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Data Fabric Field Test: SAP vs. DIYv1.0

Hands-on Assessment and TCO Analysis of Deploying SAP Datasphere vs. Assembled Applications

Table of Contents

  1. Executive Summary
  2. Introduction
  3. Path to a Data Fabric
  4. Field Study and Results
  5. Conclusion
  6. About SAP
  7. About William McKnight
  8. About Jake Dolezal

1. Executive Summary

Interest in building a data fabric is very high, and for good reason. By enabling real-time, trustworthy data across multiple clouds, you can expedite the migration of analytics to the cloud, guarantee security and governance, and quickly generate business value. A data fabric provides a semantic layer that enables a map of your metadata, unifying data based on its meaning, rather than just its location, and preserving the business context and logic of that data across the fabric.

The method for building a data fabric has been assumed to be a messy do-it-yourself (DIY) blend of a data warehouse, a data lake, data integration tools, and data governance tools. Advancements like the semantic layer act as a unified translator, seamlessly connecting your data ecosystem for efficient exploration and analysis and establishing a holistic data environment that empowers users and streamlines decision-making. Acting as independent translators for each data source, the semantic layer topology eliminates messy integrations and provides unified understanding across the entire data ecosystem. Advancements like these yield more streamlined and integrated solutions that simplify the process.

In short, a business data fabric replaces siloed data sources with a unified ecosystem. It transforms data from a cost center to a strategic asset that fuels accessibility, efficiency, and innovation for informed decision-making and competitive advantage.

Key to this capability is the implementation of a metadata map, which captures the location and context of data across the data fabric, providing insights into the characteristics, relationships, and attributes of the data. A comprehensive metadata map empowers you to unlock the true potential of your data, enabling deep insight and strategic advantage.

In this GigaOm Benchmark report, we introduce SAP Datasphere. We compare the task of creating, integrating, distributing, and managing a data fabric with a common DIY set to SAP. Looking across all organization sizes, we found that a DIY data fabric deployment over three years cost 2.4x more than SAP Datasphere. These sharp cost advantages played out across all aspects of adoption—data fabric infrastructure, initial migration/build, CI/CD, and administration.

Figure 1 breaks out SAP Datasphere’s cost advantage across these aspects. It shows that organizations can slash three-year TCO spending with a business data fabric powered by SAP Datasphere, producing savings of up to 138%”.

Figure 1. Comparing Relative Cost of DIY over SAP Datasphere Across Operational Concerns