Data Preparation: The Key to Better Analysis


Data Preparation in Social Research2
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How do you analyze data if it is not in the perfect format? To properly analyze data, you need to make sure that the data is formatted correctly and each variable is clearly labeled so you can appropriately interpret what you’re seeing. Before analyzing data, it needs to be ready and prepared. This process of getting the data ready before analysis is called data preparation. 

It can be one of the most time-consuming parts of analytics and the least enjoyable part of data scientists’ jobs, but also one of the most important. Preparing data for analysis can be an intimidating process, but it doesn’t have to be that way!

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What is data preparation?

Data preparation is, as its name suggests, a preparatory stage of big data analytics that involves cleaning and transforming the existing raw data into a format suitable for further processing. In a nutshell, data preparation refers to the process of ensuring that the data is formatted and prepared for analysis.

In order to turn raw data into valuable insights and minimize or avoid errors caused by low data quality and integrity, it is important to place data in the proper format.

Data preparation may require to remove duplicate rows or columns, merge multiple datasets together, perform string manipulation on columns containing text, apply custom logic or functions to transform the dataset from one representation into another.

Benefits of data preparation

Data preparation is a fundamental step in data analysis. This process is often overlooked, but it is a critical step that can dramatically improve the analysis and results. Before making any business decisions, be sure that the data is clean and accurate. There are many advantages of cleaned and correct data, including – 

  • Access to high-quality data: Data preparation assists organizations in obtaining clean, easy-to-use, structured data and maintaining its consistency.
  • Improved Analysis: Day to day operations in an organization are dependent on data analysis, so clean and prepared data helps in better analysis. It reduces errors and eliminates anomalies or illogical values.
  • Make interpretation easier: Prepared data is simple to understand, and anyone can work with it effectively. It enables organizations to interpret data systematically.
  • Increased flexibility: Access to high-quality data increases flexibility. Flexibility improves the competitiveness of an organization.
  • Better decision making: ​​Data preparation leads to better decision making. It is crucial to have access to accurate and reliable information to make more effective, fact-based decisions.
  • Increased productivity: Employees tend to be more productive when they get access to high-quality, error-free data.
  • Elimination of errors: During data preparation, sporadic errors can be detected in advance, and data errors can be avoided.

Data preparation process

Data preparation involves several steps before getting started with creating reports or performing any statistical analysis. The first step in the data preparation process is collecting the data from various datasets and understanding what exactly needs to be done before that data can be used for analysis. 

The collected data can be messy and incomplete, requiring cleaning it up by removing duplicate records and other unwanted information such as getting rid of unnecessary extra data and outliers, identifying missing values, etc. 

Data validation is also a part of data preparation, as it helps ensure the data is accurate. Test the cleansed data and it must be validated for errors. Ensuring that the data can produce correct results will save time later on.  

In many instances, the data is available in a format that makes it difficult or impossible to use without some level of manipulation so it is needed to transform the data into a structured format to make it more understandable to a larger audience. Once the data is prepared, it can be stored and used for other analytical processes and analysis. 

Challenges of data preparation

Data preparation is a time-consuming and labor-intensive process. If not done carefully, it can also cost organizations a lot of money. There are three major challenges in data preparation: dealing with complexity, volume, and timing.

Complexity: One of the most complex processes in data analysis is data preparation. Data collected from various sources may have issues with quality, accuracy, and consistency. Missing or incomplete data and Invalid data values can make data complex and difficult to prepare for the analysis. 

Volume: Organizations generate massive amounts of data in various formats, making it difficult to prepare such a sheer volume of data for analysis.

Time: Timing refers to how quickly an organization needs access to the data once it has been prepared, since loading and preparing large volumes of data might take some time. As previously stated, it is a time-consuming and resource-intensive process that poses challenges to an organization.

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Cloud and data preparation

Cloud computing has taken the business by storm in recent years and can be used for all kinds of different operations. Within cloud-computing environments, many operations can be performed faster, more reliably, and often more cost-effectively than using the on-premises infrastructure.

Data preparation is also one of these operations that companies can perform within the cloud. It is often regarded as a separate stage of pre-processing when working with large volumes of enterprise data. With the help of the cloud, prepared data can be accessible to all departments within your organization, resulting in improved collaboration between departments. Cloud storage can store massive amounts of data, and anyone can access it from any location. 

In the end, although data preparation can be considered a tedious task, the bulk of it consists of hours spent cleaning up, adding missing values, and performing normalization on the data. However, if organizations put in enough effort they can greatly increase the chance of successfully analyzing the data by making sure the data is valid and properly prepared.

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