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

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

Navigating the Predictive Analytics Market

Predictive analytics is on the verge of widespread adoption. Enterprises are extremely interested in deploying predictive capabilities. In a recent TDWI survey about data science, about 35 percent of respondents said they had already implemented predictive analytics in some way. In a 2017 TDWI education survey, predictive analytics was the top analytics-related topic respondents wanted to learn more about.

Fern Halper, Ph.D.

Sponsored by SAS, Alteryx, Cloudera

Making Multiplatform Data Architectures Work for You: Common Use Cases and Reference Architectures

To leverage the new wave of advanced data sources available, users and architects are turning to a multiplatform data architecture (MDA), where numerous diverse data platforms and tools are integrated in a multiplatform, distributed architecture. An MDA is typified by an extreme diversity of platform types that may include multiple brands of relational databases, NoSQL platforms, in-memory functions, and tools for data integration, analytics, and stream processing. Any of these may be on premises, in the cloud, or in hybrid combinations of the two.

Philip Russom, Ph.D.

Sponsored by SAP

Ask the Expert: Data Science

TDWI Members Only

It’s hard to find a topic out there hotter than Data Science right now; and can be equally hard to find one more confusing. Data Science techniques have revolutionized nearly any industry you can imagine, and in some cases created whole new ones from thin air. Despite this, much of Data Science remains couched in mystery--a magic black box that is supposed to solve all of our problems.

Frank Evans

Get More Business Value from a Data Lake via Data-as-a-Service (DaaS)

Data lakes are coming on strong as a modern and practical way of managing the large volumes and broad range of data types and sources that enterprises are facing today. TDWI sees data lakes managing diverse data successfully for business-driven use cases, such as omni-channel marketing, multi-module ERP, the digital supply chain, and data warehouses extended for business analytics. Yet, even in business-driven examples like these, user organizations still haven’t achieved full business value and return on investment from their data lakes.

Philip Russom, Ph.D.

Sponsored by Unifi Software