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You're facing time constraints in a data migration project. How can you ensure top-notch data quality checks?

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In data migration, time constraints can seriously jeopardize the quality of data being transferred. Ensuring the integrity, accuracy, and completeness of your data under such pressure is a challenge, but with a strategic approach, you can maintain top-notch quality checks. Data migration isn't just about moving data from one system to another; it's about transferring the value that data represents. Therefore, it's essential to prioritize data quality, even when the clock is ticking.

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1 Plan Ahead

Planning is crucial when you're racing against time in a data migration project. Before the actual migration, you should map out all data sources and establish clear data quality benchmarks. This step allows you to identify potential issues early and set realistic timelines for each phase of the migration. By understanding the scope and complexity of the data, you can allocate resources effectively and avoid last-minute rushes that compromise data quality.

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Mohamed Riyaz

Data Architect | Solution Architect | Data Modeler | Data Engineer | Bigdata | STC | Teradata | CDMP? | TOGAF?
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1. Scope Definition and Prioritization: o Start by clearly defining the scope of your data migration project. What data needs to be moved? What are the boundaries? o Split the project into manageable chunks—think of them as your Minimum Viable Products (MVPs). Prioritize these based on business needs. Not all data has equal urgency!

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2 Automate Checks

Automation is your ally in ensuring data quality under tight deadlines. By using specialized data migration tools, you can automate many of the repetitive tasks involved in data quality checks. These tools can validate data against predefined rules, flag inconsistencies, and even correct common errors without human intervention. This not only speeds up the process but also reduces the likelihood of human error, which is crucial when time is of the essence.

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3 Prioritize Data

Not all data is created equal. In a time-sensitive project, you must prioritize which data sets are most critical to your business operations and customer experience. By focusing your efforts on the most valuable data, you can conduct thorough quality checks where they matter most. This approach ensures that even if some less critical data isn't scrutinized to the same extent, the core functionality and service quality remain intact.

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Mohamed Riyaz

Data Architect | Solution Architect | Data Modeler | Data Engineer | Bigdata | STC | Teradata | CDMP? | TOGAF?
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4. Data Quality (DQ) Rules: o Identify the DQ rules you’ll apply to your data. These rules ensure data integrity. o Automate where possible—use DQ tools or in-house solutions. o Focus on critical data—what could seriously impact the business if it’s incorrect?

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4 Validate Continuously

Continuous validation throughout the migration process helps catch errors as they occur, rather than at the end when time may not permit comprehensive corrections. Implement a system of checkpoints where data quality is assessed at various stages of the migration. This iterative approach allows for incremental improvements and ensures that any discrepancies are addressed promptly, safeguarding the integrity of the migrated data.

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5 Engage Stakeholders

Involving stakeholders from different departments can provide additional insight into data quality requirements. They can help identify which data elements are most important for their specific operations and contribute to setting accurate data quality standards. Engaging stakeholders early also ensures that everyone understands the time constraints and the importance of maintaining data quality, fostering a collaborative effort towards the project's success.

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Mohamed Riyaz

Data Architect | Solution Architect | Data Modeler | Data Engineer | Bigdata | STC | Teradata | CDMP? | TOGAF?
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3. Communication Is Key: o Set up a clear communication plan. Regularly update stakeholders on progress. o Be transparent about any issues you encounter. No one likes surprises!

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6 Monitor Post-Migration

After the migration is complete, it's vital to continue monitoring data quality. Post-migration checks can uncover issues that weren't apparent during the transfer process. Establishing a routine for regular audits and reviews of the migrated data helps maintain its quality over time. This ongoing vigilance is key to ensuring that the data continues to serve its intended purpose effectively and accurately.

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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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Madhan Jayaprakash

Director - Quality Engineering | Build a strategical, reliable and scalable solutions to systems end-to-end business process testing | Certified in Scaled Agile
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Continuous Replanning and communication: One of the crucial item is replanning along the way when the progress and schedule deviates which occurs in many cases. The replanning should comprise of the timeline shift, burn-down / burn-up chart adjustment to measure the remaining work, re-define and prioritize the scope for a MVP (Minimum viable product). Doing so will help to focus on the quality check of the module/ business process that has the most impactful (customer experience or financial exposure).

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Mohamed Riyaz

Data Architect | Solution Architect | Data Modeler | Data Engineer | Bigdata | STC | Teradata | CDMP? | TOGAF?
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o Involve all relevant stakeholders. Their buy-in is crucial for success. o Explain your approach—why these steps matter. Get them on board!

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You're facing time constraints in a data migration project. How can you ensure top-notch data quality checks?

1

2

3

4

5

6

7

1 Plan Ahead

2 Automate Checks

3 Prioritize Data

4 Validate Continuously

5 Engage Stakeholders

6 Monitor Post-Migration

7 Here’s what else to consider

Data Architecture

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You're facing time constraints in a data migration project. How can you ensure top-notch data quality checks?

1

2

3

4

5

6

7

1 Plan Ahead

2 Automate Checks

3 Prioritize Data

4 Validate Continuously

5 Engage Stakeholders

6 Monitor Post-Migration

7 Here’s what else to consider

Data Architecture

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