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How can you use a data masking tool to enhance data governance?

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Data masking is a technique that transforms sensitive or confidential data into anonymized or pseudonymized versions, without compromising its usability or quality. Data masking can help you enhance your data governance by protecting your data assets, complying with data privacy regulations, and enabling data sharing and collaboration. In this article, you will learn how to use a data masking tool to implement data masking in your data architecture.

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1 What is a data masking tool?

A data masking tool is a software application that automates the process of applying data masking rules and policies to your data sources, such as databases, files, or streams. A data masking tool can also provide features such as data discovery, data classification, data quality, data lineage, and data audit. A data masking tool can integrate with your existing data infrastructure and tools, such as ETL, BI, or cloud platforms.

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2 Why do you need a data masking tool?

A data masking tool can help you address some of the common challenges and benefits of data masking, such as preventing data breaches and data leakage, complying with data privacy regulations, and enabling data sharing and collaboration. It can do this by replacing sensitive or confidential data with realistic but fictitious data that preserves its structure and format. This ensures that your data processing and storage meets the requirements of data minimization, consent, and anonymization. Furthermore, it provides stakeholders with consistent and reliable masked data sets that do not expose any personal or sensitive information.

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Harinath Sukumaran

Data & AI Governance | Management Consulting | Data Analytics | Data Strategist | Data Privacy | Data Security | Data Quality | Ex-EY | Data Science | MDM | Python | Data Architecture | Data Management |
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A data masking tool enhances data governance by protecting sensitive information during testing or analysis. It replaces or anonymizes sensitive data, ensuring privacy compliance and maintaining data utility. This safeguards confidential information, aligning with data governance principles and regulatory requirements.

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3 How do you choose a data masking tool?

When selecting a data masking tool for your data architecture, you need to consider a variety of factors, such as the types and formats of data sources you want to mask, the level and type of data masking you want to apply, the rules and criteria for identifying and masking sensitive or confidential data, and the results and impacts of data masking on data quality, usability, and performance. For example, you may need to determine if the data masking tool supports static or dynamic masking, deterministic or random masking, encryption or substitution masking, and if it can monitor and report on data accuracy, consistency, validity, or latency.

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Michael Thieme

Passion for shaping trusted data products
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Typical challenges in the use of #datamasking tools are the complexity of the data, compliance with data protection regulations and ensuring data quality. At the same time, it is particularly important that the masked data remains meaningful for analysis purposes. Approaches include the implementation of flexible masking algorithms that can take into account different data types and structures. The tools should also offer #anonymisation and #pseudonymisation functions to ensure #dataprotection. Monitoring #dataquality is also crucial to ensure that the masked data is suitable for analyses. In addition, close collaboration with data protection experts and #compliance teams is required to ensure that all legal requirements are met.

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4 How do you use a data masking tool?

A data masking tool can simplify and streamline the data masking process by performing various steps. This includes scanning and analyzing data sources to identify sensitive or confidential information, such as names, emails, phone numbers, SSNs, etc. The tool can then categorize and label the data elements based on their sensitivity level and assign them to data domains. Additionally, the tool can apply the chosen masking techniques and policies to transform the data elements into masked data that retains its original format and structure but has no real meaning or value. Finally, the tool can be used to verify that the masking has been executed correctly and that the masked data meets your data quality and security standards.

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5 What are some best practices for using a data masking tool?

A data masking tool can help you enhance your data governance, but for successful and sustainable data masking, you need to maintain an updated inventory of your data sources, track the origin and transformation of your data, audit your data masking performance and results, and test and evaluate your masked data sets. Documenting their data masking policies and techniques, recording their data masking activities and outcomes, reporting any issues or anomalies that may affect your data quality, usability or compliance, and ensuring that the masked data sets meet your functional and non-functional requirements are all important steps to take.

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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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How can you use a data masking tool to enhance data governance?

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1 What is a data masking tool?

2 Why do you need a data masking tool?

3 How do you choose a data masking tool?

4 How do you use a data masking tool?

5 What are some best practices for using a data masking tool?

6 Here’s what else to consider

Data Architecture

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How can you use a data masking tool to enhance data governance?

1

2

3

4

5

6

1 What is a data masking tool?

2 Why do you need a data masking tool?

3 How do you choose a data masking tool?

4 How do you use a data masking tool?

5 What are some best practices for using a data masking tool?

6 Here’s what else to consider

Data Architecture

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