Data Blog by Lizeo

The definition of data preparation by Lizeo

What is data preparation?

Data preparation is the process of transforming raw data to extract value and ensure data quality. This mandatory step of preprocessing ensures that data is reformatted, corrected and can combine various datasets to refine the information which has been collected.
Data preparation is a time-consuming process however, it is an imperative stage of data analytics that removes any misleading or biased data quality. Business decisions can only be made when precise and dependable data that has been cleaned and processed is presented for collaboration with business users. It is also essential for the automatic generation of marketing content from data.
Steps to ensure accurate data preparation include:
  • Data collection: Collecting relevant data from various data sources depending on use case.
  • Data discovery: Discovering various datasets to become acquainted with data and determine what type of data preparation tool will be necessary for it to become actionable.
  • Cleaning, normalisation & matching: Cleaning raw data to delete duplicates, adjust data structure and reformat so it can be compared and merged using existing datasets. Any absent data value can then be matched.
  • Data transformation: Integrating the marketing and business perspective as well as using different data sources to prepare a data cube. This makes it possible to enrich data and include additional information using authoritative third party data sources. This guarantees informed business decisions with successful outcomes.
Using automation for data prep is exceedingly recommended to reduce processing time so professionals can concentrate on data analytics. This increases scalability so the process of data preparation evolves at the same speed as the business and ensures a higher quality and quantity of data collected. Automation also helps to evade human error and to maintain quality data management. Essentially, it is impossible to analyse data without preprocessing it. These kinds of production units ensure the data is analysed with a methodological process to produce precise and reliable information.

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