data transformation
According to Kirk (2016), there are four group actions which are, data acquisition, data examination, data transformation, and data exploration. In this discussion, I would choose data transformation to elaborate on. With the great quality of information floating on these days, it is essential to channelize the information suitably to make it more interpretable and meaningful. A good example that can elaborate on the value of data transformation would be: if you had this dataset, and wanted to clover this data over the phone to somebody, imagine how hard it will be? With this after data transformation, aggregating nations by size and courses by fields. Some of the actions that are undertaken in the data transformation and how they are essential are discussed as follows.
The first action in data transformation is cleansing the data. This includes discarding all the entries with null figures, or at least fill them up with average data. The second action of data transformation is conforming data to the target format. On this, it happens when the data required is in DD-MM-YYYY form and the existing data has it in YYYY/DD/MM format, one needs to modify the dataset o as to conform to requirements. The third action of data transformation is standardizing the data. This action allows the data ready for worldwide utilization. When the data is in piston dictionary format, changing it to JSON format will enable it to be hugely usable and acceptable.
Data transformation actions are significant in that they help in changing data from one format or structure into another form. It is essential to operations such as data integration and data management. It helps in making data more meaningful.