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

Ask the Expert: Ask the Expert on Data Maturity

TDWI Members Only

An increase in data maturity correlates to an increase in business success. Yet though organizations gladly allocate budget to business projects, they neglect data maturity—even to the point of allowing it to deteriorate.

William McKnight

Augmenting and Enriching Data Sets for Analytics Value

As BI and analytics become more mainstream, organizations are realizing that it makes sense to both enrich and augment their data in order to gain more insight. Successful companies realize that utilizing traditional structured data only for analytics is a non-starter. Organizations are more often adding ‘new’ data sources to the mix, including demographic data, text data, and geospatial data to their data sets. They are also looking for external data, such as social media data, weather data, and other third-party sources. The demand from data consumers has also driven many new organizations to pursue sharing their data. Many of these data sources are cloud-based.

Fern Halper, Ph.D.

Sponsored by Snowflake

Architecting a Hybrid Data Ecosystem: Achieving Technical Cohesion and Business Value in a Multi-platform Environment

One of the strongest trends in data management today and into the future is the development of complex, multi-platform architectures that generate and integrate an eclectic mix of old and new data, in every structure imaginable, traveling in time frames from batch to real time. The data comes from legacy, mainstream enterprise, Web, and third-party systems, which may be home grown, vendor built, open source, or a mix of these. More sources are coming online from machines, social media, and the Internet of Things. These data environments are hybrid and diverse in the extreme, hence the name hybrid data ecosystems (HDEs).

Philip Russom, Ph.D.

Sponsored by Denodo

Ask the Expert: Should You Learn MapReduce or Spark?

TDWI Members Only

Want to become a data engineer but aren’t sure which technologies are the right fit for the job? People switching into big data are faced with a difficult decision—should you learn MapReduce or Spark? The answer seems simple, but requires more information and insight. Answering this and other questions correctly places you on the path to becoming a data engineer.

Jesse Anderson

Data Management for Big Data, Hadoop, and Data Lakes

A perfect storm of data management trends is converging. First, organizations across many industries are experiencing the big data phenomenon, which forces them to capture and leverage data from new sources, in structures and velocities that are new to them, in unprecedented volumes. Second, technical users are scrambling to learn new data platforms like Hadoop and their evolving best practices. Third, the data lake arose suddenly in 2016 as the preferred approach to managing very large repositories of raw source data. Fourth, business managers have attained a new level of sophistication in their use big data for business value and organizational advantage.

Philip Russom, Ph.D.

Sponsored by Arcadia Data, HVR, Paxata, Panoply

Database Strategies for Modern BI and Analytics

The data universe has changed. Big data, cloud computing, and open source have dramatically expanded the number of data warehousing offerings available to today’s businesses. An increasing number of companies are implementing self-service business intelligence (BI) and visual analytics tools to access and make sense of all of the new and diverse sources of data their teams are consuming. Data literacy is changing equally fast as an increasing number of “data consumers” want to interact with data on their own rather than through IT.

David Stodder

Sponsored by Looker

End Your Data Struggle: How to Seamlessly Analyze Disparate Data

Many organizations today are struggling to get value from their data and advanced analytics initiatives. The struggle begins with data diversity, as organizations are trying to support new apps, customer channels, sensors, and social media outlets. Each source may have its own data structure, quality, and container (in the form of files, documents, messages). The struggle is exacerbated by the exploding volume of data that must be captured, processed, stored, and delivered to the right users in a state that is fit for their own individual needs.

Philip Russom, Ph.D.

Sponsored by Snowflake