How SAP customers can accelerate analytics in the cloud

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. 

German retailer Breuninger, which operates 11 department stores and an ecommerce site serving customers in three countries, realized its data was the key to keep evolving and innovating alongside the ever-changing needs and behaviors of its customers. As a result, it turned to Google Cloud to bring together its dispersed IT landscape, which included multiple SAP systems, and use BigQuery to analyze diverse datasets from across the business. Now that Breuninger runs reports in BigQuery instead of pulling custom SAP reports, it’s getting insights more cost-effectively and much faster—so quick, in fact, that customer data is in real time. This means more informed decision-making for Breuninger’s teams and more personalized, exciting experiences for its customers across every channel.

The BigQuery Data Transfer Service automates data movement from external data sources —like Google Marketing Platform, Google Ads, YouTube, and partner SaaS applications—to BigQuery on a scheduled and fully managed basis. Your analytics team can lay the foundation for a data warehouse without writing a single line of code.  In addition, Google Cloud Public Datasets offer a powerful data repository of more than 100 high-demand public datasets from different industries. Google Cloud provides storage at no charge for all public datasets, and customers can query up to 1 TB of data per month at no cost. Google Cloud has partnered with leading data management and integration solution providers, such as Informatica, Qlik, Datavard, SAP and Software AG, for a robust set of tools and solutions to extract data from SAP systems including ECC, S/4, and BW into BigQuery as the target data warehouse. Additionally, Atos has developed Rapid Deployment Accelerators for SAP Analytics with BigQuery.  Using pre-defined data models  and combining master with transactional data to enable self service reporting while providing business logic that can achieve 50-70% faster development cycles at 60-75% lower cost, according to Atos. SAP data in BigQuery creates the opportunity to add external source data such as Search trends, Ads, Maps and more to drive deep business insights leveraging the built-in machine learning capabilities of BigQuery.

Google Cloud’s AI and ML tools for analytics

In addition to BigQuery, Google Cloud has a number of tools that let you quickly and easily integrate AI and ML into your applications for advanced analytics. Google Cloud AI Building Blocks make it easy to add sight, language, conversation, and structured data into your applications. You can use proven, pre-trained APIs, or you can use Cloud AutoML to create high-quality custom models with minimal effort and machine learning expertise.

Organizations with data residing in visual sources can use Google Cloud AutoML Vision, an intelligent, AI-powered product that allows customers to derive learnings from images in the cloud or at the edge. Power company AES relies on AutoML Vision to assess damage to its hundreds of wind turbines. AES uses drones to inspect and photograph its turbines. These drones typically take 30,000 images, and each one must be examined. With Google Cloud's AutoML Vision, AES can use machine learning to auto-detect damage so that engineers can spend less time identifying damage and more time repairing it.

Build a data-driven business

To learn more about how SAP customers can benefit from Google Cloud analytics and machine learning solutions, visit cloud.google.com/solutions/sap.