Business analysts working with larger and larger data sets are finding traditional BI methods can't keep up with their need for speed. BigQuery BI Engine is designed to meet this need by accelerating the most popular dashboards and reports that connect to BigQuery. With the freshest data available, your analysts can identify trends faster, reduce risk, match the pace of customer demand, even improve operational efficiency in an ever-changing business climate.

These days, there are two kinds of businesses: data-driven organizations; and companies that are about to go bust. And often, the only difference is the data stack. Data quality is an existential issue—to survive, you need a fast, reliable flow of information. The data stack is the entire collection of technologies that make this possible. Let's take a look at how any company can assemble a data stack that's ready for the future.

There is nothing permanent except change. In a world of turbulent, unpredictable change, we humans are always learning to cope with the unexpected. Hopefully, your machine learning business applications do this every moment, by adapting to fresh data. In a previous post, we discussed the impact of COVID-19 on the data science industry.

Today’s enterprise data science teams have one of the most challenging, yet most important roles to play in your business’s ML strategy. In our current landscape, businesses that have adopted a successful ML strategy are outperforming their competitors by over 9%. The implications of ML on the future of business are clear. However, only 4% of enterprise executives today report seeing success from their ML investment.

Start-ups founded on analytics have been shaking up every industry. Finance has been disrupted by Monzo's data focus, Netflix’s analytics has upended film entertainment, and Swyfft has used data to change the game for US home insurance. Today's users have come to expect analytics in their applications.

According to the European Banking Authority report on Advanced Analytics and Big Data in banking, the implementation of data technologies, infrastructure, and practices is still at “an early stage”. The game is on for early contenders in this winner-takes-most market. Banks that move quickly are likely to get ahead of the curve, grabbing more of the market pie before others rise to the challenge.

A watershed year brought continued rapid growth, major engineering advances, and partner awards from Google, AWS, Snowflake and Databricks.

Spreadsheets are everywhere! They are one of the most useful productivity tools available. They make organizing, calculating, and presenting data a breeze. Google Sheets is the spreadsheet application included in Google Workspace, which has over 2 billion users. Machine learning, or ML for short, has also become an essential business tool. Making predictions with data at low cost and high accuracy has transformed industries.

The component previewer is a feature that allows you to preview your data at each component step without having to validate packages and run full-scale production jobs. It gives you the ability to extract, transform and preview your data on any transformation component, allowing you to debug your pipeline and/or to confirm and validate your data flow logic. Component previews are similar to the data previews available on source components, which you might already be familiar with.

With a new year ahead, it’s time for financial services to pause, take stock of the “new normal,” and plan a path forward. COVID-19 forced nearly every industry to adapt to a new reality, and the financial services industry was no exception. Consumer habits shifted drastically. Suddenly, many people started working from home. Employee and customer needs changed. Adaptability was a necessity.