Nowadays with such a growing interest in data analysis / data science, there are abundant articles and material covering the skillset necessary to master those disciplines. Virtually, any blog, magazine, book, or specialized web site or portal describes the skillset necessary to become a data scientist, data analyst or any data professional.
Virtually all of them, have some fundamental skillset necessary to become a data professional such as Technical Science Skills, Mathematics, Statistics, Technical Computing Skills (Python, R, SQL, Hadoop, etc.).
However, there always seems to be a low emphasis on being specialized in the industry that they are working in, and it doesn’t exactly mean the ‘data industry’ per se. It is fundamental to understand what type of problems need to be addressed within that specific industry and identify ways in which data can be leveraged to drive solutions and otherwise unobtainable business insights.
There are three fundamental keys in business intelligence these are ‘Data’, ‘Information’ and ‘Knowledge’, often times the lines are blurred about what is data, what is information and what is knowledge. It is easy for data professionals and others to use data to produce visually appealing charts and reports but limit themselves to describe what they see –this is information-, but not understanding the real meaning of the report -this is knowledge-.
Data are facts of the world (financial transactions, age, temperature, etc…). Information appears when we work with those numbers and we can find value and meaning. Information should help us to make informed decisions.
Knowledge is when data and the information turn into a set of rules to assist the decisions. In fact, we cannot store knowledge because it implies theoretical or practical understanding of a subject.
The trouble with real world data -the commodity- is that the probability of finding false correlations is high and gets higher as the datasets grow. So, data professionals and anyone dealing with data need to apply fundamental rules to the whole process from end-to-end: Identifying the right data –know all data isn’t equal-; aggregate data in the right way to get the relevant information –know the business-; extract the right knowledge to address specific case- leverage knowledge to drive innovation and change.
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