Currently, cloud services with built-in predictive analytics APIs, including Google Cloud, Microsoft Azure and others, allow for effective implementation of large-scale, cost-efficient PdM systems without significant investments in hardware or software.
Furthermore, by employing APIs, data can be shared across organizations and within an organization itself. As illustrated here, in order to realize its full potential, predictive analytics should open the necessary functions for application via APIs that will allow developers, data scientists, and domain specialists to create applications and solutions that meet the organizational needs.
Through this democratization of predictive analytics, one sees creativity and innovation, more so as cross-functional teams are allowed to work collaboratively in order to solve complex business issues.
However, it is important that one does not ignore the fact that APIs, as much as they bring advantages, has their drawbacks and issues that organizations have to consider. For instance, handling of some rights and responsibilities such as data privacy, security, and compliance when working with third-party APIs needs strict regulation of access to data.
Moreover, the organization needs to assess the quality level, adaptability, and productivity of the API provider to sustain the business needs and service level agreements.
