Data observability is the ability to monitor and understand the data that flows through an organization's systems. Organizations can monitor their data in real-time, detect anomalies, and take corrective action based on alerts. Organizations use data observability to collect, analyze, and visualize data from various sources to manage their system's behaviour across the data ecosystem.

Good data hygiene means data is correct and easily used to draw insight. This definition then begs the question: How do you achieve it?

Retrieving data from a source, ensuring it suits business requirements, and moving that data into a target data source is critical to any data strategy. Low-code tools can help create robust and flexible ETL processes that automate your data loading.

Building a data-driven pricing platform for speed, scale and automation with BigQuery, Looker and more.

Trans World Airlines (TWA). Pan American Airlines. Sports Authority, Staples, Woolworth's, and Banana Republic. Aside from some minor similarities, these companies are all very diverse.

Since the 2006 launch of Amazon Web Services (AWS), the world’s first hyper-scale public cloud provider, thousands of data-driven businesses have shifted on-premise data storage and analytics workloads into the cloud by architecting or adopting a cloud data platform. As the volume, variety, and velocity of enterprise data continues to grow in 2023, cloud data platforms with legacy tech and complex architectures are becoming increasingly time-consuming and costly to manage.

Backcountry, the specialty retailer of premium outdoor gear and apparel, shares key lessons on using a modern data stack to overcome data silos, complexities with legacy systems and improve its customer experience.

Data plays a profound role in finance. In fact, some might argue that finance professionals are some of the most data-driven individuals in an organization. That’s because finance data, and the insights you draw from it, can literally make or break a company. This is especially true in times of economic uncertainty, when businesses are trying to make data-driven decisions about where to invest and cut resource allocation.