When people hear the term big data system, only a few are familiar with its concepts. Several business owners, however, may have a general idea. To them, big data analysis involves the collection of a massive amount of information being generated, then using such to craft meaningful insights and decisions. And they would be right about that. For the analyst, however, big data systems mean way more that that.
Extricating Meaning from Mounds of Information
A big data analyst needs to have a specific set of skills in order to do his job. One such skill is understanding that data has meaning. Data scientist Dr. Steve Hanks stresses that most of the time, people tend to forget that data means something. Therefore, that meaning should be understood. Heaps of numbers and percentages are more than simple figures. What do they stand for? What do their current values mean to the success of the organization?
The Real Nature of Data
It was once said that if something can’t be measured, it doesn’t exist. In the world of data analytics, scientists tend to think the same way. When something is said to be ‘data-driven,’ scientists often mean it is ‘evidence-based.’ Something that’s data-driven is considered quantitative and/or tangible. It’s something that can be actually measured. Data, however, is not only quantitative. Qualitiative data are often missed out on by analysts trying to be ‘data-driven.’ Simply put, to derive meaning from a sea of information, an analyst must look at both natures of data and never either way around.
Bringing Everything into the Spotlight
After extricating the meaning, what now? An analyst’s final task is to make that meaning digestible. For that, he can resort to a simple method: visualization. One of the best ways to communicate a data set’s meaning is presenting the vital concepts graphically. The method obviously relies on the concept of visual learning, where a wall of text doesn’t hold sway.