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.

Sponsored by SAP

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

Sponsored by Qlik®

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.

Sponsored by SAS, ThoughtSpot, Vertica

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

Sponsored by Trillium Software

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.

Sponsored by Collibra

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

Sponsored by Snowflake