Treading unchartered waters
Operating in over 60 countries and supported by 9,000 employees, this leading technology and
communications company hadn’t prioritized Data Quality and Master Data Management as part of its
business information management strategy. As a result, its data quality had degraded over time,
resulting in a variety of issues.
For example, inconsistent product categorization adversely impacted accuracy of revenue recognition
treatment, while challenges with customer data resulted in manual accounting rework. Additionally,
obsolete data in its systems required extensive labor to monitor, maintain, and action.
Background and challenge
The company was looking to implement SAP RAR, an automated revenue recognition and accounting
solution. Yet, significant data challenges, both known and unknown, emerged as the primary obstacle.
As a result, the company needed immediate help in identifying and addressing their data quality
issues.
To combat inaccurate, inconsistent, and out-of-date data, the client needed to overhaul how it
defined, measured, and maintained data quality within its various revenue data impacting systems.
There was a need to: drive data accountability, document data definitions, standards and metrics to
measure data quality; review existing pain-points and determine the root cause of data quality
issues; and assist business teams in troubleshooting accounting issues caused due to bad data
Solution
To make standardizing and optimizing data fields in the global revenue platform manageable, we
prioritized small number of data fields based on their degree of impact and importance to business
priorities. We compiled data and process maps to help data producers and owners fix data and prevent
errors going forward
STAGE 1 - Pre-migration data optimization
- Analyzed the entire migration data dump
- Evaluated quality and stability of the data being moved
- Defined various inclusion / exclusion criteria based on SAP RAR data quality requirements
- Evaluated and categorized each line item per the devised data categorization criteria
- Identified and corrected issues
Filtering out the noise through a tiered process to focus on the key data fields
STAGE 3 - Ongoing data governance
- Established a strong data quality rules management and monitoring process
- Structured root cause analysis to identify and address underlying causes of a problem
and prevent future bad data
- Built new processes and controls to achieve compliance and provide assurance over data
flowing into SAP RAR
- Deployed Alteryx and RPA managed solutions to monitor data quality
(next to real time data governance)
Illustrative workflow data quality rules to assess data from its point of creation and
appropriately categorize it for governance purposes
Results
Data optimization efforts resulted in 99% of SAP records being analyzed and optimized for either
transitioning to SAP RAR in an accurate and consistent manner or management through an alternate
solution
Data optimization results
Not only was the client successful in alleviating their data quality pain points to meet their SAP
RAR migration objectives, but also in establishing a strong foundation to perform more advanced data
governance practices:
- Improved data quality for SAP RAR migration
- Deployed an automated tool to direct revenue data to its appropriate accounting solution
- Developed business acumen in staff to ensure data input quality
- Implemented a data stewardship platform to provide real-time insights on data quality
Providing adequate assurance through continuous monitoring and regular audits
