Learn the basics of Google BigQuery in this introduction as well as ideal use cases and best practices.

It is incredible to see how much businesses rely on data today. 80% of business operations are running in the cloud, and almost 100% of business-related data and documents are now stored digitally. In the 1960s, money made the world go around but in today’s markets, “Information is the oil of the 21st century, and analytics is the combustion engine.” (Peter Sondergaard, 2011)

Data helps businesses gain a better understanding of processes, improve resource usage, and reduce waste; in essence, data is a significant driver to boosting business efficiency and profitability.

This reliance on data isn’t without challenges though. A business can have large data warehouses and no efficient way of processing the data in them. There is also the challenge of sorting valuable data from noise, primarily when you collect data from public sources. Amassing data is meaningless without the tools and means to process, analyze, and act on it. So the important questions are: How can you make this process painless and how do you become a successful data-driven company? The answer to both lies in Google BigQuery.

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