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

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

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

Sponsored by IBM

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

Sponsored by IBM

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.

Sponsored by SAP

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.

Sponsored by IBM

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.

Sponsored by SAP