The era of IoT brought along a big transformation in the enterprise data lifecycle but as if that was not enough, the implementation of AI on the edge is completely revolutionizing the whole ecosystem in order to make sure it meets real-time analysis needs. It’s very obvious that IT pros will be making grave mistakes if they think they can afford a room for guesswork in IoT infrastructure planning. The fact on the ground these days is that your digital architecture can undergo changes unpredictably within a space of two years because IoT and AI have seriously started playing great roles in the gathering and handling of data. Minimal diversity in the traditional data lifecycle When we have not started operating in clouds, it was easier for IT pros to execute, understand, and control the data lifecycle, since the enterprise data lifecycle was relatively manageable and essentially static and circular. Then, data was more structured, less diverse, and traversed only a few routes to lesser destinations. At that point in time, we did not have the need for the mazy steps we have now in the traditional data lifecycle you could easily carry out the following processes on your data…
