Framework for data science in the context of small and medium-sized brazilian enterprises
Bibliographic record
Abstract
Small and Medium Enterprises (SMEs) are responsible for a considerable market share in emerging or developed economies. In China, it comprises around 75% of the workforce, while in Canada and European Union countries, this rate reaches 64% and 67%, respectively, while in Brazil, around 78% of jobs are generated only in Micro and Small businesses. However, economic representation does not reflect the adoption of Information Technologies (IT) for SMEs, where diffusion is estimated between 7% and 33% in this type of business. At the same time, it is around 77% for large companies. Issues such as IT maturity level, lack of investment, and technical capabilities often limit the use of such technologies to large companies or startups born in a digital environment. At the same time, SMEs still need to catch up on the sidelines of a rapidly growing market. Given the relevance of incorporating disruptive technologies and their impact on the success of organizations, this study sought to gather information through literature review and field research, elements for proposing a Data Science Framework (DCF) in the context of Brazilian SMEs. The methodological process was based on topic modeling to create the bibliographic portfolio with the application of the Latent Dirichlet Allocation (ALD) algorithm in the context of text mining, added to market contributions through interviews with professionals working in the technology segment. And that part of its work has been in Small and Medium-Sized Brazilian Companies. Perceiving the value and adjustments of the generic FCD, interviewees agreed that the FCD could be used to guide the adoption of Data Science processes in generic companies. Improving information governance was mentioned as the point of most significant value, followed by improving process efficiency and increasing team performance. Respondents also highlighted clarity in project/product scope, decision -making, improved IT governance, and other benefits provided by the FCD. The need for qualified human capital and the low perception of value were identified as the main barriers in SMEs. Other obstacles include financial limitations, lack of company organization, organizational culture, and insufficient technological availability. As additional information, the interviewees proposed developments on systems interoperability, the FCD segmentation by company size, and the delivery of value in phases based on the company's maturity level. In summary, research has shown that the generic FCD can be applied in SMEs as a guide for structuring the data flow and improving efficiency in decision -making in SMEs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.052 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".