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How can you create a data classification scheme to protect sensitive data in your warehouse?

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Data is one of the most valuable assets of any organization, but it also comes with risks and responsibilities. If you are managing a data warehouse, you need to ensure that you protect the sensitive data that you store, process, and share. One of the best practices to do this is to create a data classification scheme, which is a system of categorizing data based on its level of sensitivity, confidentiality, and impact. In this article, you will learn how to create a data classification scheme for your data warehouse and why it is important for data security and governance.

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Divya Jyoti

Architect at NN | Diversity and Equity ambassador | Ex-Microsoft | Ex-Oracle | TOGAF | Microsoft Certified Azure…
Marc Beierschoder

AI & Data Leader at Deloitte | Driving Transformation with Cutting-Edge Solutions | Boosting Business Outcomes in…

1 Data classification levels

When creating a data classification scheme, it is essential to define the different levels of data sensitivity for your data warehouse. While there is no universal standard, you can use common examples as a reference, such as public data that is openly available, internal data that is intended for internal use only, and restricted data that requires strict access control and encryption. Additionally, you may add more levels or sublevels depending on your specific needs and regulations. Ultimately, the goal should be to create a comprehensible hierarchy of data sensitivity that accurately reflects the value and risk of your data.

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Marc Beierschoder

AI & Data Leader at Deloitte | Driving Transformation with Cutting-Edge Solutions | Boosting Business Outcomes in Switzerland | Recognized Global Tech Influencer
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The best practice for establishing data classification levels in a data warehouse lies in developing a clear, comprehensive policy that is well-integrated into your organization's culture and operations, supported by technology, and continually refined in line with evolving data landscapes.

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2 Data classification criteria

To create a data classification scheme, you need to establish the criteria for assigning data levels to your data warehouse. This can involve a combination of factors such as data source, content, and usage. For example, external data from third parties may have different contractual or legal obligations than internal data from your own systems. Additionally, data that contains personal information, financial transactions, or intellectual property may have higher sensitivity than data that contains general statistics, trends, or insights. Moreover, data that is used for decision making or analytics may have different sensitivity than data that is used for testing or development. It's important to create a logical and objective framework to classify your data accurately and consistently according to relevant business context and industry standards.

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Marc Beierschoder

AI & Data Leader at Deloitte | Driving Transformation with Cutting-Edge Solutions | Boosting Business Outcomes in Switzerland | Recognized Global Tech Influencer
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Best practice for defining data classification criteria involves creating a clear, objective framework that considers the data source, content sensitivity, and usage context. Classify data based on its risk of exposure—personal, financial, and intellectual data typically demand higher sensitivity levels. Consider legal obligations and access needs, ensuring consistency across all data types. Regularly update the framework to reflect changing regulations and business requirements.

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3 Data classification process

The final step to create a data classification scheme is to implement the process of applying the data levels to your data warehouse. You can choose from different methods, such as manual, automated, or hybrid. Manual reviews and labels each data element or dataset according to the criteria, but it can be time-consuming and error-prone. Automated tools or scripts scan and tag your data based on rules or algorithms, but it can be challenging to design and maintain. A hybrid approach combines manual and automated methods for optimizing the process. Other methods can also be used depending on your architecture and workflow. Ultimately, the goal is to create a reliable and efficient process that helps you classify your data accurately and quickly.

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Marc Beierschoder

AI & Data Leader at Deloitte | Driving Transformation with Cutting-Edge Solutions | Boosting Business Outcomes in Switzerland | Recognized Global Tech Influencer
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Begin by defining clear, rule-based criteria that automated tools can reliably apply for the majority of data. Use manual oversight to handle exceptions, complex cases, and to review automated classifications for accuracy. Ensure that the process is integrated into your existing data workflows and architectures to enhance efficiency. Regularly update and maintain your classification tools and processes to keep pace with data evolution, and continually train staff involved in manual classification to minimize errors. The goal is to establish a scalable, sustainable, and accurate classification system that keeps classification up-to-date with minimal delays.

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4 Data classification benefits

Creating a data classification scheme for your data warehouse has many benefits for data security and governance, such as improved data protection, compliance, quality, and value. With a clear and standardized data classification scheme, you can apply appropriate security measures and controls to your sensitive data, ensure compliance with relevant laws, improve the quality and consistency of your data, and maximize the value and potential of your data. However, creating a data classification scheme is not a one-time task. It requires regular review and update. By following the steps and best practices outlined in this article, you can create a data classification scheme that helps you protect, manage, and use your data effectively and responsibly.

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5 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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Divya Jyoti

Architect at NN | Diversity and Equity ambassador | Ex-Microsoft | Ex-Oracle | TOGAF | Microsoft Certified Azure Solution Architect Expert | PRINCE2 | SAFe? 5 Agilist | Digital Transformation & Cloud | Architecture
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Maintaining a good data lineage document also takes off some effort of manual classification. Once some data is classified as 'sensitive', at the time of extraction it can still be traced as sensitive after transformation. This takes off effort of reclassification after each transformation.

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How can you create a data classification scheme to protect sensitive data in your warehouse?

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1 Data classification levels

2 Data classification criteria

3 Data classification process

4 Data classification benefits

5 Here’s what else to consider

Data Warehousing

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How can you create a data classification scheme to protect sensitive data in your warehouse?

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1 Data classification levels

2 Data classification criteria

3 Data classification process

4 Data classification benefits

5 Here’s what else to consider

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