What is essential to ensure raw data can be accelerated by a Data Model after ingestion?

To ensure that raw data can be accelerated by a Data Model after ingestion, normalization to the Splunk Common Information Model (CIM) is essential. The CIM provides a consistent framework for representing events across different data sources, which allows for better integration and understanding of the data within Splunk.

When data is normalized to the CIM, it follows standardized naming conventions and field structures. This uniformity is crucial for the Data Models to function correctly, as they rely on specific fields and structures to aggregate and analyze data effectively. By adhering to the CIM, Data Models can automatically recognize and utilize the relevant fields in the data, allowing for accelerated searches, better performance, and enhanced reporting capabilities.

The other options, while they may contribute to the overall usability or organization of the data within Splunk, do not directly impact the ability of the Data Model to accelerate the data. Normalization to customer standards or extracting fields, for example, might improve specific search queries or data clarity but does not ensure the necessary structure and compatibility with Data Models and the CIM framework. Additionally, applying tags can aid in search and categorization but is also not directly tied to the acceleration of Data Models. Thus, normalization to the Splunk Common Information Model is fundamental for maximizing the efficiency