1. What are data inconsistencies?
2. Why do data inconsistencies occur?
3. How to identify data inconsistencies?
4. How to analyze data inconsistencies?
5. How to resolve data inconsistencies?
1. What are data inconsistencies?
Data inconsistencies are situations where the same data element has different values or formats in different sources or systems. For example, a customer’s name, address, or order status might be different in your ERP system and your CRM system. This can lead to confusion, errors, or missed opportunities in your business operations. Data inconsistencies can also affect your data analysis and reporting, as they can distort your metrics, trends, or insights.
2. Why do data inconsistencies occur?
Data inconsistencies can occur for a variety of reasons, including human mistakes, data integration issues, data quality issues, and data security issues. For example, typos or incorrect data entry can lead to errors or variations in your data. Additionally, if your data sources or systems are not connected or synchronized properly, your data might not be updated or transferred correctly. Plus, if your data is not validated, standardized, or cleaned, it could contain duplicates, missing values, or incorrect values. Lastly, if your data is not protected or encrypted, it might be exposed to unauthorized users or malicious attacks which can alter or corrupt the data.
3. How to identify data inconsistencies?
To identify data inconsistencies in your ERP system, it’s important to perform regular data audits and checks. Data profiling tools can be used to generate statistics, summaries, and reports on your data. Data comparison tools can help you compare your data across different sources or systems to find discrepancies or anomalies. Additionally, data validation tools can be used to check your data for accuracy, completeness, consistency, and conformity. All of these methods are essential for identifying any issues with your data.
4. How to analyze data inconsistencies?
In order to analyze data inconsistencies in your ERP system, you must first determine their root causes and impacts. Various techniques and frameworks can be used to do this, such as assessing data quality dimensions such as accuracy, completeness, consistency, timeliness, and relevance. Data quality assessment tools can be used to identify, classify, and prioritize data inconsistencies. Lastly, data quality improvement tools can be employed to define, execute, and track data correction, prevention, or enhancement actions.
5. How to resolve data inconsistencies?
To resolve data inconsistencies in your ERP system, you need to apply the appropriate data quality solutions. Data cleansing can fix or remove errors or variations, while data integration can combine or synchronize data from different sources. Additionally, data governance can establish and enforce your data policies, rules, and standards. Tools such as deduplication, standardization, normalization, extraction, transformation, loading, replication, data security, data quality, data lineage, and data stewardship can be used to perform these operations.
SOURCE: https://www.linkedin.com/advice/3/what-do-you-encounter-data-inconsistencies-6hyif?trk=cah1
