After the Twitter IPO, Big Data Will Become More Structured

I should certainly mention the Twitter IPO. And immediately ask the question what this means for the sources Big Data tends to come from. A total of nine according to Kapow Software (see infographic below): archives, docs, media, business apps, social media, public Web, data storage, machine log data, and sensor data. Red means external data (public Web), yellow is internal (archives and data storage), and orange designates both (docs, media, business apps, social media, sensor data). Intelligence by Variety Kapow plotted the Velocity, Variety, and Volume characteristics of each, and put a black peg to them, indicating the data’s structruredness vs. unstructuredness. As was the case with last week’s infographic, the picture really triggers you to take notice of the postulated question “Intelligence by variety: where to find and access Big Data?” This largely depends on APIs which, Kapow says, aren’t available for archives, docs and machine log data. The other categories – media, business apps, social media, public Web, data storage, and sensor data – do have some APIs. Semantic Web through IPOs and APIs What does this mean? Right, we have an extraction and an interoperabilty challenge. All nine Big Data sources are drawn as silos which obviously is the reason we must deploy Big Data techniques to make more sense out of them. But I can assure you this: social media, the public Web, “other” media, “business” apps, and soon also sensor data – these five out of nine already are ingredients of one thick and semantically related Big Data soup. Since after Twitter’s IPO, which I feel will be far more successful than for instance Facebook’s, the semantically related  Big Data soup will become thicker and richer than ever, we might well end up toward the Semantic Web situation of lod-cloud.net. The Infographic BTW, here is Kapow Software’s infographic which will soon need a major update (click to enlarge):