Data Preparation Process

There are a few important steps in the data preparation process, and each one is essential to making sure the data is prepared for analysis or other processing. The following are the key stages related to data preparation:

Step 1: Describe Purpose and Requirements

Identifying the goals and requirements for the data analysis project is the first step in the data preparation process. Consider the followings:

• What is the goal of the data analysis project and how big is it?
• Which major inquiries or ideas are you planning to investigate or evaluate using the data?
• Who are the target audience and end-users for the data analysis findings? What positions and duties do they have?
• Which formats, types, and sources of data do you need to access and analyze?
• What requirements do you have for the data in terms of quality, accuracy, completeness, timeliness, and relevance?
• What are the limitations and ethical, legal, and regulatory issues that you must take into account?

With answers to these questions, data analysis project’s goals, parameters, and requirements simpler as well as highlighting any challenges, risks, or opportunities that can develop.

Step 2: Data Collection

Collecting information from a variety of sources, including files, databases, websites, and social media, to conduct a thorough analysis, providing the usage of reliable and high-quality data. Suitable resources and methods are used to obtain and analyze data from a variety of sources, including files, databases, APIs, and web scraping.

Step 3: Data Combining and Integrating Data

Data integration requires combining data from multiple sources or dimensions in order to create a full, logical dataset. Data integration solutions provide a wide range of operations, including combination, relationship, connection, difference, and join, as well as a variety of data schemas and types of architecture.

To properly combine and integrate data, it is essential to store and arrange information in a common standard format, such as CSV, JSON, or XML, for easy access and uniform comprehension. Organizing data management and storage using solutions such as cloud storage, data warehouses, or data lakes improves governance, maintains consistency, and speeds up access to data on a single platform.

Audits, backups, recovery, verification, and encryption are all examples of strong security procedures that can be used to make sure reliable data management. Privacy protects data during transmission and storage, whereas authorization and authentication

Step 4: Data Profiling

Data profiling is a systematic method for assessing and analyzing a dataset, making sure its quality, structure, content, and improving accuracy within an organizational context. Data profiling identifies data consistency, differences, and null values by analyzing source data, looking for errors, inconsistencies, and errors, and understanding file structure, content, and relationships. It helps to evaluate elements including completeness, accuracy, consistency, validity, and timeliness.

Step 5: Data Exploring

Data exploration is getting familiar with data, identifying patterns, trends, outliers, and errors in order to better understand it and evaluate the possibilities for analysis. To evaluate data, identify data types, formats, and structures, and calculate descriptive statistics such as mean, median, mode, and variance for each numerical variable. Visualizations such as histograms, boxplots, and scatterplots can provide understanding of data distribution, while complex techniques such as classification can reveal hidden patterns and show exceptions.

Step 6: Data Transformations and Enrichment

Data enrichment is the process of improving a dataset by adding new features or columns, enhancing its accuracy and reliability, and verifying it against third-party sources.

• The technique involves combining various data sources like CRM, financial, and marketing to create a comprehensive dataset, incorporating third-party data like demographics for enhanced insights.
• The process involves categorizing data into groups like customers or products based on shared attributes, using standard variables like age and gender to describe these entities.
• Engineer new features or fields by utilizing existing data, such as calculating customer age based on their birthdate. Estimate missing values from available data, such as absent sales figures, by referencing historical trends.
• The task involves identifying entities like names and addresses within unstructured text data, thereby extracting actionable information from text without a fixed structure.
• The process involves assigning specific categories to unstructured text data, such as product descriptions or customer feedback, to facilitate analysis and gain valuable insights.
• Utilize various techniques like geocoding, sentiment analysis, entity recognition, and topic modeling to enrich your data with additional information or context.
• To enable analysis and generate important insights, unstructured text data is classified into different groups, such as product descriptions or consumer feedback.

Use cleaning procedures to remove or correct flaws or inconsistencies in your data, such as duplicates, outliers, missing numbers, typos, and formatting difficulties. Validation techniques like as checksums, rules, limitations, and tests are used to ensure that data is correct and complete.

Step 8: Data Validation

Data validation is crucial for ensuring data accuracy, completeness, and consistency, as it checks data against predefined rules and criteria that align with your requirements, standards, and regulations.

• Analyze the data to better understand its properties, such as data kinds, ranges, and distributions. Identify any potential issues, such as missing values, exceptions, or errors.
• Choose a representative sample of the dataset for validation. This technique is useful for larger datasets because it minimizes processing effort.
• Apply planned validation rules to the collected data. Rules may contain format checks, range validations, or cross-field validations.
• Identify records that do not fulfill the validation standards. Keep track of any flaws or discrepancies for future analysis.
• Correct identified mistakes by cleaning, converting, or entering data as needed. Maintaining an audit record of modifications made during this procedure is critical.
• Automate data validation activities as much as feasible to ensure consistent and ongoing data quality maintenance.

Raw data may or may not contain errors and inconsistencies. Hence, drawing actionable insights is not straightforward. We have to prepare the data to rescue us from the pitfalls of incomplete, inaccurate, and unstructured data. In this article, we are going to understand data preparation, the process, and the challenges faced during this process.