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How do you ensure data quality across multiple platforms?

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Data quality is the degree to which data meets the expectations and requirements of its users and stakeholders. Data quality can be affected by various factors, such as errors, inconsistencies, incompleteness, duplication, or outdatedness. When working with multiple platforms, such as databases, applications, or web services, data quality can become even more challenging to ensure and maintain. In this article, you will learn some practical tips and best practices to help you ensure data quality across multiple platforms.

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Hisham Elhadidi

Supply Chain | Multi-Model Logistics | Transportation | Digital Transformation | Operations
Isabel Carchi, MBA

Financial Advisor

1 Define data quality standards and metrics

The first step to ensure data quality across multiple platforms is to define what data quality means for your specific context and goals. You need to establish clear and measurable standards and metrics for data quality, such as accuracy, completeness, consistency, timeliness, validity, or relevance. You also need to identify the sources, formats, and types of data that you are working with, and how they relate to each other across different platforms. By defining data quality standards and metrics, you can set the expectations and criteria for data quality assessment and improvement.

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Hisham Elhadidi

Supply Chain | Multi-Model Logistics | Transportation | Digital Transformation | Operations
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One very important aspect of data quality is the data relevancy and relatedness to the topic or the subject of analysis , it is very critical that irrelevant data to be discarded so not to deform the results or shift the analysis into wrong direction. (example: population census data can be relevant when studying economics or marketing trends , but it can be off topic and may cause result distortion when analyzing students academic levels across different countries)

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2 Implement data quality checks and validation

The second step to ensure data quality across multiple platforms is to implement data quality checks and validation processes throughout the data lifecycle. You need to check and validate the data at different stages, such as data collection, data integration, data transformation, data analysis, or data reporting. You can use various tools and methods to perform data quality checks and validation, such as data profiling, data cleansing, data matching, data auditing, or data testing. By implementing data quality checks and validation, you can identify and correct data quality issues before they affect your results and decisions.

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Isabel Carchi, MBA

Financial Advisor
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I agree with this last paragraph. I would add or emphasize that this is a step that MUST not be skipped, even after validating processes and procedures, because it takes much less time than after the fact.

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3 Monitor and report data quality issues

The third step to ensure data quality across multiple platforms is to monitor and report data quality issues on a regular basis. You need to track and measure the data quality performance and trends across different platforms, and compare them with your data quality standards and metrics. You also need to report and communicate the data quality issues and their impacts to the relevant stakeholders, such as data owners, data users, or data managers. By monitoring and reporting data quality issues, you can raise awareness and accountability for data quality improvement and maintenance.

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4 Automate and standardize data quality processes

The fourth step to ensure data quality across multiple platforms is to automate and standardize data quality processes as much as possible. You need to use tools and technologies that can help you automate and standardize the data quality checks and validation, the data quality monitoring and reporting, and the data quality correction and enhancement. You also need to establish and follow data quality policies and procedures that can help you align and coordinate the data quality activities and responsibilities across different platforms. By automating and standardizing data quality processes, you can increase the efficiency and consistency of data quality management.

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5 Train and educate data quality stakeholders

The fifth step to ensure data quality across multiple platforms is to train and educate the data quality stakeholders on the importance and best practices of data quality. You need to provide the data quality stakeholders with the necessary skills and knowledge to perform their data quality roles and tasks effectively and efficiently. You also need to foster a data quality culture that encourages the data quality stakeholders to share their feedback, insights, and suggestions for data quality improvement and innovation. By training and educating data quality stakeholders, you can enhance the data quality awareness and competence across different platforms.

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6 Review and update data quality standards and metrics

The sixth step to ensure data quality across multiple platforms is to review and update your data quality standards and metrics periodically. You need to evaluate the effectiveness and relevance of your data quality standards and metrics, and make adjustments as needed based on your changing data needs and goals. You also need to consider the feedback and input from your data quality stakeholders, and incorporate their perspectives and expectations into your data quality standards and metrics. By reviewing and updating your data quality standards and metrics, you can ensure that they reflect the current and future state of your data quality across multiple platforms.

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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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How do you ensure data quality across multiple platforms?

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3

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5

6

7

1 Define data quality standards and metrics

2 Implement data quality checks and validation

3 Monitor and report data quality issues

4 Automate and standardize data quality processes

5 Train and educate data quality stakeholders

6 Review and update data quality standards and metrics

7 Here’s what else to consider

Analytical Skills

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How do you ensure data quality across multiple platforms?

1

2

3

4

5

6

7

1 Define data quality standards and metrics

2 Implement data quality checks and validation

3 Monitor and report data quality issues

4 Automate and standardize data quality processes

5 Train and educate data quality stakeholders

6 Review and update data quality standards and metrics

7 Here’s what else to consider

Analytical Skills

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