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What are the most effective ways to integrate migrated data with existing sources?

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Data migration is the process of moving data from one system or platform to another, often for reasons such as upgrading, consolidating, or modernizing. However, data migration can also pose challenges for data quality and integration, especially when the migrated data has to coexist with existing sources. In this article, you will learn some of the most effective ways to integrate migrated data with existing sources and ensure data consistency, accuracy, and usability.

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1 Assess the data quality

Before you start integrating migrated data with existing sources, you need to assess the data quality of both. Data quality refers to how well the data meets the requirements and expectations of the users and the business. Some common data quality dimensions are completeness, validity, accuracy, consistency, timeliness, and uniqueness. You can use various tools and techniques to measure and monitor the data quality, such as data profiling, data cleansing, data validation, and data auditing. By assessing the data quality, you can identify and resolve any issues or gaps that might affect the integration process.

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Sagar More

???? SRE Consultant??Unraveling the Unseen??Pioneering Resilient Digital Ecosystems???Empowering Scalable & Sustainable Innovation??Engineering Transformation into Tangible Growth??Inspiring Next-Gen Solutions Today??
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In the data fusion ballet, assessing quality is the prima ballerina. Envision this: before the integration pas de deux, meticulous scrutiny ensures a seamless harmony. It's not just data migration; it's a symphony where quality assessment isn't a prelude but the virtuoso performance ensuring that the ensemble of migrated and existing data resonates in perfect unison. In this choreography of information, precision isn't a choice; it's the elegant dance where data quality takes center stage, pirouetting through the complexities of integration with grace and assurance.

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2 Define the data integration strategy

Next, you need to define the data integration strategy that suits your goals and needs. Data integration is the process of combining data from different sources into a unified view or repository. There are different methods and approaches for data integration, such as extract, transform, and load (ETL), extract, load, and transform (ELT), data federation, data replication, and data virtualization. Each method has its own advantages and disadvantages, depending on factors such as the volume, variety, velocity, and veracity of the data, the frequency and timeliness of the integration, the complexity and flexibility of the transformation, and the performance and scalability of the system.

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Peter Vieveen

Lecturer Data Management
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There is no such thing as a data integration strategy. You should only have one data strategy that support the translation of the business strategy towards data solutions such as data integration solution, data analysis solutions and data registration solutions. If you take the above into account I'd prefer to separate the combining of data and the process of creating one view.

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3 Choose the data integration tools

After you have defined the data integration strategy, you need to choose the data integration tools that can support your implementation. Data integration tools are software applications that help you automate and streamline the data integration process. They can provide features such as data extraction, data transformation, data loading, data mapping, data quality management, data governance, and data analytics. There are many data integration tools available in the market, such as Informatica, Talend, IBM DataStage, Microsoft SQL Server Integration Services (SSIS), Oracle Data Integrator (ODI), and AWS Glue. You should compare and evaluate the data integration tools based on criteria such as functionality, usability, compatibility, reliability, security, and cost.

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Ashu Dhall, CDMP?

Data & AI Strategist | Practitioner | President DAMA San Francisco (Bay Area) & Austin Chapter
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Prioritize compatibility by ensuring seamless connectivity with diverse data sources, such as databases and cloud storage. Opt for scalable solutions capable of handling growing data volumes and enabling real-time streaming for timely updates. Assess tools for robust data transformation and quality management features, crucial for harmonizing and maintaining data accuracy. Consider the cost-effectiveness of the tool, factoring in licensing, maintenance, and scalability costs. Verify strong API support for integration with various applications and services. Select tools that seamlessly integrate with your existing technology stack.Additionally, prioritize solutions with active vendor support and community for ongoing assistance and updates.

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4 Test and validate the data integration

Before you deploy the data integration solution, you need to test and validate the data integration. Testing and validating the data integration involves verifying that the data is transferred correctly and completely from the source to the target, that the data is transformed according to the business rules and logic, that the data is consistent and accurate across the sources and the target, and that the data meets the quality standards and expectations. You can use various methods and techniques to test and validate the data integration, such as unit testing, integration testing, system testing, user acceptance testing, regression testing, and performance testing.

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Sandeep Raju

Data & AI Strategy & Transformation | Data Management & Architecture SME | Tech Delivery | Business Change
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Data scenarios need to be mapped to business scenarios - this forms the foundation of your test scope, as it allows you to verify the data suitability for subsequent business validation of the solution. This requires early business engagement to concretely define what the data should look like to fulfill the business scenario. It will require a level of data analysis and data model understanding to flush out the various nuances that you need to prove post migration.

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5 Monitor and maintain the data integration

Finally, you need to monitor and maintain the data integration. Monitoring and maintaining the data integration involves ensuring that the data integration solution is running smoothly and efficiently, that the data is updated and synchronized regularly and timely, that the data is secure and compliant with the policies and regulations, and that the data is accessible and usable for the users and the business. You can use various tools and techniques to monitor and maintain the data integration, such as dashboards, alerts, logs, reports, audits, backups, and recovery.

By following these steps, you can integrate migrated data with existing sources effectively and improve your data quality and integration. Data migration and integration are essential for data architecture, as they enable you to leverage your data assets and optimize your data value.

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6 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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Peter Vieveen

Lecturer Data Management
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Why start a discussion base on AI on something that is currently available in a accepted framework called DMBOK. In my lectures on the applied university of Utrecht I have asked my students to define a data architecture using ChatGPT. I have showed them that the result was a combination of every solution that is currently used somewhere in the data world. ChatGPT explained a convincing story on something that was not possible to implement. After using the best practices from DMBOK they could design a pragmatic, realistisch roadmap that was easier to explain and needed much less Resources and budget to implement.

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Sandeep Raju

Data & AI Strategy & Transformation | Data Management & Architecture SME | Tech Delivery | Business Change
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Data Management foundations are essential for a successful migration. Data Producers and Data Consumers need to be in alignment on 'the definition of good' for the data. Without this, the requirements cannot be isolated, and the migration is at risk.

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What are the most effective ways to integrate migrated data with existing sources?

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2

3

4

5

6

1 Assess the data quality

2 Define the data integration strategy

3 Choose the data integration tools

4 Test and validate the data integration

5 Monitor and maintain the data integration

6 Here’s what else to consider

Data Architecture

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What are the most effective ways to integrate migrated data with existing sources?

1

2

3

4

5

6

1 Assess the data quality

2 Define the data integration strategy

3 Choose the data integration tools

4 Test and validate the data integration

5 Monitor and maintain the data integration

6 Here’s what else to consider

Data Architecture

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