How can you secure data in BI?

1 Identify data sources and data types

The first step to secure data in BI is to identify the data sources and data types that you will use in your BI project. Data sources can be internal or external, structured or unstructured, and can vary in quality, reliability, and availability. Data types can be personal, confidential, public, or regulated, and can have different levels of sensitivity, privacy, and legal implications. By identifying the data sources and data types, you can determine the data security requirements, such as encryption, authentication, authorization, backup, and audit.

2 Implement data governance and data quality

The second step to secure data in BI is to implement data governance and data quality processes and policies. Data governance is the set of rules, roles, and responsibilities that define how data is collected, stored, accessed, used, and shared in an organization. Data quality is the measure of how accurate, complete, consistent, and relevant data is for its intended purpose. By implementing data governance and data quality, you can ensure that data is trustworthy, reliable, and compliant with internal and external standards and regulations.

3 Use data security tools and techniques

The third step to secure data in BI is to use data security tools and techniques that can protect data from unauthorized access, modification, or disclosure. This can include data encryption, which is the process of transforming data into a code that can only be read by authorized parties with a key. Additionally, data masking and anonymization are effective methods for hiding or replacing sensitive data elements with fictitious or anonymized data, as well as removing or modifying identifying information from data sets. Data access control is also important for granting or denying access to data based on user roles, permissions, and policies. Lastly, data audit and monitoring can help track and log data activities and events, as well as detect and respond to anomalies or incidents.

4 Train and educate data users and stakeholders

The fourth step to secure data in BI is to train and educate data users and stakeholders on the importance of data security and the best practices and standards to follow. Data users and stakeholders can include data analysts, data scientists, data engineers, data managers, business users, executives, customers, and partners. By training and educating data users and stakeholders, you can raise awareness, foster a culture of data security, and prevent human errors or negligence that can compromise data security.

5 Review and update data security policies and practices

The fifth step to secure data in BI is to review and update data security policies and practices regularly and in response to changes in data sources, data types, data needs, data regulations, and data threats. Data security policies and practices can include data security goals, objectives, strategies, plans, procedures, guidelines, and standards. By reviewing and updating data security policies and practices, you can ensure that data security is aligned with the current and future needs and expectations of your organization and your stakeholders.

6 Test and evaluate data security performance and effectiveness

The sixth and final step to secure data in BI is to test and evaluate data security performance and effectiveness. Testing and evaluation can include conducting data security audits, assessments, reviews, simulations, and validations, and measuring data security metrics, indicators, outcomes, and impacts. By testing and evaluating data security performance and effectiveness, you can identify data security strengths, weaknesses, opportunities, and threats, and take corrective or preventive actions to improve data security.

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?