Initiate Intra- and Interorganizational Data Democratization new
Overview
| Sustainability Dimension | Governance |
| ML Development Phase | Data Collection and Preparation |
| ML Development Stakeholders | Business Stakeholder |
Description
“Initiate Intra- and Interorganizational Data Democratization” should be facilitated, as ML models are developed based on domain-specific knowledge. Therefore, the access and competencies to understand data are crucial for successful ML implementation (van Giffen & Ludwig, 2023). Democratization can occur within and outside an organization. For the former, companies need to issue policies and foster data exchange within the organization to facilitate business value generation (Harvard Business Review Analytics Service, 2020). The second refers to open data exchange. By participating in international data exchange for non-critical data, an organization can contribute to and benefit from ML research interorganizational (De Saulles, 2020; Elgarah et al., 2005).
Sources
- van Giffen, B., & Ludwig, H. (2023). How siemens democratized artificial intelligence. MIS Quarterly Executive, 22(1), 3.
- Harvard Business Review Analytics Service. (2020). Turning data into unmatched business value. https://services.google.com/fh/files/blogs/hbr-turn-data-into-business-value-report.pdf
- De Saulles, M. (2020). Data Liquidity: Data Exchange Platforms as Drivers of Innovation. https://doi.org/10.13140/RG.2.2.20887.93603
- Elgarah, W., Falaleeva, N., Saunders, C. C., Ilie, V., Shim, J. T., & Courtney, James. F. (2005). Data exchange in interorganizational relationships: Review through multiple conceptual lenses. ACM SIGMIS Database: The DATABASE for Advances in Information Systems, 36(1), 8–29. https://doi.org/10.1145/1047070.1047073