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Data paves the way for every strategic move made by banks and insurance companies. Whether looking to create a new service, complying with regulations, or overhauling and re-engineering legacy operations, a massive data project is always central to the effort. For financial services businesses, the pace at which they can reshape and repurpose data has become a key determinant of their ability to predict market trends and meet client expectations.

Last week in our BigQuery Reference Guide series, we spoke about the BigQuery resource hierarchy - specifically digging into project and dataset structures. This week, we’re going one level deeper and talking through some of the resources within datasets. In this post, we’ll talk through the different types of tables available inside of BigQuery, and how to leverage routines for data transformation.

When it comes to anomaly detection, one of the key challenges that many organizations face is that it can be difficult to know how to define what an anomaly is. How do you define and anticipate unusual network intrusions, manufacturing defects, or insurance fraud? If you have labeled data with known anomalies, then you can choose from a variety of supervised machine learning model types that are already supported in BigQuery ML.

During the product keynote at our recent QlikWorld online event, we unpacked the power of the analytics data pipeline to transform raw data into informed action. Imagine a data pipeline where information flows continuously into everyday processes, allowing your organization to seize every business moment, as it happens...

Mercury, the Roman god of commerce, is often depicted carrying a purse, symbolic of business transactions, wearing winged sandals, illustrating his abilities to move at great speeds. Transactions power the world’s business systems today, ranging from millions of packages moving worldwide tracked in real time by logistics companies to global payments from personal loans to securities trading to intergovernmental transactions, keeping goods and services flowing worldwide.

In financial services, data is essential for storing product information, capturing customer details, processing transactions and keeping records of accounts; the relationship between products and their underlying data has always been symbiotic. A significant amount of data infrastructure is static, fragmented across data silos or based on legacy platforms. This has created an impedance mismatch between products and the underlying data.