Data cleaning and data transformation are two important steps in the data preparation process to ensure that data is suitable for analysis and modeling. As such, they tend to be used interchangeably and there is the perception that they are one and the same. In reality, they serve different purposes and involve different activities.
Definitions
Data cleaning is the act of cleaning our data by identifying and correcting errors, inconsistencies, and inaccuracies in the dataset. The primary goal of data cleaning is to improve the quality of the data, making it more reliable and accurate for analysis.
On the other hand, data transformation involves converting data, often already cleaned, into a more suitable form for analysis or modeling. Data transformation tasks depend heavily on clean data; hence, it can be said that data cleaning is a prerequisite to data transformation.
Key Differences
The definitions alone should give an idea on how data cleaning and data transformation differ from one another. But we shall delve into the differences a bit further for a much better understanding.
One key difference is with regard to purpose. The purpose of data cleaning is to fix the existing problems in the data, while the purpose of data transformation is to create new possibilities from the data.
Scope is another key differentiator. The scope of data cleaning is usually narrower and more specific, while the scope of data transformation is usually broader and more general.
Lastly, the two differ with regard to when they are to be conducted within the larger data process. While data cleaning and data transformation are necessary steps in the data process, the choice of which goes first would depend on the nature and complexity of the data and the analysis goals. In general, data cleaning should be done before data transformation, as it ensures that the data is reliable and consistent. However, in some cases, data transformation can be done before or during data cleaning, as it can help to identify or solve some data quality issues.
Best Practices
Standardize data types and naming conventions – Establish the standard data types and naming conventions across sources so that you can accurately analyze and compare your data and organize according to such data types. This also helps determine which data is to be discarded for the purposes of the eventual data transformation process.
Add metadata to provide context - Log data will not often provide all of the context within the data so it would be necessary to add mission-critical context that might be needed by the people using the data. This can include information about the source, relevant URLs, or promotional codes, among others.
Optimize data before it’s stored - To handle large amounts of data, it is important to optimize data to maximize the efficiency of both compute and storage. That includes indexing data so queries are more efficient and compressing data to reduce storage costs.
Put a process in place to handle malformed or incomplete data - Part of ensuring that the data remains clean is rejecting or fixing data that is incorrect or malformed. That could mean putting a data limiter in place for certain fields that rejects certain data types, string lengths, or some other required parameter for your data. Ideally, there should be a process in place to review data that is rejected to ensure there would be no gaps in the data and such issues are addressed promptly.
Validate the data transformation - Before sending large amounts of data to a database, it is important to set up the data transformation correctly and that it is done at any time there is a new ingest source. It is important to ensure that all expected fields are included and have the correct names, that data is being stored as the correct datatype, and to verify that the data is being transformed correctly if there are more advanced computations and transformations.
The Importance of Data Cleaning and Data Transformation
Data transformation and data cleaning are both essential for effective data management, If the data is not cleaned and transformed properly, it can lead to inaccurate, misleading, or biased conclusions. And if the data is not transformed appropriately, it can affect the performance and interpretation of the analytical models, such as regression, classification, or clustering.
Implementing best practices for both processes ensures high-quality data, which is crucial for accurate business insights and strategies. Combining these practices helps businesses make informed decisions based on reliable and well-structured data.
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