As the Olympics kicked off in Tokyo at the end of July, we found ourselves reflecting on the beauty of diverse countries and cultures coming together to celebrate greatness and sportsmanship. For this month’s blog, we’d like to highlight some key data and analytics performances that should help inspire you to reach new heights in your data journey.

Let’s review the highlights!

BigQuery ML Anomaly Detection: A perfect 10 for augmented analytics

Identifying anomalous behavior at scale is a critical component of any analytics strategy. Whether you want to work with a single frame of data or a time series progression, BigQuery ML allows you to bring the power of machine learning to your data warehouse. 

In this blog released at the beginning of last month, our team walked through both non-time series and time-series approaches to anomaly detection in BigQuery ML:

• Non-time series anomaly detection
  • Autoencoder model (now in Public Preview)
  • K-means model (already in GA)
• Time-series anomaly detection
  • ARIMA_PLUS time series model (already in GA)

These approaches make it easy for your team to quickly experiment with data stored in BigQuery to identify what works best for your particular anomaly detection needs. Once a model has been identified as the right fit, you can easily port that model into the Vertex AI platform for real-time analysis or schedule it in BigQuery for continued batch processing.

App Analytics: Winning the team event

Google provides a broad ecosystem of technologies and services aimed at solving modern day challenges. Some of the best solutions come when those technologies are combined with our data analytics offerings to surface additional insights and provide new opportunities. 

Firebase has deep adoption in the app development community and provides the technology backbone for many organization’s app strategy. This month we launched a design pattern that shows Firebase customers how to use Crashlytics data, CRM, issue tracking, and support data in BigQuery and Looker to identify opportunities to improve app quality and enhance customer experiences.

Crux on BigQuery: Taking gold in the all-around data competition

Crux Informatics provides data services to many large companies to help their customers make smarter business decisions. While they were already operating on a modern stack and not on the hunt for a modern data warehouse, BigQuery became an enticing option due to performance and a more optimal pricing model. Crux also found advantages with lower-cost ingestion and processing engines like Dataflow that allow for streaming analytics.… when it came to building a centralized large-scale data cloud, we needed to invest in a solution that would not only suit our current data storage needs but also enable us to tackle what’s coming, supporting a massive ecosystem of data delivery and operations for thousands of companies.Mark Etherington
Chief Technology Office, Crux Informatics

Technology is a team sport, and Crux found our support team responsive and ready to help. This decision to more deeply adopt Google Cloud’s data analytics offerings provides Crux with the flexibility to manage a constantly evolving data ecosystem and stay competitive.

You can read more about Crux’s decision to adopt BigQuery in this blog.

Google Trends: A classic emerges a champion

Following up on the launch of our Google Trends dataset in June, we delivered some examples of how to use that data to augment your decision making. 

As a quick recap of that dataset, Google Cloud, and in particular BigQuery, provide access to the top 25 trending terms by Nielsen’s Designated Market Area® (DMA) with a weekly granularity. These trending terms are based on search patterns and have historically only been available on the Google Trends website.https://www.youtube.com/embed/9FJAXMF0ASc?enablejsapi=1&

The Google Trends design pattern addresses some common business needs, such as identifying what’s trending geographically near your stores and how to match trending terms to products to identify potential campaigns. 

Dataflow GPU: More power than ever for those streaming sprints

Dataflow is our fully-managed data processing platform that supports both batch and streaming workloads. The ability of Dataflow to scale and easily manage unbounded data has made it the streaming solution of choice for large workloads with high-speed needs in Google Cloud. 

But what if we could take that speed and provide even more processing power for advanced use cases? Our team, in partnership with NVIDIA, did just that by adding GPU support to Dataflow. This allows our customers to easily accelerate compute-intensive processing like image analysis and predictive forecasting with amazing increases in efficiency and speed. 

Take a look at the times below:

Data Fusion: A play-by-play for data integration’s winning performance

Data Fusion provides Google Cloud customers with a single place to perform all kinds of data integration activities. Whether it’s ETL, ELT, or simply integrating with a cloud application, Data Fusion provides a clean UI and streamlined experience with deep integrations to other Google Cloud data systems. Check out our team’s review of this tool and the capabilities it can bring to your organization.

The app economy has enabled a huge range of unique business models to flourish. One such model is online food ordering and delivery services, in which apps leverage geo-location data to aggregate local food choices and offer personalized options to consumers.

A leading company in this space is Just Eat. Launched in the UK in 2001 with a vision of ‘serving the world’s greatest menu. Brilliantly.’ The company has capitalized on the popularity of online food delivery and grown its presence across 12 markets. 

Just Eat acts as an intermediary between take-out food outlets and hungry customers, giving local restaurants access to a broader base of potential diners, while providing consumers with an easy and secure way to order and pay for food from their favourite restaurants.

Today the company helps 27 million customers find food from more than 112,000 restaurants—everything from homemade Italian pasta, to Chinese noodle bowls, to fish-and-chips. 

Data is the fuel of Just Eat’s rapid growth, but it wasn’t always looked at that way. In its early days, Just Eat struggled with the deluge of information and faced fragmentation across its systems. In fact, the company realized its legacy data vendor wasn’t capable of ingesting 90 percent of the data produced by its food platform. This was incredibly frustrating for Just Eat’s analysts and data scientists, who had to waste time cleaning up sources instead of leveraging the data to create a better user experience. 

Just Eat turned to Google Cloud, and now uses machine learning (ML) to power sophisticated consumer recommendations on both its app and website. It also makes heavy use of features offered by Google Cloud Platform, including BigQuery for running analytics on its customer data set and Cloud Pub/Sub for messaging app users with relevant offers in real-time. 

Having all of Just Eat’s data in one platform has translated into real value for its customers. With Google Cloud tools, Just Eat has created its own proprietary Customer Ontology framework, which today contains 5.5 billion features that better understand consumers’ behavior and food habits, and provides insights into previous visits.

Just Eat recently created an “Adventurous Index” to map its customers according to their ordering habits, enabling them to tailor their marketing and user experiences. For example, mid-adventurous customers are shown a choice of restaurants that serve their most ordered cuisine, while adventurous customers can choose from restaurants that serve a wider variety. This not only has prompted consumers to be more adventurous with their choices, but also has led to more business at a more diverse set of restaurants.

Matt Cresswell, Director of Customer Platforms at Just Eat said that Google Cloud has become integral to its product delivery: “Consumer food choice is a hugely nuanced topic. We know that individuals have their own unique journeys when they use Just Eat. We’ve sought to create a truly one-to-one relationship with every customer. The changes we’ve made to the platform mean they can access the dishes they enjoy at the touch of a fingertip, and find inspiration to discover new dishes they’ll love. We’re grateful to Google Cloud for helping us support our customers on their culinary explorations.”

Today, a typical enterprise uses over 100 SaaS applications while large organizations commonly use over 400 apps. These applications contain valuable data on customers, suppliers, employees, products and more, offering the potential for valuable insights and powerful workflows. However, in the past, this data has often remained siloed and underutilized, even within an application.

Customer demand is driving an unprecedented wave of innovation across the SaaS industry, with providers building intelligence into their applications through rich analytical and AI/ML capabilities, and enabling their customers’ data ecosystems through real time data sharing. The data cloud platform powering these applications is a critical factor in companies’ ability to innovate and optimize efficiency, while keeping its customer’s data secure.

Today, over 700 tech companies including Zoominfo, Equifax, Exabeam, Bloomreach, and Quantum Metric power their products and businesses using Google’s data cloud. This week at the Data Cloud Summit, we announced the Built with BigQuery initiative which helps ISVs get started building applications using data and machine learning products. By providing dedicated access to technology, expertise and go to market programs, this initiative allows tech companies to accelerate, optimize and amplify their success.

“Enabling customers to gain superior insights and intelligence from data is core to the ZoomInfo strategy,” ZoomInfo CEO Henry Schuck says. “We are excited about the innovation Google Cloud is bringing to market and how it is creating a differentiated ecosystem that allows customers to gain insights from their data securely, at scale, and without having to move data around. Working with the Built with BigQuery initiative team enables us to rapidly gain deep insight into the opportunities available and accelerate our speed to market.”

Building intelligent SaaS applications with a unified data platform

Google’s data cloud provides a complete platform for building data-driven applications, from simplified data ingestion, processing and storage to powerful analytics, AI/ML and data sharing capabilities, all seamlessly integrated with Google Cloud’s open, secure, sustainable platform. With a huge partner ecosystem and support for multicloud, open source tools and APIs, we provide technology companies the portability and extensibility they need to avoid data lock-in.

Through the Built with BigQuery initiative, we are helping tech companies to build the next-gen SaaS applications on Google’s data cloud with simplified access to technology, dedicated engineering support and joint go to market programs.

Exabeam, a next generation cybersecurity solution, is a great example. The company leverages Google’s data cloud to provide their customers with a “limitless-scale” cybersecurity solution.

“Built with Google’s data cloud, Exabeam’s limitless-scale cybersecurity platform helps enterprises respond to security threats faster and more accurately” said Sanjay Chaudhary, VP of Products at Exabeam. “We are able to ingest data from over 500 security vendors, convert unstructured data into security events, and create a common platform to store them in a cost effective way. The scale and power of Google’s data cloud enables our customers to search multi-year data and detect threats in seconds.”

Foster innovation and unlock new business models through data sharing

Data becomes even more valuable when shared. According to research, the global data monetization market size is growing at a CAGR of 47.9% and is projected to reach $11.7 Billion by 2026. It’s a compelling proposition for SaaS companies as it enables them to deliver increased value for their customers while expanding their own partner ecosystem, increasing stickiness and unlocking new revenue streams.

Google Cloud’s strategy for data sharing encompasses three key areas: secure data sharing in BigQuery, the ability to create private and public exchanges alongside commercial and public datasets in Analytics Hub, and a robust ecosystem of premier partner and Google data.

Through the real-time data sharing capabilities of BigQuery, SaaS companies are enabling their customers to combine their data with the customer’s own proprietary data, and other third-party data sources, to derive 360-degree insights. Over 4,500 organizations share more than 250 Petabytes of data weekly in BigQuery, not accounting for intra-organizational data sharing*.

For example, manufacturers can get real-time visibility into their entire supply chain by combining datasets from ISVs, such as supply chain innovator, Blume Global, and data publishers.

“Our partnership with Google Cloud is helping us achieve our mission to build the next-generation supply chain operating system. Blume Maps, our digital twin of the supply chain world built with Google’s data cloud, allows our customers to generate accurate lead times, real-time shipment location and ETAs” said Blume Global CEO Pervinder Johar. “We apply the power of Google Cloud’s data and analytics capabilities to our growing database of over 1.5 million global data points to feed our lead time and dynamic ETA engine. We are also able to create data twins of unique logistics data and share with users around the globe.”

Google Cloud’s Analytics Hub is a fully-managed service built on BigQuery that allows organizations to efficiently and securely exchange valuable data and analytics assets across any organizational boundary. With unique datasets that are always-synchronized and bi-directional sharing, you can create a rich and trusted data ecosystem between business units or partnerships–one in which everyone gains value immediately.

“As external data becomes more critical to organizations across industries, the need for a unified experience between data integration and analytics has never been more important. We are proud to be working with Google Cloud to power the launch of Analytics Hub, feeding hundreds of pre-engineered data pipelines from hundreds of external datasets,” said Dan Lynn, SVP Product at Crux. “The sharing capabilities that Analytics Hub delivers will significantly enhance the data mobility requirements of practitioners, and the Crux data integration platform stands ready to quickly integrate any external data source and deliver on behalf of Google Cloud and its clients.”

Innovate, optimize and amplify with Google Cloud

The Built with BigQuery initiative provides access to technology, expertise and go-to-market:.

• Access to Technology: Get started fast with a Google-funded, pre-configured sandbox. Access Cloud Credits to fund your, and your customer’s, POCs and apply for innovative pricing models designed for ISVs.
• Access to Expertise: Accelerate and optimize product design and architecture with access to designated experts in building data-driven SaaS applications from the ISV Center of Excellence, providing insight into key use cases, architectural patterns and best practices. Access experts from across Google to enable co-innovation with products such as Earth Engine and Google Marketing Platform.
• Access to Go-to-market: Amplify your success with joint marketing programs to drive awareness, generate demand and increase adoption.

Get Started

Start building your applications on Google Cloud with $300 in free credits or apply for the Built with BigQuery initiative.

*As of November, 2021, within a typical 7 day period in BigQuery, and not accounting for intra-organizational data sharing.