How An Engineering Leader Created a Cutting-edge CRM Experience

Hi Tech Manufacturing

Client Overview

A rapidly growing engineering and manufacturing organization, located in the midwest, with a turnover of a little over $4 billion. 

The organization is engaged in designing, building, and servicing infrastructure for data centers, communication towers, and commercial/industrial facilities. 

The organization has over twenty thousand employees worldwide, twenty five  manufacturing and assembly facilities, and an established local presence across six countries.

Project Scope

The scope of the project is to migrate data from multiple SAP ECC instances to Oracle Engagement Cloud. The target landscape included Oracle Service Cloud and Oracle Sales Cloud.  The implementation was to be carried out in multiple waves based on the business units. Sales and Service were separate tracks as the business and users were mutually exclusive.

Validation of the Sales and Service data were to be performed against the  master data of Customer and Product that were cleansed and governed by ChainSys dataZen data management tool.

Historical data was archived from SAP ECC and CRM OnDemand to Hadoop Data Lake for reporting and analytics.

Service Users wanted a view of the historical data from the Hadoop lake, without exiting Oracle applications.

Business Situation

The business was using multiple instances of SAP ECC for their Services business and Oracle CRM OnDemand for the Sales function.

The services business was not fully integrated with exceptions handled manually and the depot repair running in silo. There was no validation against warranty or entitlement of services when the service notifications were logged. The installed base data was never cleaned. Users were storing critical service information in long concatenated text in SAP leading to important business data hidden in unstructured format.  

The Prospects and Sales and Service Contact data were never cleaned and existed in multiple sources.

The Services and Sales data migration were part of a larger Transformation program. The life cycle of the digital transformation program was executed in multiple waves and each wave had four iterations for CRP, SIT, UAT and PROD. The Sales and Service data had to be fine tuned on every cycle and iteration.

Technical Situation

For the Services business, SAP Notification transaction data was extracted based on the extraction criteria set by the business. The open transactions were migrated as Service requests in Oracle Service cloud. The List of Values (LoVs) were different in each instance of SAP ECC. They were rationalized and compared with the proposed LoVs in Oracle Cloud instances to create cross-references for transformation.  Every single attribute of SAP ECC was mapped with the attributes of Oracle Services Cloud. The mapping was carried out in three passes and multiple iterations. dataZap’s pre-built templates were used to speed up the migration process.

Extracting from SAP, Validating with Master data, Enrichment & Transformation of selected attributes, pre-validation and loading of data were carried out in multiple cycles for each track, in each iteration and in each wave.

A similar approach was followed for migrating open Opportunities from Oracle CRM OnDemand to Oracle Sales Cloud. Input for the Sales Territories were taken from the HR data, enriched with additional attributes, and loaded into Sales cloud.

Historical data were required for reference and were archived and moved to Hadoop Data Lake. The transactions included were SAP ECC Notifications & Work orders and the Opportunities from CRM on-Demand.

dataZap’s pre-built templates were used to speed up the operation. The pre-built adapters and bapis were used for extracting data from SAP. For data extraction, transformation, and load, ChainSys dataZap was used.

Apart from migrating the data, Oracle setup corrections and fine tuning of the LoVs were carried out in each data migration iteration.  One of the challenges faced while migrating the service transactions was the validation against the Installbase customer asset and warranty. More than a million of Customer assets data were cleansed.


dataZap comes with standard templates for mapping SAP attributes with Oracle Cloud, standard FBDI / web services for data loading and adapters for extracting data from SAP.  Connectivity were established directly with the source system SAP ECC6 and the target systems Oracle Cloud and Hadoop Data Lake using the configurable Connection option in the tool.

Business rules for the extraction, transformation and loading were configured for reuse. Preload and post load data validations were carried out. Post load data reconciliation reports were provided to the business for verification and validation. Master data and historical transaction data were loaded into Hadoop Data Lake using dataZap tool.

Custom screens were developed for querying historical services data from Hadoop Data Lake. Users could use this to query historical data without leaving Oracle Service cloud.



Key benefit to the project was using the prebuilt templates that ChainSys had delivered for data extraction, transformation and load (ETL) processes without starting to build them from scratch. Data was successfully migrated through testing in iterative rounds/sprints, with data validation and using best business practices in data management.

Other key benefits included:

  • A 30% improvement in services function. Business was able to continue and operate normally after the successful data migration and validate against eligibility at the time of service request itself.
  • End to End service process with all service data available for consolidated reporting and analytics.
  • With the ChainSys tools once configured, 60-70% repeatability was ensured in multiple waves and test cycles during the life cycle of implementation.
  • More meaningful data was captured in Oracle Sales cloud, thus improving the Sales productivity.

Products and Services Used

dataZap - Pre-Configured Templates & Migration Engine to Extract, Transform, Pre-Validate, Load, Reconcile & Report.

dataZen - To 'Get Clean' and 'Stay Clean', and Introduce Master Data Governance.


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