When the Utah Wildlife Resources Department was unable to process the growing amounts of data it was gathering about the state’s wildlife population, it turned to analytics tools on Google Cloud.
Data has long been used to monitor and conserve wildlife populations.
Wild animals are caught, marked with tracking devices, released and then observed. The geospatial data collected then enables biologists, conservationists, and others to study wildlife habits and keep an eye on wildlife populations.
Through May 2020, the Utah Wildlife Resource (DWR) division was using analytical applications developed and deployed on-site to monitor and maintain wildlife populations in the state’s vast open spaces, including national parks such as Zion, Bryce Canyon, Arches, and Canyonlands belong. The wildlife includes bison, puma, moose, elk, mountain lions, and wolves.
At first it worked fine. But as more animals were tagged and more data was collected, the system began to fail. By spring 2020, the previous wildlife tracking application of the DWR in Utah was overwhelmed.
According to Eric Clark, account director for Google Cloud Services at SpringML, a Utah consulting firm that turned to DWR for help redesigning its analytics, it simply couldn’t handle the volume of data it was consuming and query that data in a timely manner Stack.
Eventually, under SpringML’s guidance, the DWR in Utah decided to completely rebuild their analytics stack and do it using Google Cloud Platform.
Eric Clark, Account Director for Google Cloud Services at SpringML, explains how the Utah Division of Wildlife Resources uses Google Cloud tools to track and care for the state’s wildlife population.
Using Google’s analytics platform would allow the Utah DWR to manage the amount of data Clark is collecting.
Perhaps more importantly, given the processing power of BigQuery – a serverless cloud data warehouse from Google that enables petabytes of data to be analyzed – it is also capable of handling the inevitable surge in data that comes with advances in technology This will make it possible to collect even more data on wildlife.
“It’s about getting your data into a place where you can start using tools and functionality. That’s why we love using BigQuery as the centerpiece of many of the solutions we develop,” said Clark in a presentation at the Google Data Cloud Summit, a virtual user conference hosted by the tech giant.
Now we can concentrate on the data and the functionality of the application and no longer have to worry about the servers. As the data and user base grow, it will scale and meet the demands.
Eric ClarkAccount Director for Google Cloud Services, SpringML
Regarding the DWR in Utah, he added that growing amounts of geospatial data were key in the decision to use BigQuery.
“Given the expected growth and long-term needs of the DWR that really need to rely on this data, we felt that this was the best way to future proof this solution and really position it to scale,” said Clark.
By September 2020, Utah’s DWR was equipped with a redesigned wildlife tracker that runs on Google Cloud. At the front end there is an application that was created with the Google App Engine and is supported by BigQuery.
“Now we can focus on the data and functionality of the application instead of worrying about the servers. As the data and user base grow, it will scale and meet demand,” said Clark.
As an example of what Utah’s DWR can now do with data using Google analytics tools, Clark ran a query to determine where a single Puma tagged with a GPS tracking device had traveled over a year. During this time, 5,300 location points were collected.
The response to the query was immediate and resulted in a pattern of travel east to west through the mountains of Utah. A heat map showed that the puma stayed the longest in the northwest of its journey.
“With a query with 5,300 points, the previous system would have had problems,” said Clark.
A query below, showing the movement of all animals tracked by the Utah DWR over a month in the Book Cliffs Mountains of eastern Utah, found approximately 63,000 location points. This information was also returned in less than a second.
“That query would never have worked,” said Clark. “We have completely removed the performance restrictions.”
Utah’s DWR had a specific use case. However, this use case exemplifies what any company can do with data when properly managed and positioned for action, says Clark.
Citing statistics from IDC and Seagate Technology, Clark said companies worldwide are projected to generate 175 zettabytes of data by 2025. At the beginning of 2020, there were an estimated 44 zettabytes of data.
Every business has to deal with the same increase in data volume that the DWR in Utah is now managing, and every organization has to deploy an analytics stack capable of handling an exponential increase in data.
According to Clark, it starts with where the data is stored and managed.
“When we think about modern analytics and get the most out of the data you already have and will have in the future, it turns out that where your data is really important,” he said.
It will determine whether companies can perform advanced analytics including forecasting and forecasting, whether they can leverage machine learning and advanced intelligence, consolidate and connect disparate data sources, and future proof themselves, Clark continued.
“When we think about the amount of data growth that we know we will see in the years to come, we need to think about expanding our data management skills,” said Clark. “We have to think about how we can analyze it, not the infrastructure, so we can pay as much attention to the findings as possible.”