Framework para ciência de dados no contexto de pequenas e médias empresas brasileiras
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».