Value of Data Automation in the Cloud

What is Data Automation?

Data automation is the process of automating a repetitive manual process in the cloud. One of the most common goals of data automation is to save time, but it is also a powerful way to reduce costs and improve data quality and governance.

Data Automation Examples

Below are a few business personas with associated examples of typical manual ETL processes that should be automated. As you read through these business personas, see if you can identify members of your team who have these same pain points.

Business Analyst

Joe is a business analyst on the product team at an e-commerce company. Every Monday he runs a VBA script within Microsoft Excel to pull Sales data from their database. Then he uses Excel to visualize the results so he can analyze the product category he oversees. This process typically takes 90 minutes to run. His computer has to be turned on and connected to the VPN for the VBA script to run successfully. Joe built this VBA script over a few months period and is the only one on the team who knows VBA.      

Sales Manager

Jennifer is a manager of a sales team. Every week she downloads a sales report for the prior week from the sales management system.  She then calculates the potential leads, deals closing, and deals that have closed.

Finance Manager

Every month John downloads warranty and order data from their accounting software to calculate dollar amounts they need to write off. John uses a vlookup in Excel to identify the original orders for each warranty by product type.  

Benefit of Data Automation

Looking at the examples, it’s easy to see the numerous benefits of data automation for these businesses and teams. A few benefits of data automation include:

Reduce Cost

The cost of computing resources is a fraction of the price compared to the cost of analytics, data science, and manager salaries. Data automation can save time and money for your business by allowing you to make decisions based on the most current and accurate data. 

Save Time

Your employees will spend less time processing data and more time analyzing and making decisions from the data automated by your ETL pipeline.  

Improve Data Quality

Manual data processes have a high risk of human error, inconsistent, and delayed data. Data automation allows business users to focus on the quality of the analysis rather than the quality of the data.   

Improve Data Governance

Data Automation allows a process owned by a centralized team to maintain the business logic. This gives business users confidence business metrics were verified and used across the company.

Incorporating Automation in Your Cloud Data Stack

The goal is to incorporate this automation into your ETL/ELT layer of your cloud data stack. ETL/ELT is defined as the following:

Extract data from a source system

Transform is where you clean the data and create your analytics model

Load the data in a cloud data warehouse

With the advent of cloud solutions, the transformation and load steps can be switched, and ELT is the modern approach.

Matillion ETL is an example of a cutting-edge ETL tool.

Related: Automate Monitoring Matillion Tasks with the Matillion Task History API

How Can Data Clymer Help You with Data Automation?

Data Clymer enables organizations to access, analyze, and take action on their data by implementing modern data analytics stacks using data warehouses, transformation tools, and BI. 

Whether you are a mature business with a current cloud data stack or want to automate data processing and don’t know where to start, Data Clymer can help. We will help identify data processes that can be automated with a modern cloud data stack, enabling your employees to spend more time analyzing and making better decisions from your data.

Meet the Author

Blake Walton is a Cloud Data Consultant experienced in Python, R, SQL, full data stack implementation, data modeling, and business intelligence. Using his background in statistics and his keen ability to understand business processes, he is able to implement data stacks in a way to gain insights for both business users and advanced analytics alike.