- SAP Managed Tags
Accurate, consistent, and reliable data is one of the pillars of successful data migration. Without it, even the most well-planned migration process can end up in errors, delays, and costly setbacks.
In this post, we’ll explore data quality best practices, underline the fundamentals of data quality in SAP systems, and look through the latest techniques used in this area.
Data Quality Attributes
Bringing your data to the latest quality standards is crucial for successful data migration and maintenance, as the success of the data management procedure depends greatly on the condition of the initial data landscape. Besides this, improving the quality of your data helps you stay ahead of the competition and make informed business decisions.
So, in order to benchmark the quality of SAP data, the following attributes are used:
- Accuracy. Make sure your data is clean and accurate to achieve a successful data migration.
- Completeness. Collect all the data that is needed to be transferred or operated on.
- Conformance. Keep your data in a unified and standardized format across all of your SAP systems.
- Consistency. Do your best to ensure all data across SAP systems has a similar format.
- Timeliness. Make sure the data is available on request whenever needed to ensure no system lags.
- Uniqueness. Keep your data well-maintained without duplicated and unnecessary legacy elements.
- Validity. Ensure that all your data is derived from proven sources, is error-free, and valid for further operations.
Making your data match these criteria as much as possible is one of the most important steps to quick and lag-free SAP data migration.
SAP Data Quality Challenges: Real-Life Examples
Here are a few examples of how SAP users address data quality challenges in real-life environments. All the issues are taken from people’s experience with Migravion, an SAP data management solution.
Challenge 1: Optimizing the Data Structure and Completeness for More Accurate SAP Reporting
The company faced a challenge when several fields in the SAP export were either consistently "0" or contained too many unique values, complicating aggregation and analysis. By leveraging Migravion’s Data Profiling module, the data management team gained clear insights:
- Constant Columns: Fields such as "No. of contacts" indicated data entry issues or demonstrated that these columns were not actively used.
- High Cardinality: Attributes like "Sales Document" exhibited high uniqueness, confirming their role as identifiers.
- Missing Values: A 9.3% gap in data for fields like "Valid from/to" pinpointed critical areas requiring prompt remediation to avoid reporting errors.
As a result, managers were able to:
- Remove or repurpose unused fields, thereby reducing system noise.
- Enhance quality control by enforcing mandatory data entry for previously overlooked fields.
- Lower the manual effort required by analysts, who no longer had to search for anomalies by hand.
Ultimately, these improvements led to more accurate reporting in SAP modules (SD, MM, etc.), fewer planning and integration errors, and greater agility in managing customer and product information.
Challenge 2: Enhancing Data Completeness for Accurate Reporting
The organization encountered a challenge: a screenshot clearly illustrated missing values across various fields—light gaps in the visualization indicated absent data. Notably, fields such as Valid from, Valid to, Telephone, and select financial/control columns (e.g., WBS Element, Cred.Contr.Area) were only partially populated.
Leveraging Migravion’s Data Profiling module, the team gained clear insights:
- Focus on Critical Fields:
Missing dates compromised order and contract accuracy, while absent telephone numbers hindered customer communication. - Prioritize Data Enrichment:
The team swiftly enriched contact information and enforced mandatory date entries to streamline cleanup. - Strengthen Quality Control:
By updating input interfaces with required fields and additional validation, they reduced errors in orders and calculations.
- Focus on Critical Fields:
As a result, data completeness and timeliness in SAP improved, manual corrections decreased, order-to-payment cycles accelerated, and reporting accuracy was enhanced.
Challenge 3: Visual Monitoring of Problem Areas and Optimization of Field Completion
A dashboard screenshot displayed key metrics that revealed data compliance with set rules and the frequency of violations.
Notably, the visualization highlights:
- Rule Adherence Rate (top left):
Indicates which rules (e.g., "BP_Type," "City," "Email," "Last_Name") are consistently followed and where compliance is low. - Top Violated Rules (top right):
Shows the most frequently broken rules (e.g., invalid "Postal_Code," incorrect "BP_ID," missing "Country"), helping to pinpoint critical error-prone fields. - Error Rate per Record (bottom left):
Demonstrates how often errors occur per record. High error rates in certain ranges may signal data source issues or mass upload errors. - Field Usage Rate (bottom right):
Reveals how often fields are populated. For instance, while "Email" is filled in over 90% of records, "Company Name" appears less frequently, guiding decisions on which fields to mandate or consolidate.
As a result, the team was able to:
- Prioritize Remediation:
Target problematic rules and fields with focused measures, such as interface tweaks, staff training, or source data reviews. - Improve User Experience:
Reduce data entry errors, thereby saving time.
- Enhance SAP Data Quality:
Achieve more accurate orders, addresses, and inventory figures—directly boosting process efficiency and overall business accuracy.
SAP Data Quality Best Practices
Before we switch to data quality best practices, let’s take a look at the aspects of SAP data quality management you need to cover:
- Data governance integration – establishing a structured, systematic approach to managing and overseeing data to ensure the data meets the required quality standards.
- Data profiling – analyzing and assessing the content, structure, and quality of data to identify duplication and format inconsistency.
- Data cleansing – identifying and removing errors, inconsistencies, inaccuracies, and anomalies to ensure that the data is accurate, complete, and usable.
- Data matching and deduplication – merging duplicate records (e.g., customers, vendors) to improve consistency and eliminate redundancy.
- Automation and monitoring – automating quality checks and real-time monitoring to quickly detect and resolve anomalies.
- Data enrichment – filling raw data with external or contextual information (e.g., geocodes, industry classifications) to boost value.
- Data aggregation and comparison – consolidating and comparing data from various sources (e.g., SAP vs. legacy ERP) to identify discrepancies and ensure consistency.
Covering these aspects throughout your SAP systems is crucial to ensure and maintain the quality of your data.
With Migravion, these aspects are covered through the Data Quality module, which is designed specifically to focus on profiling, cleansing, and deduplication. It aligns with industry standards while addressing SAP challenges via native integration with SAP data models (e.g., material codes, vendor hierarchies) and pre-built plugins for migration tasks (e.g., tax ID validation). These features help lower migration risks, reduce manual data checks, and enforce governance within SAP workflows.
Module | Migravion’s Approach |
Data governance integration | Custom validation rules; block non-compliant data uploads |
Data profiling | Pre-/Post-Analysis Plugins; Python integration (Pandas, scripts) |
Data cleansing | Transformations Plugin; side-by-side data comparison |
Data matching and deduplication | Configurable thresholds; advanced fuzzy logic via Python |
Automation and monitoring | Jenkins/cron integration; email notifications |
Data enrichment | Join Tables Plugin; custom key definitions |
Data aggregation and comparison | Cross-system alignment; CSV/Excel discrepancy reports |
The optimal way to make sure all the aspects of SAP quality data are covered is to follow these best practices:
Establish data quality standards
To improve the quality of your data, start by defining what "high-quality data" means for your organization. It's essential that these standards align with the specific needs and goals of your business, as well as with the specific industry benchmarks.
Defining data quality standards will allow you to establish reliable benchmarks for achieving data accuracy through your systems. It will also help identify the areas for improvement.
Conduct regular data audits
Consistent data audits are critical for maintaining SAP data quality. By regularly reviewing your data, you can evaluate data accuracy and pinpoint areas that require correction.
Auditing your data may involve assessing data completeness, consistency, and accuracy. The best way to conduct data audits is to use dedicated software designed to streamline SAP data quality management .
Monitor data quality regularly
Compared to data audits, which is a one-time action aimed at estimating the final result, data monitoring is an ongoing activity that helps you evaluate the process and make the necessary improvements or adjustments promptly.
Ongoing monitoring of SAP data quality is vital to sustain accurate, reliable data over time. By setting up processes to identify and fix issues proactively, you can maintain the integrity of your data.
Continuous monitoring offers two key benefits: First, it lets you reassess your data and spot any emerging issues. Second, it helps you confirm that the improvements you've made are delivering the desired results and make timely adjustments to the data quality management strategy.
Ensure data entry validation
Data entry mistakes are costly and are the main reason for data quality issues. To minimize errors, you need to ensure that all data inputs are valid and accurate.
This strategy involves precise checking for any missing or incomplete data. Indeed, dedicated SAP-compatible tools can help you here to save time and speed up the process.
Implement a data governance policy
Establishing clear data governance policies is key to maintaining high-quality data in SAP. This framework should address the following components:
- Defining data ownership
- Setting access controls for data
Developing data management processes
It's crucial to engage your team in this process to ensure everyone is aligned and equipped to handle the data effectively. Data management tools can support and simplify governance implementation, as well.
Utilizing AI for Data Quality
Artificial intelligence can greatly improve the process of data quality management, which is why it is incorporated into the most sophisticated SAP data quality management tools.
AI capabilities do a great job when it comes to automating data capture, identifying duplicated, corrupted, or missing data pieces, detecting and minimizing errors, and assessing data relevance. What’s more, AI-powered predictive analytics may help identify potential flaws in data migration scenarios and help prevent them. Some cutting-edge data management platforms already harness the power of AI technologies to ensure hassle-free and efficient data quality management.
Conclusion
SAP systems have always paid great attention to the quality of data and ways to improve it. That’s why using these data quality best practices in your organization will help you streamline the SAP data quality management routine , keep your data organized and structured, cut operational costs, and prepare your current landscape for digital transformation.
P.S. Do you know of other ways to eliminate data duplication in SAP, or have anything to add to the cases described in the post? Share your insights and experiences in the comments below.
Accurate, consistent, and reliable data is one of the pillars of successful data migration. Without it, even the most well-planned migration process can end up in errors, delays, and costly setbacks.
In this post, we’ll explore data quality best practices, underline the fundamentals of data quality in SAP systems, and look through the latest techniques used in this area.
Data Quality Attributes
Bringing your data to the latest quality standards is crucial for successful data migration and maintenance, as the success of the data management procedure depends greatly on the condition of the initial data landscape. Besides this, improving the quality of your data helps you stay ahead of the competition and make informed business decisions.
So, in order to benchmark the quality of SAP data, the following attributes are used:
- Accuracy. Make sure your data is clean and accurate to achieve a successful data migration.
- Completeness. Collect all the data that is needed to be transferred or operated on.
- Conformance. Keep your data in a unified and standardized format across all of your SAP systems.
- Consistency. Do your best to ensure all data across SAP systems has a similar format.
- Timeliness. Make sure the data is available on request whenever needed to ensure no system lags.
- Uniqueness. Keep your data well-maintained without duplicated and unnecessary legacy elements.
- Validity. Ensure that all your data is derived from proven sources, is error-free, and valid for further operations.
Making your data match these criteria as much as possible is one of the most important steps to quick and lag-free SAP data migration.
SAP Data Quality Challenges: Real-Life Examples
Here are a few examples of how SAP users address data quality challenges in real-life environments. All the issues are taken from people’s experience with Migravion, an SAP data management solution.
Challenge 1: Optimizing the Data Structure and Completeness for More Accurate SAP Reporting
The company faced a challenge when several fields in the SAP export were either consistently "0" or contained too many unique values, complicating aggregation and analysis. By leveraging Migravion’s Data Profiling module, the data management team gained clear insights:
- Constant Columns: Fields such as "No. of contacts" indicated data entry issues or demonstrated that these columns were not actively used.
- High Cardinality: Attributes like "Sales Document" exhibited high uniqueness, confirming their role as identifiers.
- Missing Values: A 9.3% gap in data for fields like "Valid from/to" pinpointed critical areas requiring prompt remediation to avoid reporting errors.
As a result, managers were able to:
- Remove or repurpose unused fields, thereby reducing system noise.
- Enhance quality control by enforcing mandatory data entry for previously overlooked fields.
- Lower the manual effort required by analysts, who no longer had to search for anomalies by hand.
Ultimately, these improvements led to more accurate reporting in SAP modules (SD, MM, etc.), fewer planning and integration errors, and greater agility in managing customer and product information.
Challenge 2: Enhancing Data Completeness for Accurate Reporting
The organization encountered a challenge: a screenshot clearly illustrated missing values across various fields—light gaps in the visualization indicated absent data. Notably, fields such as Valid from, Valid to, Telephone, and select financial/control columns (e.g., WBS Element, Cred.Contr.Area) were only partially populated.
Leveraging Migravion’s Data Profiling module, the team gained clear insights:
- Focus on Critical Fields:
Missing dates compromised order and contract accuracy, while absent telephone numbers hindered customer communication. - Prioritize Data Enrichment:
The team swiftly enriched contact information and enforced mandatory date entries to streamline cleanup. - Strengthen Quality Control:
By updating input interfaces with required fields and additional validation, they reduced errors in orders and calculations.
- Focus on Critical Fields:
As a result, data completeness and timeliness in SAP improved, manual corrections decreased, order-to-payment cycles accelerated, and reporting accuracy was enhanced.
Challenge 3: Visual Monitoring of Problem Areas and Optimization of Field Completion
A dashboard screenshot displayed key metrics that revealed data compliance with set rules and the frequency of violations.
Notably, the visualization highlights:
- Rule Adherence Rate (top left):
Indicates which rules (e.g., "BP_Type," "City," "Email," "Last_Name") are consistently followed and where compliance is low. - Top Violated Rules (top right):
Shows the most frequently broken rules (e.g., invalid "Postal_Code," incorrect "BP_ID," missing "Country"), helping to pinpoint critical error-prone fields. - Error Rate per Record (bottom left):
Demonstrates how often errors occur per record. High error rates in certain ranges may signal data source issues or mass upload errors. - Field Usage Rate (bottom right):
Reveals how often fields are populated. For instance, while "Email" is filled in over 90% of records, "Company Name" appears less frequently, guiding decisions on which fields to mandate or consolidate.
As a result, the team was able to:
- Prioritize Remediation:
Target problematic rules and fields with focused measures, such as interface tweaks, staff training, or source data reviews. - Improve User Experience:
Reduce data entry errors, thereby saving time.
- Enhance SAP Data Quality:
Achieve more accurate orders, addresses, and inventory figures—directly boosting process efficiency and overall business accuracy.
SAP Data Quality Best Practices
Before we switch to data quality best practices, let’s take a look at the aspects of SAP data quality management you need to cover:
- Data governance integration – establishing a structured, systematic approach to managing and overseeing data to ensure the data meets the required quality standards.
- Data profiling – analyzing and assessing the content, structure, and quality of data to identify duplication and format inconsistency.
- Data cleansing – identifying and removing errors, inconsistencies, inaccuracies, and anomalies to ensure that the data is accurate, complete, and usable.
- Data matching and deduplication – merging duplicate records (e.g., customers, vendors) to improve consistency and eliminate redundancy.
- Automation and monitoring – automating quality checks and real-time monitoring to quickly detect and resolve anomalies.
- Data enrichment – filling raw data with external or contextual information (e.g., geocodes, industry classifications) to boost value.
- Data aggregation and comparison – consolidating and comparing data from various sources (e.g., SAP vs. legacy ERP) to identify discrepancies and ensure consistency.
Covering these aspects throughout your SAP systems is crucial to ensure and maintain the quality of your data.
With Migravion, these aspects are covered through the Data Quality module, which is designed specifically to focus on profiling, cleansing, and deduplication. It aligns with industry standards while addressing SAP challenges via native integration with SAP data models (e.g., material codes, vendor hierarchies) and pre-built plugins for migration tasks (e.g., tax ID validation). These features help lower migration risks, reduce manual data checks, and enforce governance within SAP workflows.
Module | Migravion’s Approach |
Data governance integration | Custom validation rules; block non-compliant data uploads |
Data profiling | Pre-/Post-Analysis Plugins; Python integration (Pandas, scripts) |
Data cleansing | Transformations Plugin; side-by-side data comparison |
Data matching and deduplication | Configurable thresholds; advanced fuzzy logic via Python |
Automation and monitoring | Jenkins/cron integration; email notifications |
Data enrichment | Join Tables Plugin; custom key definitions |
Data aggregation and comparison | Cross-system alignment; CSV/Excel discrepancy reports |
The optimal way to make sure all the aspects of SAP quality data are covered is to follow these best practices:
Establish data quality standards
To improve the quality of your data, start by defining what "high-quality data" means for your organization. It's essential that these standards align with the specific needs and goals of your business, as well as with the specific industry benchmarks.
Defining data quality standards will allow you to establish reliable benchmarks for achieving data accuracy through your systems. It will also help identify the areas for improvement.
Conduct regular data audits
Consistent data audits are critical for maintaining SAP data quality. By regularly reviewing your data, you can evaluate data accuracy and pinpoint areas that require correction.
Auditing your data may involve assessing data completeness, consistency, and accuracy. The best way to conduct data audits is to use dedicated software designed to streamline SAP data quality management .
Monitor data quality regularly
Compared to data audits, which is a one-time action aimed at estimating the final result, data monitoring is an ongoing activity that helps you evaluate the process and make the necessary improvements or adjustments promptly.
Ongoing monitoring of SAP data quality is vital to sustain accurate, reliable data over time. By setting up processes to identify and fix issues proactively, you can maintain the integrity of your data.
Continuous monitoring offers two key benefits: First, it lets you reassess your data and spot any emerging issues. Second, it helps you confirm that the improvements you've made are delivering the desired results and make timely adjustments to the data quality management strategy.
Ensure data entry validation
Data entry mistakes are costly and are the main reason for data quality issues. To minimize errors, you need to ensure that all data inputs are valid and accurate.
This strategy involves precise checking for any missing or incomplete data. Indeed, dedicated SAP-compatible tools can help you here to save time and speed up the process.
Implement a data governance policy
Establishing clear data governance policies is key to maintaining high-quality data in SAP. This framework should address the following components:
- Defining data ownership
- Setting access controls for data
Developing data management processes
It's crucial to engage your team in this process to ensure everyone is aligned and equipped to handle the data effectively. Data management tools can support and simplify governance implementation, as well.
Utilizing AI for Data Quality
Artificial intelligence can greatly improve the process of data quality management, which is why it is incorporated into the most sophisticated SAP data quality management tools.
AI capabilities do a great job when it comes to automating data capture, identifying duplicated, corrupted, or missing data pieces, detecting and minimizing errors, and assessing data relevance. What’s more, AI-powered predictive analytics may help identify potential flaws in data migration scenarios and help prevent them. Some cutting-edge data management platforms already harness the power of AI technologies to ensure hassle-free and efficient data quality management.
Conclusion
SAP systems have always paid great attention to the quality of data and ways to improve it. That’s why using these data quality best practices in your organization will help you streamline the SAP data quality management routine , keep your data organized and structured, cut operational costs, and prepare your current landscape for digital transformation.
P.S. Do you know of other ways to eliminate data duplication in SAP, or have anything to add to the cases described in the post? Share your insights and experiences in the comments below.
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