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Analytics in a Multi-Cloud World with BigQuery Omni
Enterprises have more data at hand than they have ever had in the past. But are unable to leverage it fully.
“We have all of this data at our fingertips. But we just can’t quite get to it because we’re living in this world of data silos,” says Emily Rapp, Product Manager, Google Cloud.
Part of the problem is that over 80% of enterprises use more than one cloud provider. For many companies, leveraging a multi-cloud strategy is a competitive advantage, and sometimes a necessity. Multi-cloud allows organizations to meet users where they are and expedite their transformation journey to deliver a compelling customer experience.
So how can data teams manage? That’s why Google Cloud has introduced BigQuery Omni, a flexible, multi-cloud solution that lets you analyze data across clouds.
Watch the video to learn how to break down data silos in your environment and see how you can run analytics in a multi-cloud world.
How TeamSnap Improved Return on Ad Spend Significantly
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Anyone who has ever coached or played on a sports team, or had a child involved in sports, knows how difficult scheduling and logistics can be. From game and practice schedules, to uniforms and who’s bringing the snacks, it can be a lot for coaches, administrators, parents, and players to manage.
It’s no wonder that TeamSnap, a sports team, club, and tournament management app, has exploded in popularity worldwide. By syncing events to everyone’s personal calendars and providing messaging and payment tracking, TeamSnap makes communication and organization easy.
TeamSnap markets its app to coaches, players, and clubs via targeted YouTube ads. It also uses Google AdWords and DoubleClick to advertise on search results and run programmatic campaigns. These methods have been highly effective, helping TeamSnap grow to millions of users worldwide and become one of the most popular apps in the iOS app store.
As its business and data grew, TeamSnap was challenged to track ROI and measure the customer journey across channels and devices over time. The company’s marketing budget grew quickly, making it even more important to spend wisely. With data in Google Analytics 360, DoubleClick Campaign Manager, Google AdWords, and Salesforce, TeamSnap needed a way to link and correlate those data sources in a scalable, timely, and cost-effective way to understand the true impact of its digital marketing across websites and mobile apps.
To avoid the painstaking manual process of pulling data from multiple sources, TeamSnap began using Google Analytics 360, which integrates with Google BigQuery, to provide a fully managed big data analysis service. TeamSnap analyzes the data using Tableau, which connects directly to Google BigQuery for fast analytics and helps the company share and collaborate on that information with self-service ease.
The combination allows TeamSnap to easily track the activity of millions of users with self-service ease, without worrying about the scalability or availability of the big data platform.
“Before Google Analytics 360, Google BigQuery, and Tableau, tracking our return on ad spend was difficult because we had so much data,” says Ken McDonald, Chief Growth Officer at TeamSnap. “We didn’t always have insights to make the best choices. We don’t have that problem anymore because we’ve moved to real-time reporting. We find additional revenue growth opportunities almost daily.”
Making Ad Dollars Work Harder
TeamSnap now automatically imports unsampled Google Analytics 360 logs into the Google BigQuery data warehouse. To import data from other sources such as Google AdWords, DoubleClick, and YouTube, TeamSnap uses Google BigQuery Data Transfer Service. With all relevant data consolidated in Google BigQuery, TeamSnap can use Tableau to perform advanced analytics on its digital marketing, executing ad-hoc analyses in seconds, while eliminating data sampling issues, to improve accuracy. These analyses can also be reused and shared with internal and external stakeholders via Tableau Online, promoting governed reuse and consistency.
“Using Google Analytics 360 and Google BigQuery with Tableau to track our return on ad spend is ideal,” says Ken. “It’s easy to use SQL to query the data or explore it with drag-and-drop ease.”
With Google BigQuery, Ken and his team can bring all the data from the TeamSnap billing systems, internal CRM, and other Google services into one straightforward dataset that everyone uses. With Tableau, users are able to perform self-service analytics on this data and provision analyses via shared dashboards that communicate the same consistent truth across the company. These dashboards provide a single view of the business to discover new patterns and questions worth analyzing.
All of this results in enormous time savings because no one is re-inventing the wheel. “Using these tools, we immediately reallocated $300,000 of ad spend that was performing poorly, generating 200% ROI in the first two days,” says Ken.
Ken now spends his time analyzing data instead of trying to pull it all together, identifying pockets of inefficient spend in real time and reallocating those marketing dollars toward better performing campaigns.
“Before, we could only focus on the largest campaign-level datasets because it was so time consuming to pull the data,” he says. “With Google BigQuery and Tableau, we can examine our advertising ROI much more granularly and reallocate more than $10 million in ad spend annually to grow the company faster and more efficiently.”
More Effective A/B Testing
To make sure it is delivering the best customer experiences, TeamSnap uses Google Optimize to run A/B tests on its website. It uses Google BigQuery and Tableau to verify and supplement these findings by measuring longer-term customer behavior across devices, spanning both web and mobile apps.
By pulling in data from Google Optimize, Google Analytics 360, Salesforce, and in-house billing and CRM systems, and understanding it with Tableau, TeamSnap has increased the accuracy and effectiveness of its A/B testing, gaining a more complete picture of customer onboarding and activity. In some cases, it found that short-term indicators it previously trusted were actually poor predictors of long-term behavior.
“Integration between Google Analytics 360 and Google BigQuery is seamless, giving us much more confidence in our A/B testing,” says Ken. “We’re constantly finding new and interesting ways to use our digital marketing data. Often, making a simple change can increase revenue by hundreds of thousands of dollars a year.”
Improving Product Quality
TeamSnap also uses Google BigQuery and Tableau to improve its own product, tracking customer activity at such a granular level that usability and functionality issues can be exposed and addressed faster. It’s also increasing customer engagement by verifying that potential customers are coming in through the right onboarding path—for example, a coach versus a player, or a consumer versus a club or other sports business. Using A/B testing to make sure customers are routed to the appropriate flow, TeamSnap drove $4 million in additional customer value each year.
“We initially chose Google BigQuery and Tableau to help with marketing, but we realized quickly that they could help us on the product side as well,” says Ken. “Most of the testing we do is about making things better and easier for our customers, and we’re accelerating that process with Google BigQuery and Tableau.”
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To make great products: do machine learning like the great engineer you are, not like the great machine learning expert you aren’t.
Most of the problems you will face are, in fact, engineering problems. Even with all the resources of a great machine learning expert, most of the gains come from great features, not great machine learning algorithms. So, the basic approach is:
- Make sure your pipeline is solid end to end.
- Start with a reasonable objective.
- Add common-sense features in a simple way.
- Make sure that your pipeline stays solid.
This approach will work well for a long period of time. Diverge from this approach only when there are no more simple tricks to get you any farther. Adding complexity slows future releases.
Once you’ve exhausted the simple tricks, cutting-edge machine learning might indeed be in your future. See the section on Phase III machine learning projects.
This document is arranged as follows:
- The first part should help you understand whether the time is right for building a machine learning system.
- The second part is about deploying your first pipeline.
- The third part is about launching and iterating while adding new features to your pipeline, how to evaluate models and training-serving skew.
- The final part is about what to do when you reach a plateau.
- Afterwards, there is a list of related work and an appendix with some background on the systems commonly used as examples in this document.
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As a customer experience platform, Genesys helps clients nurture great relationships with customers, and creates seamless user journeys across all channels and devices. Its technologies are used by more than 10,000 companies in over 100 countries, making Genesys the top provider of its kind for 25 years in a row.
To improve data efficiency and communication across the company, Genesys turned to Analytics Pros, a digital analytics agency. Together, they used the power of Google Data Studio to transform the way teams across Genesys accessed and used data.
Communicating With Data
The marketing and product teams at Genesys had always been passionate about using data to optimize their user experience and marketing channels. But the problem was showing the data. The different offices and executives around the world had trouble logging in to view the data—and once they did, the reports were intimidating and confusing.
After learning more about the capabilities of Data Studio, Genesys decided to run a pilot project. Analytics Pros helped the company combine multiple data sets into self-service, fully-customizable dashboards on Data Studio. And it was a huge success. Regional teams were thrilled to have meaningful dashboards, and even executives started using and sharing reports.
Mid-Sized B2B Firm Achieves the Business Trifecta with a Single Strategy
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Thirteen years’ experience in e-commerce has given Teddy Chan, Chief Executive Officer and Chief Technology Officer, AfterShip, a deep understanding of the challenges of shipping and tracking packages to customers worldwide.
“The key problem many merchants face is customers asking ‘where is my order?’ and ‘when I will get the package?’” Chan says. “When I considered this issue, I came up with the idea of AfterShip.” Chan helped found AfterShip in 2011 to enable merchants to keep track of packages sent to customers via a web portal or an API. AfterShip also allows merchants to notify customers of anticipated delivery times.
“Using AfterShip, merchants can provide the same experience to buyers regardless of which couriers they use,” Chan says. “Merchants can also improve their customer engagement by including up-selling or marketing content with their delivery notifications.”
Hong Kong-headquartered AfterShip continues to grow quickly and now has a 40-person team. Thirty members of this team are based on the China special administrative region and 10 are based in India.
“Google Cloud Platform has a network of global datacentres with deep connectivity that enables us to put our infrastructure close to our customers. In addition, the ability to horizontally scale our global database using tools such as Google Cloud Spanner eliminates any limits on our geographic expansion.”
—Teddy Chan, Chief Executive Officer and Chief Technology Officer, AfterShip
By October 2017, the business was tracking about 30 million packages per month and had expanded its services to include label and rate calculation and self-service return. Revenue, package transaction numbers and team size have doubled every year for the past three years, while more than 300,000 merchants and 426 couriers are signed up to the service. Key customers include Wish, Etsy and Groupon.
Close to half AfterShip’s customers are based in the United States, about one third in Europe and the remainder are located in Asia. AfterShip had initially delivered its applications and services from an incumbent public cloud service. However, the company wanted to continue its growth trajectory while automating key infrastructure processes, implementing a continuous deployment model and controlling costs.
The business needed to achieve these objectives while maintaining a global presence and high-quality service. AfterShip started reviewing its options and decided to migrate to Google Cloud Platform (GCP). “Google Cloud Platform has a network of global datacentres with deep connectivity that enables us to place our infrastructure close to our customers,” Chan says.
“In addition, the ability to horizontally scale our global database using tools such as Google Cloud Spanner eliminates any limits to our geographic expansion. Furthermore, the managed services provided through GCP would allow us to focus on building better features for online merchants.” The reliability provided by GCP would also enable AfterShip to meet the stringent service level requirements of large digital marketplaces in the United States, Asia and elsewhere.
“Google Cloud Platform could manage the high volumes and enable us to deliver the service levels that would realise our ambition of becoming the number one tracking API platform in the world,” Chan says. “For example, with Google Cloud Platform, we can provide a 99.95% monthly uptime service level to our customers.” Finally, GCP provided managed solutions, including Google Kubernetes Engine powered by open source container orchestrator Kubernetes, that would enable AfterShip to automate processes such as scaling and enable its team to focus on developing applications.
AfterShip has moved its websites into GCP infrastructure in three datacentres around the world and anticipates completing the migration in Q4 2017. “Google provided a lot of assistance, particularly early in the project when we needed it,” Chan says. “They briefed us on several services we hadn’t known about that could replace the equivalents in the public cloud we were using previously.” The business then completed the migration using its own skilled team members. As well as Google Kubernetes Engine and Google Cloud Spanner, AfterShip is using Google BigQuery to store and analyse transaction information.
“Google Cloud Platform could manage the high volumes and enable us to deliver the service levels that would realise our ambition of becoming the number one tracking API platform in the world.”
—Teddy Chan, Chief Executive Officer and Chief Technology Officer, AfterShip
Deployment times down from one hour to two minutes
With deployment times falling from up to one hour in its previous cloud environment to about two minutes in GCP, AfterShip has been able to adopt a continuous deployment model. “This has improved our service levels,” Chan says. “If there are any issues we can fix them quickly, while we can iterate faster to create new features in response to customer requests or changes in the market. “This enables us to continue to lead our competitors.”
Targeting a 30 percent reduction in costs
AfterShip is now targeting a 30% reduction in costs by optimising its use of Docker containerisation technology on GCP.
“By using Docker with Kubernetes, we have been able to fine-tune our use of compute resources and better control our costs,” Chan says. “We’re extremely pleased with Google Cloud Platform as it really is built for engineers,” he adds. “In addition, its documentation is extremely clear, allowing us to troubleshoot or carry out activities on the platform ourselves. “We look forward to continuing to grow and extend our package tracking and associated services with Google Cloud Platform.”
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To design and implement a successful machine learning (ML) project, you often need to collaborate with multiple teams, including those in business, sales, research, and engineering. Understanding the essentials of gathering and preparing your data is crucial to align teams, and getting a project off the ground.
Building an ML model that achieves your business’ goal, requires the kind of data that a model can learn from. If the goal is to predict a target output based on some input, you’ll need some data consisting of past input-output examples. For example, to detect (that is, to predict) a fraudulent credit card transaction (the target of the prediction), each example must contain information on a past credit card transaction and whether that transaction was fraudulent or not.
So what are the best practices in preparing and curating data for a machine learning initiative? The answer to this question lies within the guide. You’ll learn:
- To evaluate whether your data is suitable for ML
- What types of data you need–and which ones to eliminate
- How to prevent data leakage
- Tricks to speed up an ML project such as Google’s human labeling service, and Cloud Dataprep
- The importance of the granularity of data
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