The data preparation process is a compulsory phase of transforming collected data into analytical and actionable information. Once the data is prepared, analysts can analyse data using dashboards and KPIs and build models to support their findings. At the beginning, data is used as part of a use case or business question.
Data helps to improve a business’ market understanding and action business decisions based on relevant information. The main obstacle in ensuring data quality is that data is retrieved from multiple sources in a fast-growing multitude of forms which doesn’t guarantee its reliability. A methodological approach must be employed to process data and extract information that is useful.
Data preparation tools ensure the extracted information is formatted and corresponds to the practices outlined by a business’ user cases. Preparing data for analysis also ensures a higher data quality as it involves both a cleaning and a matching phase. It is impossible to build applicable models or algorithms without using analytics and trustworthy data transformation.
Analytics can’t be made quickly if data is not processed or incorrectly processed, because it needs to be cleaned first
. Relying upon raw data without data cleaning means that data analysts will be sharing inaccurate analytics.