Given the amount of data being accumulated by individuals and teams within the organization on a daily basis, there is a great deal of possibilities that the raw data may not be deemed suitable for whatever purpose it will be needed for in the first place.
This is where data preparation comes in. Simply put, it is the process of preparing raw data so that it is suitable for further processing and analysis. It is an important process in itself as it often involves critical tasks such as reformatting data, making corrections, and combining datasets to enrich data. In turn, this prepared data will be utilized for machine learning algorithms and for data visualization and exploration.
The steps in data preparation
The specifics of the data preparation process vary by industry, organization, and need, but the workflow largely follows a particular process which is as follows:
1. Data gathering - The data preparation process begins with finding the right data. This can come from an existing data catalog or data sources can be added ad-hoc.
2. Discovery and assessment - This is about getting to know the data and understanding what has to be done before the data becomes useful in a particular context. Given the enormity and complexity of this task, it helps to have visualization tools to help in the profiling and browsing of data.
3. Cleansing and validation - Cleaning up the data is traditionally the most time-consuming part of the data preparation process. Nevertheless, going through this step is important to produce a more accurate and cohesive dataset. Important tasks here include:
Removing extraneous data and outliers
Filling in missing values
Making data align with industry or organization standards
Masking private or sensitive data entries
Once the data has been cleansed, it undergoes validation by testing for errors in the data preparation process up to this point. Any errors that were detected during this process must be resolved before moving forward.
4. Data transformation -After the data is cleaned and validated, it undergoes a transformation that will make the data more easily understood by a wider audience and provide additional insights as well. This is usually done through visualizations and the inclusion of information relevant to the data.
5. Storing - Data is stored or channeled into a third-party application such as a business intelligence tool, thus clearing the way for processing and analysis to take place.
Why data preparation is important
Data preparation is often a lengthy undertaking, especially for data engineers or other data users. But it is an essential process and a prerequisite in order to eliminate biases and provide the needed context in order to make the data more insightful for its audience.
On a macro level, data preparation creates higher-quality data for data science, analysis, and other data management-related tasks by eradicating errors and normalizing raw data before it is processed.