TDWI | Training & Research | Business Intelligence, Analytics, Big Data, Data Warehousing

Making Predictive Analytics Work – 5 Keys to Successful Model Deployment and Management

This webinar focuses on five keys to successful model deployment, management, and the current state of predictive analytics/machine learning.

Fern Halper, Ph.D.

Sponsored by Alteryx

Ask the Expert on The UX Guide to Analytics

TDWI Members Only

In this Ask the Expert webinar, Nick will lead attendees through a journey of what processes and methodologies the world’s leading analytics consulting firms use to maximize adoption, reduce risk and achieve business value through behavioral change. Nick will then answer any questions you have on how to leverage all or parts of the process into your own analytics processes.

Nicholas Kelly

Enabling Data Science to Be Data Science: Strategies for Increasing Self-Service Data Science

View this TDWI Webinar to learn how you can create a data science strategy that benefits from less dependence on IT and affords greater self-service capabilities for both data scientists and business users. We will discuss how trends in technologies and practices are opening up opportunities to significantly reduce the data management drag on data science projects so that there can be greater focus on what adds the most value to the business.

David Stodder

Sponsored by SAP

What It Takes to Be Data-Driven: Technologies and Practices for Becoming a Smarter Organization

Join Fern Halper, TDWI VP and senior research director for advanced analytics, and David Stodder, senior research director for BI, to learn more about the research from their new Best Practices Report about what it takes to become data-driven. They will discuss their findings and present recommendations for organizational and technology best practices.

Fern Halper, Ph.D., David Stodder

Sponsored by Hortonworks, Looker, SAP, SAS

Evolution of the Data Lake—Implementing Real-Time Change Data in Hadoop

A ten-fold increase in worldwide data by 2025 is one of many predictions about big data. With such growth rates in data, the “data lake” is a very popular concept today. Everybody touts their platform capabilities for the data lake, and it is all about Apache Hadoop. With its proven cost-effective, highly scalable, and reliable means of storing vast data sets on cost-effective commodity hardware regardless of format, it seems to be the ideal analytics repository. However, the power of discovery that comes with the lack of a schema also creates a barrier for integrating well-understood transaction data that is more comfortably stored in a relational database. Rapidly changing data can quickly turn a data lake into a data swamp.

Krish Krishnan

Sponsored by IBM

Up to the Minute: The Need for Rapid Adoption of Streaming Data

As Internet of Things (IoT) technologies become more common and web data grows in volume, there is growing evidence that the ability to analyze continuous data is not only valuable but necessary. In fact, those with the ability to capture and analyze massive numbers of independent continuous data streams will have a powerful capability that will help them to power operational intelligence and predictive analytics. A growing number of applications increasingly rely on fast analysis, but tomorrow’s world will be even more dependent on up-to-the-minute consumption of data streams.

David Loshin

Sponsored by Snowflake

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.

Keith McCormick