Good data preparation gives efficient analysis, limits errors and inaccuracies that can occur to data during processing, and makes all processed data more accessible to users. It has also gotten easier with the self-service data preparation tool that enables users to cleanse and qualify on their own.
In simple terms, data collection can be termed as collecting, cleaning, and consolidating data into one file or data table, primarily for use in the analysis. In more technical terms, it can be termed as the process of gathering, combining, structuring, and organizing data to be used in business intelligence (BI), analytics, and data visualization applications. Data preparation is also referred to as data prep.
Importance of data preparation
Fix errors quickly - The data Preparation process helps to catch errors before processing. After data has been removed from its source, these errors become more challenging to understand and correct.
Top-quality data - Data Cleansing and reformatting datasets ensure that all data used in the analysis will be high quality.
Better business decision - Higher quality data can be processed and analyzed more quickly and efficiently leads to more timely, efficient, and high-quality business decisions.
Superior scalability - Cloud data preparation can grow at the pace of the business.
Future proof - Cloud data preparation automatically upgrades so that new capabilities or bug fixes can be triggered as soon as they are released. This allows organizations to stay ahead of the future betterment without risking delays or additional costs.
Accelerated data usage and collaboration - Doing data preparation in the cloud is always on, does not require any technical installation, and lets teams collaborate on the work for faster results.
Now, The Self-service Data Preparation process has become faster and more accessible to a wider variety of users.
To learn more about data preparation, Schedule a demo.