Systems | Development | Analytics | API | Testing

Today, we’re hearing from telematics solutions company Geotab about how Google BigQuery enables them to democratize data across their entire organization and reduce the complexity of their data pipelines.

Analytics and data visualizations have the power to elevate a software product, making it a powerful tool that helps each user fulfill their mission more effectively. To stand apart from the competition, today’s software applications need to deliver a lot more than just transaction processing. They must also provide insights that help drive better decisions, alert users to matters that require their attention, and deliver up-to-the-minute information about the things that matter most.

In the past, most software applications were all about “data processing.” In the parlance of old-school management information systems, that meant an almost exclusive focus on keeping accurate transactional records alongside any master data necessary to complete that mission. Transaction processing is important, of course, but in today’s world, applications are expected to deliver a lot more than that.

Our mission at Google Cloud is to help our customers fuel data driven transformations. As a step towards this, BigQuery is removing its limit as a SQL-only interface and providing new developer extensions for workloads that require programming beyond SQL. These flexible programming extensions are all offered without the limitations of running virtual servers.

Most commonly, data teams have worked with structured data. Unstructured data, which includes images, documents, and videos, will account for up to 80 percent of data by 2025. However, organizations currently use only a small percentage of this data to derive useful insights. One of main ways to extract value from unstructured data is by applying ML to the data.