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Last updated on 2024年7月14日

What EDI strategies improve your data quality and analytics?

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Electronic data interchange (EDI) is the process of exchanging business documents and data electronically between different parties, such as suppliers, customers, and partners. EDI can improve your operational efficiency, reduce errors, and lower costs, but it also requires careful planning and management to ensure data quality and analytics. In this article, you will learn about some EDI strategies that can help you achieve these goals and optimize your EDI performance.

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jaya Krishna tummala ,MS, CSM

Award Winning Senior Functional Quality Assurance|BSA|SAP QA|Safe 6.0 Agilist|Sigma Xi Full Member|IEEE Senior…

1 Define your data standards

One of the first steps to improve your data quality and analytics is to define your data standards and requirements for EDI transactions. Data standards are the rules and formats that specify how data should be structured, validated, and exchanged. They can include industry standards, such as ANSI X12 or EDIFACT, as well as custom standards, such as XML or JSON. By defining your data standards, you can ensure that your EDI data is consistent, accurate, and compatible with your systems and partners.

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jaya Krishna tummala ,MS, CSM

Award Winning Senior Functional Quality Assurance|BSA|SAP QA|Safe 6.0 Agilist|Sigma Xi Full Member|IEEE Senior Member|Fellow IETE|Author
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Implement automated data quality checks and controls within EDI systems, processes, and workflows to enforce data standards and prevent data errors or discrepancies proactively. Use automated validation scripts, rules engines, or algorithms to validate data accuracy, completeness, and consistency in real-time or batch processing scenarios.

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2 Implement data quality checks

Another EDI strategy to improve your data quality and analytics is to implement data quality checks throughout your EDI process. Data quality checks are the methods and tools that verify and correct your data before, during, and after transmission. They can include data validation, data cleansing, data mapping, data transformation, and data reconciliation. By implementing data quality checks, you can prevent and resolve data errors, such as missing, duplicate, or incorrect data, that can affect your EDI transactions and reports.

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jaya Krishna tummala ,MS, CSM

Award Winning Senior Functional Quality Assurance|BSA|SAP QA|Safe 6.0 Agilist|Sigma Xi Full Member|IEEE Senior Member|Fellow IETE|Author
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Align data structures and formats between different systems to facilitate accurate data exchange and integration. Also convert data formats and structures as needed to meet the requirements of different systems and applications involved in the EDI process.

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3 Automate data integration

A third EDI strategy to improve your data quality and analytics is to automate data integration between your EDI system and your other business applications, such as ERP, CRM, or BI. Data integration is the process of transferring and synchronizing your data across different sources and destinations. By automating data integration, you can eliminate manual data entry, reduce human errors, and speed up data processing. You can also enable real-time data access and visibility, which can enhance your decision making and performance measurement.

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4 Monitor and measure data performance

A fourth EDI strategy to improve your data quality and analytics is to monitor and measure your data performance on a regular basis. Data performance is the indicator of how well your data meets your business objectives and expectations. It can include metrics such as data accuracy, completeness, timeliness, reliability, and usability. By monitoring and measuring your data performance, you can identify and address any data issues, such as data gaps, inconsistencies, or anomalies, that can affect your EDI outcomes and insights.

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5 Update and optimize data processes

A fifth EDI strategy to improve your data quality and analytics is to update and optimize your data processes according to your changing business needs and environment. Data processes are the activities and workflows that involve your data, such as data collection, transmission, storage, analysis, and reporting. By updating and optimizing your data processes, you can adapt to new data sources, formats, standards, and regulations, as well as new business goals, opportunities, and challenges. You can also improve your data efficiency, security, and scalability.

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6 Learn from data best practices

A sixth EDI strategy to improve your data quality and analytics is to learn from data best practices and benchmarks in your industry and domain. Data best practices are the proven methods and techniques that can help you achieve data excellence and value. They can include data governance, data management, data security, data quality, data analytics, and data innovation. By learning from data best practices, you can compare and improve your data performance, as well as discover new ways to leverage your data for competitive advantage.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What EDI strategies improve your data quality and analytics?

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1 Define your data standards

2 Implement data quality checks

3 Automate data integration

4 Monitor and measure data performance

5 Update and optimize data processes

6 Learn from data best practices

7 Here’s what else to consider

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