To scale AI, organizations need to share data across the enterprise; however, they also need to have some amount of control over it. This puts them in a dilemma about which data management strategy to pursue: centralized or federated. But the answer is not so simple. The view of industry experts is that while centralized data management can poland whatsapp number data improve profits, so can a fully federated data management strategy. A combination “hub and spoke” strategy, where the organization centralizes the platform and technology but allows the teams operational autonomy, seems to offer the best of both worlds.
In Closing:
Data gives life to AI. But AI can return the favor by mitigating two key challenges confronting the data economy: silos, and lack of transparency in how personal data is used. AI tools draw on disparate information sources across an enterprise (or ecosystem), structure and format data so it is usable, and counteract silos by making the data visible, and its insights available, to all parts of the organization. This sets off a virtuous cycle where the breaking down of silos improves the performance of AI, which helps to lower the barriers between the organization’s data even further. With scale, this cycle becomes bigger and better. Hence, for enterprises, the way forward is to prioritize the right AI initiatives, become data-ready for AI, build governance and trust, provide leadership and talent, and adopt good data practices.