Data preparation is essential to the
data science process. It ensures that quality data is sourced and only the most valuable and defined insights are retrieved. It is an essential albeit time-consuming process for data scientists, to be able to refine relevant data into actionable datasets ready for analysis using
business intelligence tools.
Using common methods and technologies such as machine learning, data scientists should be able to perform high quality data analytics. The challenge, however, is that for data science teams to effectively process data for analysis, they require accurate and clean data.
Without data prep, the quality of data analytics is doomed to be poor and inaccurate. Data scientists rely on service data to build reliable models and algorithms, and if that data is inaccurate or biased so will be the models they’ll provide operational teams with. Such misleading analytics could lead to harmful business decisions.
With a very large amount of data sources available, a lot is not formatted specifically to user requirements. The data preparation process ensures that data is well formatted and easy to use, adhering to a specific set of rules. To build lucrative machine learning models which increase in performance and reliability, data quality must be the driving force for the data science process using clean data.
By evaluating and improving the accuracy and the quality of datasets, data scientists can accomplish their objectives. Although data prep is time-consuming, it is invaluable to the success of business users.
