Data Preparation Meaning?

Data preparation is the process of gathering, cleansing, transforming and modelling data with the goal of making it ready for analysis as part of data visualization or business intelligence.

Data preparation is an important step in data analytics as well as in business intelligence. It's also a core function of business analysts.

In its most basic terms, preparation means making sure that data from multiple sources can be combined seamlessly into a single useful source. That usually requires unifying and normalizing data so that it can be used for further analysis and reporting.

What Are the Benefits of Data Preparation?

A data preparation platform can help you find, prepare, and use your data faster than you ever thought possible. Here are four ways a modern data prep platform can make your life easier:

• Centralized repository for all data. This makes it easier to discover relevant datasets without having to remember where they're located or manually copy them into one place. It also helps you avoid duplicating efforts when multiple people need the same information.
• Automated data prep tasks. Automating the grunt work associated with manual data prep tasks like cleansing your data and verifying its accuracy minimizes repetitive tasks like these. A modern data prep platform allows you to focus on higher-value activities that are high impact.
• Powerful data insights. It lets you use familiar tools to get insights from your data even if those tools weren't built for the task.
• Identify and fix errors quickly. Data preparation ensures that everything in the data ecosystem is consistent and therefore makes it easier to identify issues when something does go wrong.

How It Works

Data preparation is one of the most important steps in data mining, and it’s also the most tedious part. It involves such tasks as collecting data, cleaning it up for further processing, transforming it into a suitable format, and so forth.

The idea behind data preparation is to transform raw data into a form that you can use for analysis.

Raw data is usually collected from multiple sources. For example, if you want to analyze customer behavior, your raw data might come from your company’s sales database and customer relationship management (CRM) system. In this case, sales records would be stored in a sales table while customer information would be stored in a customer table. These two tables would probably have identical fields (or columns), but they would contain different values. It’s your job as a data scientist to combine these two tables into one big table so you can determine which customers bought what products and how much they paid for those products.

The data preparation process is often overlooked in the rush to find insights in new data sources. It's a tedious, time-consuming task, but it's important to implementing a data-driven business strategy.

Data preparation is the process of collecting, transforming and enriching raw data to make it suitable for analysis. This can include cleaning, normalizing and consolidating raw data sets, as well as adding new dimensions or attributes to the data.

Data preparation is sometimes referred to as "data wrangling." It's the process of taking raw data from a variety of sources and getting it ready for analysis. Data preparation is a critical aspect of data science — the quality and reliability of analysis is only as good as the quality of input data.

Examples of Data Preparation

An example of data preparation is ensuring numerical values are stored within a table or warehouse consistently. Take the value of "time"- some may input time as "2:30 PM", while others may input this as "14:30". Whether its automatic or manual, someone is making sure that all times are inputted as EITHER on a 24-hour clock, or on a 12-hour clock, but not both. This way, those looking back on the data are able to compare the values in a clean manner.

Data Preparation Tools

• Microsoft Power BI. This powerful tool makes it easy to deal with large volumes of data and then process it, creating insights automatically that will impact businesses. It also integrates seamlessly with other products in the Microsoft suite.
• Tableau . Tableau makes data visualization a seamless activity, making it friendly for those who may not be as confident with the technical side of data. It also makes spotting trends within KPI's easy.
• SAP Data Intelligence. Despite being a large box solution, SAP Data Intelligence is customizable to the organization's needs and KPI's.