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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.

Durante los últimos tres años, Jones Lange LaSalle (JLL) ha cultivado una cultura de datos. Gracias a ello, pudieron responder con rapidez y eficiencia a los cambios provocados por la llegada de la COVID-19. Las inversiones de la empresa en análisis de datos les procuraron un retorno de USD 40 millones. Pudieron reducir los costos relacionados con el cumplimiento y los gastos generales, además de generar ingresos adicionales provenientes de nuevas iniciativas.

Learn more about the predictive modeling functions coming with Tableau 2020.3 that allow you to generate predictions and surface relationships in your data.

In June, we highlighted updates from the Hyper API teams and showcased a few solutions that our developer community entered in the latest Dev Site Mini Challenges.