The Impact of Big Data on SME´s Strategic Management: A Study on a Small British Enterprise Specialized in Business Intelligence
Bibliographic record
Abstract
The present article seeks to describe how Big Data impacts on SMEs strategy, focusing both on planning and the use of strategy tools. It is a result of a participatory and practical action research in a small British Company ($2.5 Million annual turnover) specialized in business intelligence, conferences and tradeshows during 2014 to 2017. Throughout the research period, Big Data had a profound and multifaceted impact on the strategy and operations of the company, resulting in the changing of its products, adoption of new and more dynamic CRM systems, rethinking of the strategic tools utilized by the senior management and definition of new long term strategic goals. As a conclusion, it was noted that cultural predisposition to adopt Big Data technologies had a defining influence over the course of the strategic planning and operations; as the strategy for Big Data has to go beyond simply implementing technological changes – it actually has to exist before the adoption of new technologies is even considered – demanding commitment from the senior management team as well as the operational side of the business.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".