Profiling Practices - The Profiling Process

The Profiling Process

The technical process of profiling can be separated in several steps:

• Preliminary grounding: The profiling process starts with a specification of the applicable problem domain and the identification of the goals of analysis.
• Data collection: The target dataset or database for analysis is formed by selecting the relevant data in the light of existing domain knowledge and data understanding.
• Data preparation: The data are preprocessed for removing noise and reducing complexity by eliminating attributes.
• Data mining: The data are analysed with the algorithm or heuristics developed to suit the data, model and goals.
• Interpretation: The mined patterns are evaluated on their relevance and validity by specialists and/or professionals in the application domain (e.g. excluding spurious correlations).
• Application: The constructed profiles are applied, e.g. to categories of persons, to test and fine-tune the algorithms.
• Institutional decision: The institution decides what actions or policies to apply to groups or individuals whose data match a relevant profile.

Data collection, preparation and mining all belong to the phase in which the profile is under construction. However, profiling also refers to the application of profiles, meaning the usage of profiles for the identification or categorization of groups or individual persons. As can be seen in step six (application), the process is circular. There is a feedback loop between the construction and the application of profiles. The interpretation of profiles can lead to the reiterant – possibly real-time – fine-tuning of specific previous steps in the profiling process. The application of profiles to people whose data were not used to construct the profile is based on data matching, which provides new data that allows for further adjustments. The process of profiling is both dynamic and adaptive. A good illustration of the dynamic and adaptive nature of profiling is the Cross-Industry Standard Process for Data Mining (CRISP-DM).