RESEARCH & RESOURCES

Featured Webinars

  • 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. June 19, 2018 Register

  • 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. June 20, 2018 Register

  • 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. June 21, 2018 Register

Upcoming Webinars

  • 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. June 19, 2018 Register

  • 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. June 20, 2018 Register

  • 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. June 21, 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. July 10, 2018 Register

  • 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. July 12, 2018 Register

  • 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

  • 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

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 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.

Fern Halper, Ph.D.


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.

David Stodder


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.

Fern Halper, Ph.D.


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.

Rick van der Lans


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.

Aaron Fuller


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.

Claudia Imhoff, Ph.D.


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


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