Proper data preparation is an integral component of making all processed data more accessible to users. When done correctly, data preparation makes the analysis more efficient and limits inaccuracies and errors that can pop up in data when processing.
Today, anybody can use many new tools to qualify and cleanse data independently, making data preparation more painless than ever. This article will discuss what data preparation entails and how it is done.
How Does Data Preparation Work?
Data preparation comprises the various steps of cleaning and converting raw data before it is processed and analyzed. Data preparation is an essential step before data can be processed and typically involves making corrections to data, reformatting data, and combining data sets to make the data more usable.
Most of the time, data preparation is a tedious undertaking for business users and data professionals. However, putting data in context is crucial if you wish to convert it into insights and eliminate bias due to low data quality. The tedious data preparation parts typically include enriching source data, standardizing data formats, and taking out outliers.
Why Do You Need Data Preparation?
According to Forbes, 76% of data scientists consider data preparation the most tedious part of their job, but accurate, efficient business decisions can only be made with clean data. Data preparation helps to:
- Produce better quality data: Cleaning and reformatting datasets makes sure that any data used later in the analysis is of high quality
- Fix errors: Data preparation is necessary to catch any errors before the data is processed.
- Make more informed business decisions: Data that is of higher quality can be analyzed and processed much more efficiently and quickly, which leads to business decisions that are more timely, efficient, and overall higher-quality.
Moreover, as data and data processes migrate over to being cloud-based, data preparation becomes even more necessary. Some benefits of cloud-based data preparation include:
- It’s Future Proof: Cloud data preparation automatically upgrades so that new problem fixes or capabilities can be turned on the moment they are released. This highly efficient upgrade system allows organizations to stay one step ahead of innovation without additional costs and delays.
- Superior Scalability: Cloud data preparation expands according to the individual scale of the business. Companies don’t have to worry about continually expanding their underlying infrastructure or predicting where technology is going.
- Accelerated Data Usage and Collaboration: When data preparation in the cloud means it is always working, doesn’t call for additional technical installation, and allows teams to collaborate on the work to produce faster results continually.
A reliable, cloud-native data preparation tool will offer other benefits, such as a GUI that are easy to use and intuitive for more comfortable and more efficient preparation.
How To Prepare Data
While data preparation specifics depend heavily on organization, industry, and need, the framework largely remains the same. Here are the necessary steps of data preparation:
- Assemble Data: Any data preparation process starts with gathering the right data set. The data can be added as you go or be pulled from an existing data catalog.
- Find And Assess Data: After the data is collected, each dataset needs to be identified. This step comprises determining the data and understanding exactly what needs to be done before the data is useful in any particular context. Data identification can be a daunting task, but Zuar offers a data preparation platform that provides visualization tools that assist users in browsing and profiling their data with minimal downtime and effort.
- Clean And Validate Data: Traditionally, cleaning up the data is the most time-consuming portion of data preparation, but it’s essential for getting rid of erroneous data and filling in gaps. Critical tasks here include taking out extraneous outliers and data, conforming data to a more standardized pattern, filling in missing values, and concealing sensitive or private data entries. After the data is cleaned, it needs to be validated by testing to check for any other errors in the data preparation process up to this point. It is common to catch a mistake in the system that needs to be resolved before moving forward.
- Reconstructing And Augmenting Data: Transforming data comprises updating the value entries or format to reach a clear outcome or to make the data more easily understood by a wider audience. Augmenting data is the process of connecting and adding data with other relevant information to offer more in-depth insights.
- Store Data: After the data is prepared, the data can either be stored or channeled into a third-party application, like a business intelligence tool. This clears the way for processing and analysis.
Looking for an efficient data solution for your company? Work with Zuar today.
Easy-To-Use Data Preparation Tools
Data preparation is a vital process for any commercial usage of data, but it also calls for a significant investment.
Data analysts and data scientists say that they spend around 80% of their time doing data prep rather than analysis. This begs the question: Is your company’s data team spending too much time on thorough data preparation? What if your organization doesn’t have a team of data analysts or data scientists at all?
This is where Zuar’s business intelligence solutions come in. Zuar’s Data Value Chain offers a flexible process to deliver answers for any business questions in mere days rather than months. This approach to BI helps companies use and refine their data and consequentially outperform their competitors.
Mitto ETL+ Tool
Zuar has developed a tool, Mitto, to make data preparation easy, streamlined, and extensible. Mitto is an automated data pipeline that stages data for analytics. Easily integrate data from multiple sources into a database and prepare it for analysis. We set up everything for you as a service, in the cloud or on-premise.