Challenges with Data Minimization: How can Meru help?
In our last article, we talked about why Data minimization should be an integral part of an organization's privacy program. However, implementing data minimization would be considered challenging to achieve. Minimizing data can also be perceived as hindering data monetization efforts. However, implementing data minimization will improve the quality and accuracy of data, helps analytics, and goes a long way in establishing trust with customers.
Data minimization is not only about privacy, but it is about implementing efficient data management practices. The need for efficiency in managing organizational data has become increasingly important and is a necessity for maintaining a competitive advantage.
Data minimization requires good planning, connecting the dots, and working collaboratively with all relevant stakeholders across the organization. Investments in the right technology will help with scalability and implementation. The focus is always on the technology for execution. Taking a data-driven approach to planning and implementation will make data minimization simpler and effective.
Meru can help in three ways:
1. Building a detailed DataMap that can make data minimization an easy and scalable process:
A DataMap can help you identify what data exists within the organization, how it is used, and by whom. The identification can be at a system or process level.
Once the data is identified, proper classification of the data in the DataMap will help organizations understand what is important, sensitive, and confidential, thereby improving overall security. It is also important to understand if the data maintained in a system is as a system of record or just a duplicate copy of the data.
AI-powered data cataloging and classification tools can automate the classification and organization of data within the Da