Redefining data quality in large-scale migrations

At SNP, we’re driven by a commitment to helping enterprises achieve seamless and predictable migrations to SAP S/4HANA and RISE with SAP. Recently, we had the opportunity to collaborate with Accenture on a transformative project at Siemens Energy—the “Data Washing Machine”. This initiative goes beyond simple data migration, focusing on ensuring the highest data quality from the very start. By integrating advanced tools and methodologies, Siemens Energy was able to overcome common data challenges and set a new benchmark for migration success. In this blog, we’ll explore how this project is changing large-scale migrations and delivering reliable, long-term results.

The current challenge: Unforeseen data quality issues in migration tests

Traditional data migrations can often feel like navigating through a minefield. You kick off your first migration test, only to be faced with poor data quality – unexpected duplicates, inconsistent formats, or incomplete records. As a result, your team must scramble to address these issues in parallel with the ongoing migration process, slowing down progress and risking the quality of the final S/4HANA instance.

Siemens Energy anticipated this challenge in their vision to migrate millions of records from over 10 source systems to a single global S/4HANA instance. Their goal? Consolidating disparate applications while embedding robust Master Data Governance (MDG) and maintaining data integrity.

Transformation in a large-scale program involves complex requirements, spanning ERP and non-ERP applications, SAP and non-SAP systems, multiple data migrations, consolidations, harmonization, and deduplication.

The solution: A proactive approach to data quality

Accenture’s Intelligent Data Quality (IDQ) methodology takes a proactive approach to data quality. Instead of reacting to poor data during migrations, they shift the focus to data quality from the very beginning of the project lifecycle. This isn’t just a one-time cleanse: It’s an ongoing business process that ensures data accuracy, consistency, and usability at every stage.

To meet Siemens Energy’s specific needs, Accenture partnered with SNP to implement the Data Washing Machine, integrating Accenture’s IDQ tool with SNP’s CrystalBridge platform:

• IDQ: Accenture’s Intelligent Data Quality is a cloud-based SaaS tool designed to securely perform continuous data analysis, cleansing, and monitoring.
• CrystalBridge: SNP’s CrystalBridge transformation software handles both table-based and object-based data load, making it ideal for migrating complex data objects efficiently. This end-to-end approach supports many transformation scenarios like S/4HANA migrations, system carve-outs & mergers, data harmonizations.

How the Data Washing Machine works

1. Selective data extraction: SNP CrystalBridge selectively extracts millions of active business partners from various source systems, ensuring only relevant and needed data is captured.
2. Data profiling: Using IDQ, the data is profiled – applying logic to identify rules within records, helping to pinpoint discrepancies. These rules suggest data cleansing actions and identify cases that need fixing.
3. Source system cleansing: Cleansing actions are implemented directly within source systems, ensuring inaccuracies are addressed at their origin.
4. Golden record creation: Once cleansed, a “golden record” is created, serving as the authoritative, high-quality version of the data.
5. Final load into SAP S/4HANA: SNP CrystalBridge performs the final migration, loading validated, high-quality data into the S/4HANA instance.

The results: High data quality ensures faster time to value and transformation success

Over several cycles, more than 700 rules were applied in 3-week sprints, each targeting specific datasets. These iterations boosted data accuracy from 62% to 95%, delivering cleaner, more reliable data for the first migration test and setting the stage for a smoother overall transition. This execution can be achieved with speed and accuracy in just a matter of hours! With SNP CrystalBridge, we can extract a couple of millions of business partner objects in one hour.

What makes this project unique is the seamless integration between Accenture IDQ and SNP CrystalBridge. Together, they create a robust system where data cleansing, governance, and migration happen in harmony. The tools work in concert, ensuring accuracy and efficiency at every stage.

If you’d like to dive deeper into this innovative approach, don’t miss the video presentation by Jens Loidolt, Associate Director at Accenture, delivered at Transformation World. This session provides valuable insights into the project and its impact on Siemens Energy’s migration journey:

A new benchmark for migration success

The collaboration between Accenture and SNP for Siemens Energy is setting a new benchmark for data migration. By focusing on data quality from the outset and embedding intelligent data governance into migrations, enterprises can ensure smooth, predictable transitions to S/4HANA. The Data Washing Machine concept isn’t just about migrating data – it’s about migrating clean, accurate, and actionable data.

At SNP, we’re proud to be part of this groundbreaking project and are excited to offer similar solutions to enterprises looking to ensure top-notch data quality and achieve successful migrations to S/4HANA.