Todas as histórias de

When the long-established company Henkel began its “Industry 4.0” journey in 2013, no one could have imagined how quickly digitization would take place—and how valuable this data accessibility would one day be in the year of coronavirus.

Hello, Tableau community! Welcome to Andy's June round-up of the best content being produced by you, the Tableau community.

This blog post covers four reasons why organizations should consider embedded analytics.

In the midst of #COVID19, state and local governments are looking to the power of data to provide answers. We chatted with David Kopniske to get a perspective on how data plays a central role to help navigate this crisis.

We're excited to announce a new Tableau Bridge designed to save time and energy with centralized management, role separation with self-service for publishers, and improved scalability.

COVID-19 stands out as the largest and most widespread disease outbreak we’ve had to confront. Let’s look back into history to see how outbreaks in the past gave rise to practices that still inform how we manage and understand diseases today.

Before COVID-19, the biggest challenge facing many communities across the U.S.—including Tableau’s home of Seattle—was homelessness. The number of people experiencing homelessness on a given night has risen for the past several years, due to a range of factors from housing unaffordability to poverty.

Tableau Foundation and Community Solutions have been working together since 2016 to measurably reduce—and ultimately end—homelessness in communities across the U.S. Through a new $2 million commitment from Tableau Foundation, we’re accelerating the use of data to drive toward an end to homelessness by improving coordination across services and support organizations.

For those that want to contribute to the conversation or educate yourself further, we’ve compiled several resources on the complexities of the data and the context surrounding it. Here's what you should know.

Discover all the capabilities of this spiritual successor to Extract API 2.0.

Data lakes store raw data without defining a schema before loading it, making it easier to collect raw data from multiple systems. Because the data is centralized, organizations are able to utilize best-in-class and cost-optimized approaches to analytics. This is why InCrowd Sports decided to leverage a cloud-based data lake architecture for their analytics needs, employing Dremio and Tableau.