Data Preparation is the process of cleaning, structuring, and preparing data for analysis. Data preparation is a crucial step in any text analytics project, as it can make the difference between accurate and inaccurate results. Inaccurate results can lead to bad decision-making, so it is important to ensure that data is prepared correctly before beginning any analysis. Data preparation is by the IT , data analyst, or business analyst team.
There are many tools available to help with data preparation, including open-source software such as R and Python, as well as commercial software such as IBM SPSS Modeler. No matter which tool you use, the goal is always the same: to clean.
Processes of Data Preparation
There are 4 basic processes of data preparation. Here are the processes:
- Gathering. This would involve collecting data from various sources, such as web scraping or APIs, and then storing it in a format that can be analyzed.
- Combining. This is often done when data from different sources need to be analyzed together, or when different data sets need to be compared to each other.
- Structuring. This would involve organizing data into a format that can be easily analyzed. This may involve creating columns and rows or adding labels to data.
- Organizing. This would involve grouping data together so that it can be easily analyzed. This may involve creating categories or adding tags to data.
Components of Data Preparation
There are five main components of data preparation. Namely:
- Preprocessing. Data preprocessing is a data mining technique that involves transforming raw data into an understandable format. Raw data is often incomplete, inconsistent, and/or in the wrong format. The goal of data preprocessing is to make sure that the data is in an appropriate format for the machine learning algorithm that you intend to use. Data preprocessing techniques such as feature selection, dimensionality reduction, and feature transformation can improve the accuracy of your machine learning models.
- Profiling. Data profiling is the process of examining data to understand the distribution, relationships, and patterns that exist within it. Data profiling can be used to understand the data that you have, as well as to identify any problems that may exist. Data profiling is a crucial step in any data preparation process, as it can help to identify issues that need to be addressed before the data can be used for analysis.
- Cleansing. Data cleansing is the process of identifying and correcting errors in data. Data cleansing can be done manually or through the use of automated tools. Data cleansing is a crucial step in any data preparation process, as it can help to ensure that the data is accurate and complete.
- Validation. Data validation is the process of verifying that data is accurate and complete. Data validation can be done manually or through the use of automated tools.
- Transformation. Data transformation is the process of converting data from one format to another. Data transformation can be done for a variety of reasons, such as to make the data more compatible with a specific application or to make the data easier to analyze. Data transformation is a crucial step in any data preparation process, as it can help to ensure that the data is in the appropriate format for the task at hand.