These days, real-time, real-world data usually comes from multiple, disparate sources—for instance, IoT devices, messaging applications, social media, and clickstreams from web and ecommerce activity. This data is rapidly growing in variety, volume, and velocity. In a recent ESG survey, 66% of organizations report that they are managing a petabyte of data or more, with nearly one-third (31%) managing at least 5 petabytes. Taken together, these data sources offer a tremendous opportunity to add significant business value. This is certainly true for SAP customers where the combined power of operational and other data sources has the ability to transform decision making.

And therein lies the challenge: This firehose of data makes it difficult to efficiently and securely manage, store, analyze, and generate robust insights. In fact, most organizations surveyed by ESG reported that they use no more than 30% of their total data for analytics purposes. So it’s no surprise that, according to SAPinsider research from May 2020, 52% of SAP customers surveyed say that their top analytics pain point is data integration.

In the past few years, many organizations have seen the benefits of migrating their SAP and other enterprise solutions to the public cloud—from reduced IT maintenance spend, to increased data security, to a more flexible, scalable cost structure. But the choice of public cloud provider can offer much more in the way of data integration and analytics—far beyond the capabilities of on-premises solutions. Google Cloud offers two powerful analytics solutions for SAP cloud and on-premises deployments alike: BigQuery, our cloud data warehouse, and a suite of AI and machine learning tools.

BigQuery: Data warehousing with the power of Google Cloud

BigQuery is a fully managed, and serverless cloud data warehouse that supports petabyte-scale projects at blazing-fast speeds, with zero operational overhead. It offers built-in machine learning with BigQuery ML allowing users to operationalize ML models using standard SQL and supports geospatial analysis with BigQuery GIS. BigQuery automatically scales its infrastructure up or down for the best performance and separates storage from compute allowing you to run analytics at scale with a 26% to 34% lower three-year total cost of ownership (TCO) than cloud data warehouse alternatives1.

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