When implementing a data classification program, it is important to avoid too much granularity, because the process will require too many resources. Data classification is the process of assigning a level of sensitivity or criticality to data based on its value, impact, and legal requirements. Data classification helps to determine the appropriate security controls and handling procedures for the data. However, data classification is not a simple or straightforward process, as it involves many factors, such as the nature, context, and scope of the data, the stakeholders, the regulations, and the standards. If the data classification program has too many levels or categories of data, it will increase the complexity, cost, and time of the process, and reduce the efficiency and effectiveness of the data protection. Therefore, data classification should be done with a balance between granularity and simplicity, and follow the principle of proportionality, which means that the level of protection should be proportional to the level of risk.

The other options are not the main reasons to avoid too much granularity in data classification, but rather the potential challenges or benefits of data classification. It will be difficult to apply to both hardware and software is a challenge of data classification, as it requires consistent and compatible methods and tools for labeling and protecting data across different types of media and devices. It will be difficult to assign ownership to the data is a challenge of data classification, as it requires clear and accountable roles and responsibilities for the creation, collection, processing, and disposal of data. The process will be perceived as having value is a benefit of data classification, as it demonstrates the commitment and awareness of the organization to protect its data assets and comply with its obligations.