Data cleaning refers to removing data from multiple databases so it can be processed and analyzed. Many different types of data exist, and it can be difficult figuring out what to do when it comes to cleaning them up. Power Query for Excel helps users cleanse their databases by performing various data transformations.
Joins – You can use joins to combine data sets from different sources or join tables from two or more different data sources. You can join data based on a column value, row value, or a unique identifier such as a primary key or foreign key. You can also combine several columns into a single column using Power Query’s function-based “union” operator. Conditional Joins – When working with many database tables, you may find it necessary to join only specific tables that meet certain criteria. For example, you may want to join only sales records from last week’s order entry database to last month’s production records from a manufacturing database. With conditional joins, you can specify selection criteria like date ranges or date ranges within another table to join only those records that match the criteria.
Text Transformation – Cleaning up text values is a common requirement when combining datasets from different data sources. You can convert text strings to numbers, dates, or dates with times. You can also standardize names and email addresses with functions that extract numeric values from names or email addresses and perform lookups on those values against known standards such as country codes or postal codes.
Column Extraction – Columns can be extracted from one or more tables and joined to other tables to create a dataset that may include new columns. For example, a column can be extracted from a purchase order table and combined with the item name and quantity purchased to create an itemized bill of materials for use in estimating the cost of future orders. Once you’ve cleaned your data, you need to store it somewhere where it can be analyzed and queried again later. A data warehouse is a relational database where data is stored in tables and the relationships between the tables are defined. There are three types of data warehouses: Operational Data Stores (ODS), Business Intelligence Platforms (BI), and Enterprise Data Warehouses (EDW).
As the amount of data increases, it’s harder to access, understand, and analyze it to find valuable insights that drive business decisions. You need tools that allow you to easily access and analyze data so that you can make better decisions for your business.