Did you know that only 32% of companies report realizing tangible and measurable value from their data, and only 27% ​​say data and analytics projects produce insights and recommendations that are highly actionable?

Looking to level up your data-driven decision making? Machine learning and artificial intelligence models in GCP can help you predict the future.

In our previous posts, we talked about how data warehouses and data lakes can help us manage our data in a more secure, cost-effective, reliable, and scalable way, as well as how to use GCP to improve our data pipelines and their orchestration. But it doesn’t matter how rich or robust your data structure is if you cannot use it to leverage your business decision making.

What is data orchestration? Why do we need it? And how should your company go about it? Let's explore the best tools and practices for data orchestration.

In our previous posts, we talked about how Data Warehouses and Data Lakes can help us manage our data in a more secure, cost-effective, reliable, and scalable way. But there is another critical concept we need to understand in order to work with these architectures: Data Pipelines. 

Nearly every business is facing new challenges in how to store and effectively utilize data. Today we'll look at how data lakes solve these challenges and why GCP is the available, scalable, cost-efficient data lake hosting solution your business needs.

The concept of Data Warehouses (DWs) has been around since the late 80s, and it is still a critical component for any company that wants to adopt a data-driven culture. Here's why Google Cloud Platform's BigQuery might be the right choice to host your data warehouse.