The Time Consuming Part of Data Analysis
Data preprocessing is a crucial step to prepare raw data, which is often messy and incomplete, for further analyses. This step is often reffered to as ETL (Extract Transform and Load). During the ETL process data is converted into a more usable, standardized, and consistent format, resulting in more accurate and reliable visualizations. In this blog post, we will delve into the steps needed to be taken in data preprocessing along with some of its benefits and challenges.
Step 1: Data Cleaning
Data cleaning involves steps like finding discrepancies, handling duplicates, and removing incorrectly formatted data. Values may be missing from the data sets, which requires data re-pulling or adding from other data sources. Data cleaning methods are customized based on specific dataset and analysis goals.
Step 2: Data Integration
Data integration aims to unify all datasets into one dataset, providing a unique source of truth that enables more comprehensive and insightful analyses. This step can often be challenging because each source of data has different schemas and structures. To perform data integration, we need to identify some common fields between the datasets and combine them based on those fields. There are multiple merging techniques that can be done depending on the relationship between datasets such as left join, right join, full outer join, and inner join. Another data cleaning round is needed after a unified view of the data is created to address issues with redundancy and inconsistency.
Step 3: Data Transformation
Data transformation converts information into more usable and organized formats. In this step, we often perform numerical calculations and reformat texts into cleaner values to ensure more efficient downstream processes.
SaaS Tools
There are countless powerful SaaS tools and softwares that are reducing the time it takes to clean and process data. No one tool is right for everyone.
Here are some of the most popular:
Oracle Data Integrator
AWS Glue and Data Pipeline
Azure
Stitch
Fivetran
Reach out and we will help you determine what tool(s) are best for your organization, you too can unlock the full potential of your data.
Author: Tram Nguyen