Data preparation is the process of organizing, cleansing, and preparing unstructured data so that it may be used more effectively in business analytics and ML applications.
When performed correctly, data preparation also assists an organization by:
- Ensuring the data used by analytics programs provide accurate findings;
- Boosting the ROI of BI and analytics projects;
- Reducing expenses for data management and analytics;
- Identifying and correcting data errors that could not otherwise be noticed;
- Allowing executives and operational employees to make better-informed judgments; and
- Avoiding duplication of work in data preparation for various applications;
Data Preparation Process Steps
Different data professionals and software providers may include somewhat different data preparation techniques, but the process often includes the following:
- Data acquisition. Information is collected from many systems, including databases, data warehouses, and data lakes. During this stage, data scientists, members of the BI team, other data professionals, and end users who acquire data should verify that it aligns well with the analytics application goals.
- Data exploration and profiling. This involved investigating the gathered data for insights into its contents, and the necessary steps to prepare it for its intended applications. To assist with this, data profiling detects trends, correlations, and other data properties, as well as inconsistencies, abnormalities, missing numbers, and other problems that need to be addressed.
- Data cleaning. The discovered data mistakes and problems are then resolved to provide full and accurate data sets. As part of cleaning data sets, for instance, incorrect data is eliminated or corrected, missing values are filled, and conflicting entries are harmonized.
- Data structuring. The data must now be modeled and structured following the analytics requirements. For instance, data saved in CSV files or other file formats must be translated into tables for BI and analytics tools to access it.
- Transformation and augmentation of data. In addition to being organized, data must often be translated into a standardized and usable format. For instance, data transformation might entail the creation of new fields or rows that aggregate existing information. Data enrichment significantly improves and optimizes data collections (as required) by adding and enhancing data.
Validation and publication of data. In this last phase, automated algorithms are executed against the data to ensure its consistency, completeness, and precision. The generated data is subsequently placed in a data warehouse, a data lake, or a similar repository, where it is either utilized directly by the individual who prepared it or made accessible to other users.