Last updated on Jul 12, 2024

You're facing data quality discrepancies in system mergers. How do you ensure seamless integration?

Merging different systems can be a daunting task, especially when you encounter data quality discrepancies. These inconsistencies can cause significant integration issues, leading to potential data loss or corruption. To ensure seamless integration, it's crucial to address these discrepancies head-on. You need to have a clear understanding of the data structures, formats, and quality standards of each system involved. By following a structured approach, you can mitigate the risks and pave the way for a successful system merger.

1 Data Audit

Begin by conducting a thorough data audit across all systems. This will help you identify and categorize the types of data quality issues present. During the audit, assess aspects like accuracy, completeness, consistency, and timeliness of the data. Understanding the scope and nature of the discrepancies is the first step in resolving them. It's like being a detective in the world of data, where your keen eye for detail can uncover hidden errors that might otherwise disrupt the integration process.

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SAQUIB KHAN

Data Scientist | MSc Data Science Candidate | Marketing MIS Executive
Develop a comprehensive data mapping process to identify data elements, their sources, and any discrepancies or inconsistencies across the merging systems, providing a clear roadmap for data integration. Implement robust data validation procedures, including automated checks and manual reviews, to identify and address data quality issues at each stage of the integration process. Continuously monitor the integrated data for quality and consistency, and make iterative improvements to the data integration processes based on feedback and lessons learned. Work with stakeholders, including subject matter experts and data owners, to understand the business context and requirements, and to ensure that the integrated data meets their needs.
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José OSCAR Schmidt Alves Brochado

Especialista em TI | 40 anos de carreira | 25 anos como Gerente de TI | Projeto SAP S/4 | Varejo | Empresário
Um dicionário de dados completo e bem estruturado ajudará muito nesta fase do projeto. Infelizmente não é uma técnica amplamente utilizada nas organizações. Um dicionário bem feito irá equalizar os conceitos e dar sentido único aos dados que compõe a base de dados.

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2 Mapping Plan

Once you've identified the issues, develop a data mapping plan. This involves creating a blueprint that outlines how data from different sources will be combined. Your plan should include field mappings, transformations, and data cleansing requirements. Think of it as a roadmap for your data's journey, ensuring that no piece of information gets lost or misdirected along the way. This strategic approach is key to aligning disparate data formats and structures.

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José OSCAR Schmidt Alves Brochado

Especialista em TI | 40 anos de carreira | 25 anos como Gerente de TI | Projeto SAP S/4 | Varejo | Empresário
Identificada as divergências, o tratamento de equalizá-las deve ser trabalho que envolva todas as áreas e usuários relacionados ao dados incosistente. A primeira tarefa é mapear o impacto que o dado representa na organização, levantando quem o utiliza, se ele é utilizado para a obtenção de outros dados, se seu uso se restringe ao ambiente interno da empresa ou se é divulgado de alguma forma a terceiros. Um mapa completo do impacto é necessário antes de qualquer ação para a correção do dado.

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3 Standardize Data

Standardization is your next step. It's about converting all your data to a common format and applying uniform data entry standards. This could involve date formats, measurement units, or even how names are capitalized. Imagine you're unifying different dialects into one common language so that every piece of data can be understood and used without confusion. This uniformity is essential for seamless integration.

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4 Cleanse Data

Data cleansing is an integral part of ensuring high-quality data integration. This process involves correcting or removing incorrect, incomplete, or irrelevant data. Think of it as a spa treatment for your data, where you're scrubbing away the impurities to reveal a cleaner, more reliable dataset. By doing this, you prevent the transfer of any "dirty" data into your newly merged system.

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5 Integration Tests

Before finalizing the integration, conduct rigorous testing. This includes unit tests, system tests, and user acceptance tests to ensure that the data flows correctly and maintains its integrity. Picture this as a dress rehearsal for your data, where any last-minute adjustments can be made before the big debut. Testing not only helps in catching errors early but also in building confidence that your integrated systems will work as intended.

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6 Monitor Post-Merge

Finally, don't forget to monitor your data post-merge. Keep an eye out for any anomalies or issues that might arise after the systems have been combined. This ongoing vigilance is like a guardian watching over your data, ready to act at the first sign of trouble. It ensures that the integrity of your data is maintained long after the initial integration has been completed.

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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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You're facing data quality discrepancies in system mergers. How do you ensure seamless integration?

1

2

3

4

5

6

7

1 Data Audit

2 Mapping Plan

3 Standardize Data

4 Cleanse Data

5 Integration Tests

6 Monitor Post-Merge

7 Here’s what else to consider

Data Management

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You're facing data quality discrepancies in system mergers. How do you ensure seamless integration?

1

2

3

4

5

6

7

1 Data Audit

2 Mapping Plan

3 Standardize Data

4 Cleanse Data

5 Integration Tests

6 Monitor Post-Merge

7 Here’s what else to consider

Data Management

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