Data Preparation for BI
In order to gain useful business intelligence, it is important to focus upon data preparation before visualizing it. As a neat and well prepared dataset results in accurate, meaningful and clean data visualization, that considerably enhances the overall business intelligence. The process of data preparation entails cleansing, structuring and integrating data to make it ready for analysis.
According to an article published in The New York Times. Data scientists, according to interviews and expert estimates, spend from 50 percent to 80 percent of their time mired in this more mundane labor of collecting and preparing unruly digital data, before it can be explored for useful nuggets.
When the datasets are very small and simple, such as a handful of similarly structured Excel spreadsheets, data preparation will not be much of an issue. Since the different pieces of data are already stored in a similar format. However if the dataset size is huge, a number of preparations have to be done to gain effective business intelligence.
Data preparation becomes imperative and resource intensive in a number of particular situations. Such as when more than one type of data source are being used, like Excel and SQL data, from various business applications. And while working with large datasets and/or messy and unorganized data.
If an enterprise finds itself forced to summarize data before analyzing it, almost makes it clear that data preparation is necessary to be carried out now. For situations like the data is just too big for its BI solutions to handle, or they have to involve its IT or technical departments whenever a new piece of information is needed.
Within the process of data preparation, various tools are utilized to automate or at least greatly simplify the bulk of the data preparation process. By using pre-programmed adapters that connect into different types of data sources, and restructuring the data into a single centralized repository.
A limited set of data integration capabilities is included within almost every data visualization or business intelligence solution nowadays.
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