Editor’s note: Today, we’re hearing from TELUS Insights about how Google BigQuery has helped them deliver on-demand, real-world insights to customers.
Collecting reliable, de-identifiable data on population movement patterns and markets has never been easy, particularly for industries that operate in the physical world like transit and traffic management, finance , public health, and emergency response. Unlike online businesses, these metrics might be collected manually or limited by smaller sample sizes during a relatively short time.
But imagine the positive impact this data could have if organizations had access to mass movement patterns and trends to solve complicated problems and mitigate pressing challenges such as traffic accidents, economic leakage, and more.
As one of Canada’s leading telecommunications providers, TELUS is in a unique position to provide powerful data insights about mass movement patterns. At TELUS, we recognize that the potential created by big data comes with a huge responsibility to our customers. We have always been committed to respecting our customers’ privacy and safeguarding their personal information, which is why we have implemented industry-leading Privacy by Design standards to ensure that their privacy is protected every step of the way. All the data used by TELUS Insights is fully de-identified, meaning it cannot be traced back to an individual. It is also aggregated into large data pools, ensuring privacy is fully protected at all times.
BigQuery checked all our boxes for building TELUS Insights
TELUS Insights is the result of our vision to help businesses of all sizes and governments at all levels make smarter decisions based on real-world facts. Using industry-leading privacy standards, we can strongly de-identify our network mobility data and then aggregate it so no one can trace back data to any individual.
We needed to build an architecture that would provide the performance necessary to run very complex queries, many of which were location-based and benefited from dedicated geospatial querying. TELUS is recognized as the fastest mobile operator and ranked first for network quality performance in Canada, and we wanted to deliver the same level of performance for our new data insights business.
We tested out a number of products, from data appliances to an on-premise data lake, but it was BigQuery, Google Cloud’s serverless, highly scalable, and fully managed enterprise data warehouse, that eventually came out ahead of the pack. Not only did BigQuery deliver fast performance that enabled us to easily and quickly analyze large amounts of data at infinity scale, it also offered support for geospatial queries, a key requirement for the TELUS Insights business.
Originally, the model for TELUS Insights was consultative in nature: we would meet with customers to understand their requirements and our data science team would develop algorithms to provide the needed insights from the available data sets.
However, performance from our data warehouse proved challenging. It would take us six weeks of query runtime to extract insights from a month of data. To best serve our customers, we began investigating the development of an API that, with simple inputs, would provide a consistent output so that customers could start using the data in a self-serve and secure manner.
BigQuery proved itself able to meet our needs by combining high performance for complex queries, support for geospatial queries, and ease of implementing a customer-facing API.
