Data-Driven Decision-Making Framework for Enhancing Business Agility in Digital Enterprises
Bibliographic Data
| ID | 19490469 |
|---|---|
| Authors | P Raman (CPCL Polytechnic College), Karan Sukhija (Punjabi University), Shivani Sahu, Deepak Saroha (Chandigarh University), Rajidi Rammohan Reddy (Trinity College), G Kharmega Sundararaj, G Sundararaj (Aims Community College) |
| Year | 2026 |
| Volume | 7 |
| Issue | 11s |
| Publication date | 2026-05-22 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ShodhKosh: Journal of Visual and Performing Arts (JOURNAL) |
| Journal identifiers | ISSN: 2582-7472 • E-ISSN: 2582-7472 |
| Publisher | Granthaalayah Publications and Printers (PUBLISHER • IN) |
| DOI | 10.29121/shodhkosh.v7.i11s.2026.8285 |
| OpenAlex | W7162069699 |
| Language | EN |
| References cited | 15 |
The principle of data-driven decision-making (DDDM) has turned into one of the facilitators of the digital business environment that is characterised by a fast-changing environment. The paper offers a wide outline of the methods of making businesses more nimble by applying systematically integrated strategies with respect to data analytics, enterprise intelligence, and digital transformation. Recent literature readings, case studies and systematic review of the industry reports were conducted in order to identify the most important enabling factors, barriers and best practices in DDDM adoption. Based on the outcomes, the above-mentioned organizations whose data-driven processes were organized were 23 per cent more efficient in their work, and this is why the decision-making process was 17 per cent faster or more responsive to market changes. The framework stresses the fact that the leadership, agility of information system, knowledge management, and predictive analytics are important components that assist in optimizing the effectiveness of the decision-making processes. The insights provide an accurate blueprint to responsive businesses that are looking forward to utilizing data analytics to build nimbleness, novelty, and rivalry
Analytics · Best practice · Blueprint · Business analytics · Business process · Digital transformation · AI and HR Technologies · Big Data and Business Intelligence · Digital Transformation in Industry
| Highly cited | No |
|---|