Predictive Model Built on Google Cloud Helps You Get a 7-day Mosquito Forecast Report!
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Mosquitoes aren’t just the peskiest creatures on Earth; they infect more than 700 million people a year with dangerous diseases like Zika, Malaria, Dengue Fever, and Yellow Fever. Prevention is the best protection, and stopping mosquito bites before they happen is a critical step.
SC Johnson—a leading developer and manufacturer of pest control products, consumer packaged goods, and other professional products—has an outsized impact in reducing the transmission of mosquito-borne diseases. That’s why Google Cloud was honored to team up with one of the company’s leading pest control brands, OFF!®, to develop a new publicly available, predictive model of when and where mosquito populations are emerging nationwide.
As the planet warms and weather changes, OFF! noticed month-to-month and year-to-year fluctuations in consumer habits at a regional level, due to changes in mosquito populations. Because of these rapid changes, it’s difficult for people to know when to protect themselves. The OFF!Cast Mosquito Forecast™, built on Google Cloud and available today, will predict mosquito outbreaks across the United States, helping communities protect themselves from both the nuisance of mosquitoes and the dangers of mosquito-borne diseases—with the goal of expanding to other markets, like Brazil and Mexico, in the near future.
With the OFF!Cast Mosquito Forecast™, anyone can get their local mosquito prediction as easily as a daily weather update. Powered by Google Cloud’s geospatial and data analytics technologies, OFF!Cast Mosquito Forecast is the world’s first public technology platform that predicts and shares mosquito abundance information. By applying data that is informed by the science of mosquito biology, OFF!Cast accurately predicts mosquito behavior and mosquito populations in specific geographical locations.
Starting today, anyone can easily explore OFF!Cast on a desktop or mobile device and get their local seven-day mosquito forecast for any zip code in the continental United States. People can also sign up to receive a weekly forecast. To make this forecasting tool as helpful as possible, OFF! modeled its user interface after popular weather apps, a familiar frame of reference for consumers.
The technology behind the OFF!Cast Mosquito Forecast
To create this first-of-its-kind forecast, OFF! stood up a secure and production-scale Google Cloud Platform environment and tapped into Google Earth Engine, our cloud-based geospatial analysis platform that combines satellite imagery and geospatial data with powerful computing to help people and organizations understand how the planet is changing.
The OFF!Cast Mosquito Forecast is the result of multiple data sources coming together to provide consumers with an accurate view of mosquito activity. First, Google Earth Engine extracts billions of individual weather data points. Then, a scientific algorithm co-developed by the SC Johnson Center for Insect Science and Family Health and Climate Engine experts translates that weather data into relevant mosquito information. Finally, the collected information is put into the model and distilled into a color-coded, seven-day forecast of mosquito populations. The model is applied to the lifecycle of a mosquito, starting from when it lays eggs to when it could bite a human.
The SC Johnson Center for Insect Science and Family Health is one the world’s leading entomology research centers, studying advanced science of insect biology, insect-borne disease prevention and effective product solutions for consumer use. The science behind brands like OFF! is grounded in knowledge from world-class entomologists who have devoted their careers to SC Johnson’s mission of eradicating diseases like Malaria and Zika.
“We are putting the power in consumers’ hands in providing them with a tool to help predict their exposure and prevent mosquito bites,” said Maude Meier, SC Johnson entomologist. “It’s an exciting time to be working in the field of insect science as we find new opportunities to combine science and technology, like Google Earth Engine, to be a force for good in our mission to prevent the spread of insect-borne diseases.”
It takes an ecosystem to battle mosquitos
Over the past decade, academics, scientists and NGOs have used Google Earth Engine and its earth observation data to make meaningful progress on climate research, natural resource protection, carbon emissions reduction and other sustainability goals. It has made it possible for organizations to monitor global forest loss in near real-time and has helped more than 160 countries map and protect freshwater ecosystems. Google Earth Engine is now available in preview with Google Cloud for commercial use.
Our partner, Climate Engine, was a key player in helping make the OFF!Cast Mosquito Forecast a reality. Climate Engine is a scientist-led company that works with Google Cloud and our customers to accelerate and scale the use of Google Earth Engine, in addition to those of Google Cloud Storage and BigQuery, among other tools. With Climate Engine, OFF! integrated insect data from VectorBase, an organization that collects and counts mosquitoes and is funded by the U.S. National Institute of Allergy and Infectious Diseases.
The model powering the OFF!Cast Mosquito Forecast combines three inputs—knowledge of a mosquito’s lifecycle, detailed climate data inputs, and mosquito population counts from more than 5,000 locations provided by VectorBase. The model’s accuracy was validated against precise mosquito population data collected over six years from more than 33 million mosquitoes across 141 different species at more than 5,000 unique trapping locations.
A better understanding of entomology, especially things like degree days and how they affect mosquito populations, and helping communities take action is critically important to improving public health. Learn more about OFF!Cast Mosquito Forecast and see here to learn more about Google Earth Engine on Google Cloud.
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Data Leaders in 2021 and Beyond: How to Prepare
As technologies and workplace environments are quickly evolving, how people and businesses leverage data in their day-to-day workflow is also changing.
Data leaders are an emerging force navigating and charting pathways forward towards new horizons for how people and organizations experience data.
In this presentation, Pedro Arellano, Product Marketing Director, Looker, will cover three areas:
- Underline that the value of data within an organization is no longer up for debate. Everybody needs data, and everybody’s job has the potential to be improved with data.
- Three trends that will help put into context how we’ve arrived at this particular moment of such great potential for data.
- Why data leaders, almost regardless of their title, are now in a position to really influence the direction of organization like never before.
Learn what’s guiding the thinking of data leaders in 2021 and beyond.
ESG Did the Math: It’s Cheaper and Smarter to Migrate Enterprise Data Warehouses to Google BigQuery. Way Smarter
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Enterprise data warehouses (EDWs) are often deemed the most valuable asset in the data center, serving as the backbone of the business. The ongoing insight gained from these solutions has justified the significant up-front capital investments and ongoing operational costs, but the rigidity of the traditional EDW is forcing organizations to reevaluate their approach to analytics and business intelligence.
While legacy EDW solutions were all about throwing as much computational power as possible at a relatively static data set, with the inflow of new and valuable sources of data and the emergence of all-encompassing analytics initiatives, the success of today’s EDW solutions depends more on operational and resource agility than raw horsepower.
Being able to dynamically adjust to the needs of the business, integrate into operational processes, and quickly react to emerging opportunities can place an organization at a distinct competitive advantage.
Today’s EDW solutions must act as a global repository of information, provide the agility to scale up or down on demand, and seamlessly integrate with other analytics tools and services used throughout a data-driven organization.
Over the last two years, ESG has conducted detailed studies quantifying the economic value of Google data analytics services. The first evaluated Google BigQuery compared to on-premises Hadoop and AWS redshift. The second focused on Google DataProc compared to DIY Spark and Hadoop approaches. Here’s the next iteration of our economic analysis, extending the BigQuery study to incorporate a comparison to legacy enterprise data warehouses, both on-premises and in the cloud.
Through publicly available pricing and in-depth qualitative customer interviews, ESG was able to assert a base set of assumptions that power a dynamic model, incorporating up-front capital investments, deployment and migration costs, expected monthly cloud costs, administrative costs, and operational costs associated with legacy on-premises EDWs, cloud-based EDWs, and Google BigQuery.
The crux of the results show organizations can save up to 52% by using BigQuery over on-premises EDWs and up to 41% over cloud-based EDWs. Unlike legacy on-premises EDWs, BigQuery provides organizations with the key abilities that are essential to delivering a modern EDW solution, most notably the ability to integrate across other Google Cloud Platform services, including its market leading AI-based solutions and services.
Although not called t out directly in the published report, ESG’s models indicate that the savings achieved by migrating an on-premises EDW solution to Google BigQuery may actually be more cost effective than simply continuing to operate an existing on-premises EDW solution.
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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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.
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An Indian Example of How to Really Up Your Customer Experience Game and Increase Conversion Rates With AI
How about selfie analysis of users to recommend them the right lipstick color?
That’s just one of the many ideas folks at Purplle.com came up with to improve the buying experience of Indian consumers.
And without the power of Google Cloud, it would probably have remained just that…an idea.
But today, thanks to Google Cloud, “Nothing seems impossible,” says Suyash Katyayani, CTO, Purplle.
Purplle.com is an online e-commerce company in India and one of the pioneers in creating a digitally-native beauty brands in India.
“The beauty industry is so data intensive that we needed to have a strong data strategy and we were looking out for solutions which would enable us to have a strong data pipeline and a strong data warehousing solution,” says Katyayani.
That’s when it turned to Google Cloud.
Additionally, Purplle.com, says Katyayani, does not have to worry about at what scale the company operates at because they have access to state-of-the-art infrastructure from Google Cloud available to them so that their developers can run experiments.
“The biggest plus point for us has been the agility that Google Cloud has added,” says Katyayani.
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