The Big Data Roadmap Finds Many Expectantly and Unfortunately Lost
Big data is the opposite force to the more traditional relational system. Big data is often considered too big for its own good. The query languages are hardly uniform or relational. They are built on data engineered on advanced algorithms that collect resource data from sources through multiple channels. Creating a roadmap is almost impossible. Can big data reduce waste in clinical data management? Of course, it can. But, the obstacles to getting there are holding it back from becoming the next obvious step in medical data warehousing. Big data plays with the senses and distorts general expectations of what data can and should be all about.
Big Data is Cost-Effective Upfront
The big circling question is cost. Implementing a big data network is meant to be a cheaper alternative than building a defined data system from scratch. Unfortunately, the costs are often vague. The actual clinical data efficiency network is hard to define in any structural way. It is like an amorphous network that evolves and alters based on a wide number of incoming and outgoing variables. The network is too vast, and ironically too big, to effectively fit any pre-defined structuring or SQL databasing.
So why does it work? The data received from the big data efficiency network is invaluable. Compared to how traditional networks are established in medicine, big data is so much more beyond their capabilities.
The Roadmap to…What?
The inherent flaw is that big data is unable to be easily road mapped. If traditional networks were a map of the United States with color coding, big data is a map relayed in morse code from someone with a hearing problem. In a more practical sense, big data networks need to be relayed through a data scientist to properly healthcare big data. Of course, big data developers are finding ways to streamline the data pools and try to make them more accessible to non-engineers. But, the fact of the matter is, big data is big and complicated.
The big question becomes, is it worth it? Many would argue no, and they would have a viable point. Big data is inexpensive to integrate, but it still requires serious training and program design overhauling. New staff members need to be aware of how the system works, at least on a surface level. Engineers need to be hired to translate data pools to make sense of this extraordinarily valuable information. If no one can translate it, it becomes pointless.
