Data Visualization is driven by data. Its form is often derived from optimizing the efficiency of inputting data (and information about that data) into a human brain. It is a very pragmatic practice, built around numbers and logic.
And yet it is beautiful. It evokes emotions. It can be aesthetically pleasing, or hideous. It communicates complex concepts and provokes thought. It is consumed for enjoyment. Some visualizations even share similarities with poetry.
There are several stages in the life cycle of data visualizations, and while the core of the practice is driven by rational thinking, any number of stages in the process have opportunities for subjective decisions or artistic interpretations...
The purpose of Big Data is to supply companies with actionable information on any variety of aspects. But this is proving to be far more difficult than it looks with over half of Big Data projects left uncompleted.
Two of the most often reported reasons for project failures are a lack of expertise in data analysis. Reports show that data processing, management and analysis are all difficult in any phase of the project, with IT teams citing each of those reasons more than 40% of the time.
However, failures in Big Data projects may not solely lie on faulty project management. In a recent survey, a staggering 80% of Big Data’s biggest challenges are from a lack of appropriate talent. The field’s relative infancy is making it hard to find the necessary staff to see projects through, resulting in underutilized data and missed project goals.
IT teams are quickly recognizing a chasm between executives and frontline staffers whose job it is to apply findings from Big Data. In the end,it may not be the anticipated cure-all for 21st century business management. It is only as good as good as the system that runs it.
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