What makes data security metrics difficult to collect and analyze across different systems?

1 Data security definitions

One of the first difficulties is to define what data security means and how to measure it. Different systems may have different criteria, standards, and frameworks for data security, such as encryption, access control, compliance, or breach detection. Therefore, you need to establish a common definition and a set of metrics that are relevant, consistent, and aligned with your data governance goals and policies.

2 Data security sources

Another challenge is to identify and access the sources of data security metrics. These sources may include logs, reports, audits, surveys, or incidents from different systems, platforms, or vendors. You need to ensure that these sources are reliable, accurate, and up-to-date, and that you have the necessary permissions and tools to collect the data. You also need to consider the volume, variety, and velocity of the data, and how to store and manage it efficiently.

3 Data security analysis

A third difficulty is to analyze the data security metrics and derive meaningful insights. You need to use appropriate methods, techniques, and tools to process, visualize, and interpret the data, such as statistics, dashboards, or machine learning. You need to account for the complexity, diversity, and interdependence of the data, and avoid biases, errors, or gaps. You also need to communicate the results clearly and effectively to the relevant stakeholders, such as managers, auditors, or regulators.

4 Data security improvement

A final challenge is to use the data security metrics to improve the data security performance and maturity across different systems. You need to establish a feedback loop that allows you to monitor, evaluate, and adjust the data security metrics, processes, and policies based on the analysis results. You need to implement best practices, standards, and guidelines to ensure data security quality, consistency, and compliance. You also need to foster a data security culture that promotes awareness, collaboration, and accountability among the data users and owners.

Data security metrics can provide valuable information and guidance for enhancing the data security posture and resilience across different systems. However, collecting and analyzing these metrics can be difficult due to various factors. By addressing these challenges, you can improve your data governance capabilities and outcomes.

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?