登录查看更多内容

What steps can you take to ensure accurate data migration in banking?

由人工智能和领英社区提供技术支持

Data migration is the process of transferring data from one system to another, often as part of a digital transformation or merger in banking. It can be challenging, costly, and risky, especially if the data is sensitive, complex, or voluminous. However, with proper planning, preparation, and execution, you can ensure accurate data migration in banking and avoid common pitfalls. Here are some steps you can take to achieve this goal.

在这篇协作文章中查找专家回答

由社区从 3 条内容中精选。了解更多

1 Assess your data quality

Before you migrate your data, you need to assess its quality and identify any issues that might affect its accuracy, completeness, or consistency. For example, you might have duplicate, outdated, or missing data, or data that does not match the format or standards of the target system. You can use data quality tools or techniques to perform data profiling, cleansing, validation, and enrichment, and to document your data sources, structures, and definitions. This will help you improve your data quality and reduce the risk of errors or anomalies during migration.

添加您的观点

Mazen Trabulsi

Enterprise Architect | Digital Transformation Evangelist | Cloud Champion | Strategic Planning | Automation | Innovation | PDO | Data Architect , Engineer ,Analyst | Financial Services | MTS | Fintech
举报内容
The main rule that applies to Data is :"Garbage IN is Garbage OUT". Data Cleaning, Preparation and Structuring. To ensure Quality, The Engineer needs to address source data type(raw/text, Semi-structured or structured), size and nature (database, and Transmit medium(ODBC, DBLink, ETL,SSIS, Extract and Import, Backup and Restore..etc.) .Redundant data ,fragmented or bad data must be cleaned and validated involving Business stakeholders or data source providers. Reviewing data and running validation/verification scripts and applying strict filtration is vital to the process

已翻译

赞

2 Define your data mapping and transformation rules

Data mapping and transformation are the processes of converting your data from the source system to the target system, according to predefined rules and specifications. You need to define these rules and specifications clearly and accurately, based on the business requirements, the data quality assessment, and the target system architecture and design. You also need to consider any data dependencies, relationships, or hierarchies that might affect the migration. You can use data mapping and transformation tools or methods to facilitate this process and to test and verify the results.

添加您的观点

Mazen Trabulsi

Enterprise Architect | Digital Transformation Evangelist | Cloud Champion | Strategic Planning | Automation | Innovation | PDO | Data Architect , Engineer ,Analyst | Financial Services | MTS | Fintech
举报内容
Rule in transformation is LAVOISIER law of conservation: "Nothing is lost, Nothing is created, everything is transformed". Accordingly, all rows from source must be found in destination with the specified Column mappings and value transformation predefined and applied . Data reconciliation and validation process must be run to verify and ensure preservation of provided data. QA and review User acceptance may be invoked to add another layer of accuracy and SME involvement. Automating the ETL/ELT or migration packages would help rerunning and retesting until optimizing the procedure

已翻译

赞

3 Choose your data migration strategy and method

Data migration can be performed in different ways, depending on the scope, complexity, and timeline of your project. You need to choose a data migration strategy and method that suits your needs and objectives, and that minimizes the impact on your operations and customers. For example, you might opt for a big bang, phased, or hybrid approach, and use a direct load, backup and restore, or replication method. You also need to select the appropriate data migration tools or platforms that support your strategy and method, and that offer features such as automation, security, monitoring, and reporting.

添加您的观点

4 Prepare your data migration environment and team

Before you execute your data migration, you need to prepare your data migration environment and team. This involves setting up the hardware, software, network, and storage resources that you will use for the migration, and ensuring that they are compatible, reliable, and scalable. You also need to assign roles and responsibilities to your data migration team, which might include data analysts, developers, testers, project managers, and business users. You need to train and communicate with your team, and establish clear guidelines, protocols, and expectations for the migration.

添加您的观点

Mazen Trabulsi

Enterprise Architect | Digital Transformation Evangelist | Cloud Champion | Strategic Planning | Automation | Innovation | PDO | Data Architect , Engineer ,Analyst | Financial Services | MTS | Fintech
举报内容
Allocating a team in a Test environment with valid data freshly refreshed from production would give a clean and identical results in simulation.Qualified team with previous experience is necessary to identify gaps and consider extreme possibilities and unexpected exception in test scenarios. Storage and computing processing power should be allocated. Every iteration needs a data purging process ready with proper masking or encryption for sensitive data or shuffling critical values. Special attention to encryption keys on TEST/UAT . Segregating duties and rotating roles between micro-teams gives best results if resources are allocated. Aligning with project timeline involved PMO office & Data engineers to jump and apply desired modification

已翻译

赞

5 Perform your data migration testing and validation

Data migration testing and validation are the processes of verifying that your data migration is successful, accurate, and complete. You need to perform various types of testing and validation throughout the migration lifecycle, such as unit testing, integration testing, system testing, user acceptance testing, and post-migration testing. You need to use data testing and validation tools or procedures to compare your source and target data, identify and resolve any discrepancies or errors, and measure and report the quality and performance of your migration.

添加您的观点

6 Monitor and manage your data migration project

Data migration is a complex and dynamic project that requires constant monitoring and management. You need to track and control the progress, status, and outcomes of your migration, and ensure that they align with your scope, budget, and schedule. You also need to manage the risks, issues, and changes that might arise during the migration, and implement contingency plans or corrective actions if needed. You need to use data migration project management tools or frameworks to facilitate this process and to communicate and collaborate with your stakeholders and team members.

添加您的观点

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?

添加您的观点

Banking Relationships

+ 关注

给文章评分

我们借助人工智能创建了此文章。您认为这篇文章怎么样？

很棒不太好

举报此文章

查看全部

What steps can you take to ensure accurate data migration in banking?

1

2

3

4

5

6

7

1 Assess your data quality

2 Define your data mapping and transformation rules

3 Choose your data migration strategy and method

4 Prepare your data migration environment and team

5 Perform your data migration testing and validation

6 Monitor and manage your data migration project

7 Here’s what else to consider

Banking Relationships

给文章评分

感谢您的反馈

更多Banking Relationships相关文章