From Customer Data to Smart Customer Data: The Smart Data Transformation Process
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Nowadays, smart data has emerged as a new trend in creating more business value for enterprises that is defined as the data that is gathered and processed to create new insights to support business decisions. However, the transformation from data into actionable insights is still a real challenge for enterprises. For this reason, this paper presents a smart data transformation process, which aims at transforming customer data into smart customer data in order to offer actionable insights. The purpose of the study is to propose a transformation process that can be used to operate a knowledge structure for a smart service system, which can manage and deliver smart data as a service. The process covers the three dimensions of a service system: Data processing corresponding to the engineering dimension, information processing corresponding to the science dimension, and knowledge processing corresponding to the management dimension for knowledge processing. Accordingly, a case study on the smart data transformation process of a customer journey management system as a smart service system is presented to demonstrate the application of the proposed process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it