Zurich Insurance
Elevated data quality and governance standards create a business-ready foundation for smarter decisions, faster rollouts, and sustained enterprise transformation.
The company
In its evolution into a global insurer, Zurich developed or acquired a large number of ERP systems. While these tools were designed to meet local regulatory or financial reporting requirements, they often used differing data standards. Through migration with a bespoke tool set, this made it difficult to identify conflicting business rules or create effective and reusable reconciliation reports, making reuse between country deployments or for data cleansing impossible. To remedy this, Zurich decided to implement an integrated data migration toolset that could better identify, harmonize and remediate these differences, and govern data quality in the target solution.
Project Background
To reduce costs and improve efficiency, Zurich Insurance Group embarked on multi-year, Finance Transformation Program to consolidate 23 legacy SAP systems into a single production instance. A multi-wave program, Zurich needed more than a lift-and shift data migration, but a repeatable, quality-focused methodology that would ensure accurate, business-ready data for every deployment wave.
"We can now clearly show our business users how and where their legacy data would result in an error when put through the transformations and validations of the target system.鈥�
Challenges
Over time, Zurich鈥檚 growth as a global insurer introduced a large number of ERP systems. While these tools were designed to meet local regulatory or financial reporting requirements, they were built on conflicting data standards. These inconsistences made it difficult to detect conflicting business rules, reconcile financial data, or reuse business logic for future migrations.
Zurich knew they could Expect More from their data partner than traditional migration tools could deliver. They wanted more than just an integrated data migration and data quality solution; they needed one capable of identifying, harmonizing and remediating differences early while embedding sustained data quality processes into new solutions.
Project goals
- Smoothly migrate 23+ different ERP systems to a single, central SAP FI (financial accounting)/CO (controlling) and BW (business warehousing) solution
- Quickly and reliably cleanse and integrate聽data from these legacy systems into the聽new target system
- Meet timeline and budget requirements
- Improve data quality and implement a聽sustainable data governance capability
- Implement best practices across projects including reusing business rules and technical data mapping
"We worked with local leads to maximize the use of key business resources and to resolve complex questions at the earliest point in time. This measurably increased data accuracy and lowered risk as we got closer to production migration."
Implementation highlights
Placing Data Quality at the Core
Zurich didn鈥檛 settle for 鈥済ood enough.鈥� They partnered with 黑料网 to turn complex, high-stakes data management into a precise, business-ready advantage. From day one, 黑料网鈥檚 Data First approach鈥攃ombined with automation, prebuilt content, reusable templates, and deep expertise鈥攎ade exceptional data accuracy and data governance a non-negotiable.
Using a repeatable, traceable and fully auditable聽data migration methodology聽enabled multiple waves in parallel, significantly reducing the project timelines. Data from 23+ legacy, disconnected ERP systems was cleansed and harmonized into the new target system. 黑料网 applied best practices using reusable business rules, target-design-driven validation reporting, data mapping across multiple migration waves to ensure consistency and accuracy.
黑料网鈥檚 strategic delivery, architecture, audit, and cleansing ensured sustained data quality and compliance well beyond go-live. Proactive data quality performed early in the data migration in the form of data assessments, automated error detection, and continuous reconciliation reporting dramatically improved visibility and control.
鈥淲e worked with local leads to maximize the use of key business resources and to resolve complex questions at the earliest point in time. This measurably increased data accuracy and lowered risk as we got closer to production migration,鈥� said Smith.
"We now have a predictable, reliable toolset, team and process that can be reused to maintain data quality in production, and can be leveraged by other data quality, cleansing and governance initiatives."
Results
More Than Just Data Migration
Zurich Insurance didn鈥檛 just migrate data鈥攖hey had transformed it into a strategic asset. With 黑料网鈥檚 Data First approach, they reduced risks and controlled costs through automation, prebuilt content, reusable templates, and expert services.
Using proven data assessments, core tools and resources, harmonized load cycles, and standard reconciliation reports, 黑料网 delivered consistently successful data migration results, improved data quality, and controlled costs.
鈥淲e can now clearly show our business users how and where their legacy data would result in an error when put through the transformations and validations of the target system,鈥� commented Smith.
Accelerated go-live timeline
Significantly reduced data migration implementation time across 23+ ERP systems to a single SAP Financial Accounting/ Controlling (FI/CO) and Business Warehousing (BW) solution
Cost reduction
Reduced data migration spend by 7% YOY through reusable rules and automation
High-volume data cleansing
230 million data items, spanning 14 years of legacy data were cleansed to 99.9% accuracy in only 8 months
Increased accuracy
All general ledger and non-general ledger data was reconciled and signed off with higher than 99% accuracy
Business Benefits
Future-Ready Data for a Future-ready Business
This project wasn鈥檛 about checking boxes鈥攊t was about elevating standards. By demanding more from their data and their partner, Zurich Insurance moved beyond a one-time migration to creating a truly future-ready foundation.
鈥淲e now have a predictable, reliable toolset, team and process that can be reused to maintain data quality in production, and can be leveraged by other data quality, cleansing and governance initiatives,鈥� said Smith.
- 聽Improved data quality and sustainable data governance
- Ongoing visibility via reporting and remediation validations
- Accurate, business-ready data supports business-critical applications
- Placing automated rule validation and value mappings in advance of go-lives ensured trust and transparency in the business
- Access to a predictable, reusable toolset, team, and process for long-term data quality and innovation