Customers who make data sing and analytics product news to cure your data FOMO

In December, we predicted that a “revolution was coming for data and the cloud in 2021.” Well, January came and gone: our team has been busy delivering new capabilities, content and best practices to help kick your year into high gear. Our work is guided by our customers; we’re always listening to your needs and working to build innovative solutions that will help you succeed.

Here is a quick digest of what’s happening in data analytics at Google this month.

The Data Democracy Trilogy 

This past week we released the third and final installment of our “data democratization trilogy,” a series of blogs aimed at helping our community deliver on their mission to become more data-driven.  

Our blogs include best practices from incredible organizations like AB Tasty, Sunrun, Veolia, Geotab and AES Digital Hub who have empowered business users, expanded the use of machine learning and made real-time analytics ubiquitous. The democratization of insights has been a key theme for our customers and a personal passion of mine, and it will be front and center of our plans for 2021. 

If you want to find out how Dataflow, together with Pub/Sub, can help the challenges posed by traditional streaming systems or how the combination of BigQuery, Connected Sheets, Looker and Data QnA can provide faster answers to your employees, be sure to bookmark these blogs and share them with your teams and colleagues.

And, if you’re ready for more, check out our design pattern catalog. This past week, we released a set of resources to help you perform demand forecasting at scale using BQML and Data Studio. The best way to understand this pattern is to watch the video below and to register for our webinar next week: How to do demand forecasting with BigQuery ML.

As you navigate through the catalog, you’ll find everything you need from predicting customer lifetime value, building propensity to purchase models, or architecting product recommendation and anomaly detection systems. You’ll probably wonder how we came up with such an impactful list of best practices. The answer is simple: our customers!  

Our customers guide everything we do and we pride ourselves in building the solutions you need across any and all industries. That’s why, when you navigate through our catalog, you’ll find that these resources are applicable across many industries, from retail and manufacturing to financial services, telecommunications and many more.

From staying up until 3am to relaxing and eating ice cream 

To give you an example of the commitment we make to our customers, I want to point you to an outstanding conversation we posted last week between Chad Jennings, Data Analytics product manager and two of our greatest customers: the New York Times & The Major League Baseball.

The video is accompanied by a great blog, authored by The New York Times’ Executive Director for Data Products, Edward Podojil. In the piece, Ed talks about his company’s data architecture evolution and how he went from staying “up until three in the morning one night trying to keep data running for their needs” to “relaxing and eating ice cream” because he could now “more easily manage his data environment, set and meet higher expectations for data ingestion, analysis and insight.” This is the kind of story that truly warms my heart; I hope you’ll enjoy it too!

Innovators in all industries

Our customers work on some of the most meaningful and interesting issues. We pride ourselves in serving them and paying attention to their progress. Great publications like Diginomica and Healthcare business and policy site FierceHealthcare documented the journeys of some of them this month:

• We hope you’ll find value in how The Home Depot describes their journey and documented how BigQuery allowed them to achieve their “one version of the truth”.  

• You might have been inspired by Highmark Health’s decision to tackle the data fragmentation experienced in the healthcare industry by partnering with Google Cloud to tap into our AI and Analytics technology.

• Our goal is to enable every industry to accelerate their ability to digitally transform and reimagine their business through data-powered innovation. And we mean every industry.  If you’re in the entertainment industry for instance, you’ll want to read about why BMG selected Google Cloud, BigQuery and Dataproc to tap into relevant data across the music lifecycle with smarter analytics tools.

“We actually migrated all of our data warehouse to BigQuery over the last three years. The upside of that is now we have a lot more of this data together. There's only one place of truth, so there's never an argument in our organization about whether your copy of the data is the real truth or my copy of the data is the real truth.” - The Home Depot

"The Living Health model takes the information and preferences that a person provides us, applies the analytics developed with Google Cloud, and creates a proactive, dynamic, and readily accessible health plan and support team that fits an individual’s unique needs." - Highmark Health

Product capabilities you’re not going to want to miss

Our customers inspire us to do more every day and we aim to continuously introduce new functionality that makes your work easier, more robust, and better integrated.  

In January, we introduced radical usability improvements with our new BigQuery Cloud Console UI: you can now experience new multi-tab navigation, a new resource panel & new SQL editor. Find out more. 

Beyond usability, customers value scale and we hear that you want our help in making queries and use cases virtually limitless. This is why, this month, we introduced support for the BigNUMERIC datatype. BigQuery already supports a wide range of data types for storing numeric data. Of these data types, NUMERIC supports the highest degree of precision with 38 digits of precision and 9 digits of scale. But, as large web-scale datasets expand to support time, location or finance-based information with an expanded degree of precision, the current precision and scale in NUMERIC was not sufficient to support the data. 

We introduced BIGNUMERIC, which supports 76 digits of precision and 38 of scale, in public preview in all regions. Read more here. 

Finally, many of you have reached out to us to ask how you can use BigQuery with Open Source engines like Apache Spark. Chris Crosbie, product manager on Dataproc, produced an outstanding tutorial video introducing our Spark-BigQuery-connector through the use of three common use cases for data engineers and data scientists.

Want to take BigQuery for spin? Get started with the BigQuery sandbox here. While you’re at it, you might want to refer to this January blog on how to let users upload their complex CSV file into BigQuery using Google Sheets

More community news!

If you’re subscribing to this blog, you know that our teams are focused on enabling the community and partnering with you to advance the field of data analytics, machine learning and data science. Let us know how we can participate in your success!  

This past month, I had the opportunity to speak about X-Analytics with Justin Borgman, the CEO of Starburst Data, in preparation for his company’s upcoming event: Datanova. I hope you can make time for it: the two-day virtual conference kicks off on February 9th and Bill Nye, the “science guy” is the keynote! Find out more about it here.