In this second article of the series we present a practical case of applying Data Quality to Master Data.
Business scenario
An industrial company needs to monitor the quality of Business Partner records – vendors of direct materials, focused on attributes that may cause tax, fiscal or compliance impacts.
Dimensions, Business Rules, Severity and Weight
For this case, the following data quality dimensions were applied:
- Completeness
- Consistency
- Uniqueness
- Accuracy
- Timeliness
- Conformity (Validity)
The following quality rules were selected. Their severity and weight were defined according to the potential business impact in case of non-conformity:
Note: To illustrate examples of business rules related to Data Quality, we use illustrative examples. It is worth noting that the MDM+ BRO solution can reuse the same business rules already in place in the master data governance (Create/Extend/Modify processes) without needing rules built exclusively for the Data Quality context — this guarantees a single source of truth for each business rule.
Data Quality assessment process flow
Continuous improvement cycle. After corrective actions are executed, the process restarts according to the defined frequency, tracking the evolution of data quality over time and ensuring continuous improvement of the business partner master.
Execution frequency and Business Partner – Vendor selection
To cover this business scenario, the following parameters were configured in the execution job:
- Selection: Business Partners with the Vendor role that supplied, in the last 24 months, materials of type ROH (raw material) and HALB (semi-finished) with a financial amount above BRL 50,000;
- Frequency: The assessment runs monthly, overnight between the 10th and 11th.
Detailed result for one Business Partner – Vendor
Below is the result of the run for a Business Partner in January/26:
Given that the surviving-record decision was not made within January/26 (rule R001) and that the action to obtain an updated CND was successful (rule R004), the new February/26 run produced:
After the February/26 run, the surviving-record decision was made (rule R001) and Partner 500032 was marked for deletion. The new March/26 run gave:
The run history also lets us track the quality evolution of this record over time:
Global data quality monitoring for Business Partners
In the previous topic we explored the quality evolution of a specific Business Partner — very useful for granular analysis. However, MDM+ BRO also delivers this evolution at a general level, letting you monitor the evolution of the entire master data base, as shown below:
Data Quality and Master Data Governance
The effectiveness of a Data Quality initiative depends directly on the existence of a Master Data Governance model. Without defined responsibilities, quality rules tend to become just isolated technical validations, disconnected from operational context and business decisions.
In this scenario, Data Owners and Data Stewards play a critical role. They know the real impact of data on processes and act directly in the definition of business rules, severity criteria and expected actions for each type of non-conformity.
Beyond rule definition, mature Data Quality initiatives also depend on the continuous engagement of these stakeholders in handling identified exceptions. Although part of the corrections can be automated, many scenarios still require contextual analysis and human decision-making via workflow.
As shown in the first article of this series, Governance and Data Quality are not isolated disciplines. Governance defines responsibility, context and process; Data Quality operationalizes that control through continuous monitoring, indicators and execution of business-defined rules.
Conclusion
Throughout this series we have shown that Data Quality in Master Data is not just about fixing inconsistent records. Its value lies not only in identifying inconsistencies, but in establishing a continuous control cycle based on business rules aligned to the operational context, continuous monitoring and prioritization by criticality and business impact.
In critical master data — especially vendors with fiscal and tax impact — this means detecting problems, driving corrective actions, tracking KPI evolution and increasing the reliability of processes, integrations and AI initiatives through consistent and governed master data.
About akquinet Brazil
We are specialists in master data governance and Master Data Management (MDM) solutions. As part of the German AKQUINET group, we have been present in Brazil since 2012, developing and delivering projects for clients in a wide range of sectors — retail, industry, agribusiness, pharmaceutical and more. With an experienced and highly qualified team, we have become a market reference, offering solutions such as MDM+ BRO, an SAP-certified add-on for ECC and S/4HANA environments, and MDM+ MUB, a SaaS platform for other ERPs, in addition to specialized consulting services in master data governance and processes.