What is data preparation?

What is data preparation?
April 28, 2021 | Data Preparation

What is data preparation?

Introduction

Good data preparation gives efficient analysis, limits errors and inaccuracies that can occur to data during processing, and makes all processed data more accessible to users. In a sense, data preparation is like washing freshly picked vegetables so long as unwanted elements such as imperfections or dirt are removed. Taking another example, we know that the process of chopping onions to a more satisfactory state will allow for flavors to spread more quickly than would be the case if we were to drop a whole onion into the saucepot. Likewise, transforming data in the data preparation phase is getting data into a state in which it will be easier to work with. To work effectively with data, it must be prepared to address missing or invalid values and remove duplicates towards ensuring that everything is formatted correctly.

What is data preparation?

In simple terms, data collection can be termed as collecting, cleaning, and consolidating data into one file or data table, primarily for use in the analysis. In more technical terms, it can be termed as the process of gathering, combining, structuring, and organizing data to be used in business intelligence (BI), analytics, and data visualization applications. Data preparation is also referred to as data prep.

Importance of data preparation

Fix errors quickly; it helps catch errors before processing. After data has been removed from its source, these errors become more challenging to understand and correct.

  • Produce top-quality data; Cleaning and reformatting datasets ensure that all data used in the analysis will be high quality.
  • Make better business decisions; Higher quality data can be processed and analyzed more quickly and efficiently leads to more timely, efficient, and high-quality business decisions.
  • Superior scalability; Cloud data preparation can grow at the pace of the business.
  • Future proof; Cloud data preparation upgrades automatically so that new capabilities or problem fixes can be turned on as soon as they are released. This allows organizations to stay ahead of the innovation curve without delays and added costs.
  • Accelerated data usage and collaboration; Doing data prep in the cloud means it is always on, doesn’t require any technical installation, and lets teams collaborate on the work for faster results.

Steps in the data preparation process

Gather data

The data preparation process starts with finding the correct data. This can come from an existent data catalog or can be added ad-hoc.

Data discovery and profiling

This involves exploring the collected data to understand better what it contains and what needs to be done to prepare it for the planned uses. Data profiling helps recognize patterns, inconsistencies, anomalies, missing data, and other attributes and data sets issues so problems can be addressed.90

Cleaning

This helps;

  • To avoid data quality issues. These may include missing values, duplicate data, noise, outliers, and inconsistent data.
  • To avoid data quality issues.
  • To remove data with missing values.
  • To merge duplicate records.
  • To generate the best estimate for invalid values.

Data munging/ data wrangling/data pre-processing

Transforming data is updating the format or value entries to reach a well-defined outcome or make the data simply understood by a wider audience. This has two broad categories;

  • Feature selection; involves deciding on which features to use from the existing ones in your available data. A feature is a characteristic that might enable us to solve a problem. Components can be removed, added, or combined.
  • Feature transformation; involves changing the format of the data to reduce noise or variability or make the data easier to analyze. Two common feature transformations are scaling the data so that all the features can have the same value range and reduce dimensionality, usually the number of components of the data.

Data validation and publishing

To finish the preparation process, automated routines are run against the data to validate its consistency, completeness, and accuracy.

Store data

Once prepared, the information can be stored or put into a third-party application such as a business intelligence tool, clearing the way for processing and analysis to take place.

Conclusion

Data preparation is a time-consuming activity that can pull you away from more high-value work, especially as the volume of data used in analytics applications continues to grow. However, various software vendors have introduced self-service data preparation tools that automate data preparation methods, enabling users to discover, access, profile, cleanse and transform data in a streamlined and interactive way.