assets. Open source platforms, big data systems, and cloud computing all promise to revolutionize the pervasiveness of business intelligence and analytics across the organization. Consequently, many of these professionals are exploring ways to modernize their business intelligence, reporting, and analytics environments.

David Loshin


Ask the Expert on Data Literacy
TDWI Members Only

Businesses of all types and sizes are becoming more and more defined by their data. As this happens, it is equally important to improve the ability of managers, staff and even the general public, to make decisions which are well-informed by an understanding of the data behind their choices. Data literacy is the ability to understand the nature of the data we work with, and the ways in which we can interpret and communicate through our use of this important resource.

Donald Farmer


Relational Database Vendors are Going Big on Big Data

The number of options for storing, manipulating and accessing data have exploded over the last decade. Open source “NoSQL” (not-only SQL) have spread like wildfire among organizations with cutting-edge analytics. They, along with Hadoop, have lowered the cost barrier to powerful, flexible and incredibly scalable implementations of systems that access unstructured, semi-structured and flexibly-structured data in addition to relational data. However, the learning- and investment-curves have been prohibitive for many organizations. It is all too common that a company would like to advance its database and analytics capabilities with non-relational data but they don’t have the time, human resources or budgets to dedicate to spinning-up and learning how to use these new technologies.

Aaron Fuller


Master Data Management – Avoiding the Potholes

Andy Hayler brings his wealth of practical experience of master data management projects to bear in order to explore best practice in MDM. Drawing heavily on practical project experience, supplemented by survey data from customer projects, he explains the most common problems that MDM projects encounter, and how to avoid them.

Andy Hayler


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.

Philip Russom, Ph.D.


Integration and Governance for Big Data, Data Lakes, and Hadoop. Yes, you can do it.

In this presentation, we discuss the need for creating a managed data environment that supports the needs of all users of analytical data while ensuring the creation of governed, sharable, and portable data integration and governance work products.

Claudia Imhoff, Ph.D.


Putting Machine Learning to Work in Your Enterprise

Everyone is talking about machine learning—software that can learn without being explicitly programmed, machine learning (and deep learning) can access, analyze, and find patterns in big data in a way that is beyond human capabilities. The technology is being used in a wide range of industries for use cases including fraud prevention, predicting crop yields, preventing and mitigating natural disasters, predictive maintenance of enterprise assets, and improving supply chain efficiencies.

Fern Halper, Ph.D.


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