Data transformation is often a key staple in companies who are looking to organize their data, while also building a foundation for other data professionals and business professionals to access that same data. At its core, data transformation is the process of extracting, transforming, and loading (ETL) data into a company system.
The first step is to copy data from an outside source that is accessible within your company. As the data is usually messy or in a non-standardized format, you will need to transform it by restructuring the data. The final step is to load it into a location that is easily usable for its users. Below, we will discuss this process in more detail, the benefits of this process, as well as the necessary tools to make it all the easier.
What is Data Transformation?
Before we get into ETL (extract, transform, load), we must acknowledge that there is also ELT—extract, load, and transform. Therefore, you can either apply data transformation before data warehousing or after.
An example of ETL is to call upon an API (Application Programming Interface) to obtain data, followed by parsing out those results into different structures for them to be loaded into a data lake or database table. The reason you would want to transform your data before data warehousing is because there’s no need for data staging, meaning that you can focus on extracting followed by loading first. You can then structure your data after the fact, which can be performed in a variety of ways through SQL transformations.
Depending on if you or your company employ ETL or ELT, we can still distinguish the main themes of each step in the process below:
- Extract: Obtaining data from outside sources
- Transform: Restructuring the data to fit the needs of the company
- Load: Inserting data into a location that is usable for multiple types of users
Why is Data Transformation Necessary?
Data transformation is necessary for a variety of reasons.
A company benefits from having available, accessible data—both at the core of its products as well as for analytics of those same products. Think about a prominent company that sells goods where multiple features are required to sell that product (as well as perform analysis on).
Having usable data that has been transformed means a business intelligence analyst or data scientist can analyze that data for insight or use that data to build a model.
For example, a data analyst can query tables that have been structured in a columnar format, while a data scientist can create a query that will be the foundation of the dataset that is used in an important machine learning algorithm.
Moreover, with a tool like Kloudio, non-technical business users could access this data, too.
With that being said, here are some of the main reasons data transformation is necessary:
While it can be beneficial to have static CSV sources to perform analytics on, a company truly needs a process that is highly scalable.
Running scripts that automatically ETL or ELT can save your company the time and energy required of the data transformation process and let them focus on the data itself, as well as its infinite benefits.
The reason scalability is so important is that data preparation and time spent discovering it can take 80% of your analyst’s time. Having a scalable way in which to consolidate your data is extremely beneficial to a company because it will allow more time for analysts to analyze the data, rather than prepare it.
This benefit, in turn, leads to several other benefits like better quality of data, more consistent data, and greater accuracy of data consolidated, as mentioned in one of the top benefits of data warehousing.
When you are loading data from multiple sources, data security is a must. Sensitive data needs to be protected by methods like anonymizing data when transformed, or by removing direct relationships of data from the same table.
This benefit of data security is a product of the ETL and ELT processes.
After the ETL or ELT process has been executed, users can access data that has a relational connection to other tables. This structure makes it easy for users to query data from separate tables (or applications, with a tool like Kloudio).
Data is often ignored in companies to the point where less than 1% of a company’s unstructured data is used. With the use of data, better, more efficient decisions can ultimately be made.
Data Transformation Tools
Data integration is performed from tools that provide data access with no complex methods or coding. There are a variety of data transformation tools like the following, which can be performed with some of the Kloudio integrations as mentioned below:
1. Spreadsheet Integration and Data Reporting Tools
Most users, like product managers and analysts, can benefit from this method that includes utilizing tools like Excel, Google Sheets, and Airtable. With Kloudio, you can process, organize, and act on your data—right within your favorite spreadsheet tool.
2. Data Integration Tools for SQL Databases and Data Warehouses
Users like data engineers, data warehouse developers, and data scientists can integrate with these common platforms like PostgreSQL, Amazon Redshift, and Snowflake
3. Web Application and Cloud Data Integration
Business analysts can benefit from this integration type that includes platforms like Jira, Facebook Ads, and Salesforce
As you can see, data transformation tools vary far and wide, and it’s important to note which data integrations are necessary for your data team and transformation needs.
Over to You
Data transformation is a hot-button topic for many companies, and the reasoning is clear: there are countless benefits to transforming data, as well as several tools for integrating a transformational process seamlessly.
You do not need to be a data engineer to know the importance of data. Allowing your company to have available data, whether you perform ETL or ELT, is an integral step in creating a business powered by data and insights.
See how Kloudio can help you streamline your data transformation.