Data automation is considered crucial for business sustainability. According to the International Data Center (IDC), the global data sphere will grow to 163 zettabytes by 2025. That’s equivalent to 163 trillion gigabytes or ten times more than the amount recorded in 2016. For a business, such exponential growth in data can become a challenge. How would you collect, clean, and put it together for data management, analytics, and automation? The whole process would be a tremendous task.
No worries, data automation comes to your rescue!
In this blog, we’ll explain:
- What is Data Automation?
- Data Automation Examples
- Advantages of Data Automation
- How to Get Started with Data Automation
- Data Automation Strategy
- Future of Automation Tools
What is Data Automation?
The definition of data automation is the process of uploading, handling, and processing data via automated tools instead of manually performing all these tasks.
It involves three common elements: Extract, Transform, and Load (also called ETL). The ETL process is categorized into three simple steps:
- Extract data from one or more sources
- Transform into the required format of the destination system by applying transformations, such as sort, filter, etc.
- Load into the target system, such as a database or data warehouse
Automating the process of data sourcing saves time and money while improving business efficiency. It also helps reduce errors through data validation and ensures that the loaded data is structured. Collecting essential business insights from your data is necessary for the company to progress in the right direction. Thus, having automation in data analytics helps business users focus on analyzing data rather than preparing it.
Data Automation Examples
In today’s fast-moving world, there are several everyday examples of automating data, such as:
- Customer support
- Desk support
- Purchase order automation
- Employee analytics
- Scheduling meetings
Now that you have understood the meaning of data automation and explored its examples, let’s discuss its benefits.
Advantages of Data Automation: Why Is It Important For Your Business?
Data Automation offers excellent incentives for a business. It is a productive and cost-effective solution for an organization as it improves work efficiency and saves costs. Furthermore, automation is also beneficial for the employees, who can focus on challenging and high-stimulating activities rather than monotonous tasks.
Furthermore, automated data management ensures consistency. Maintaining work quality is crucial for businesses, which manual processes could compromise.
Furthermore, automation helps improve data integration from multiple data sources to a single one. Here are some ways in which automated integration can revolutionize business processes:
1. Reduced Time
Let’s face it; processing vast data volumes coming in from disparate sources is not an easy feat. Data extracted from different sources vary in format. Therefore, it has to be standardized and validated before being loaded into a unified system.
Automation saves a lot of time handling tasks that form a part of the data pipeline. Plus, it minimizes manual intervention, which means low resource utilization, time savings, and increased data reliability.
2. Better Performance and Scalability
Data automation ensures better performance and scalability of your data environment. For instance, by enabling change data capture (CDC), all the changes made at the source level are propagated throughout the enterprise system based on triggers. On the contrary, manually updating tasks consumes time and requires significant expertise.
With automated data integration tools, loading data and managing CDC is just a matter of dragging and dropping objects on the visual designer without writing code.
How to Get Started with Data Automation
Ideally, sales, customers, and inventory data should be automated. But if you consider any other type of data essential for your enterprise endeavors, it’s best to include it in the automation pipeline too. This reduces the reliance on resources and makes it easier for you to maintain data integrity and quality in the long run.
Here is a checklist to help you decide the suitable candidates for data automation:
- Does the data require frequent updates?
- Does it require manipulation before uploading/processing?
- Is the data volume high?
- Is the data coming from heterogeneous sources?
In a nutshell, any data set that needs frequent updating, transformation, or manipulation, and is more significant in size, is most likely a candidate for data automation.
Data Automation Strategy: What You Should Know
The absence of a proper automation strategy can result in time and resource wastage for your company. It can also cost you more in terms of revenue loss. Hence, your data process automation strategy should be aligned with your company objectives.
Here’s a step-by-step guide to help you put your automation strategy to use.
1. Classify data
The first step in this process is to categorize source data according to the priority and ease of access. Refer to your source system inventory and identify the sources you can access. If you are using an automated data extraction tool, make sure it supports the integral formats of your business operations.
2. Outline Transformations
The next step involves identifying transformations essential for converting the source data into the desired size. For instance, it could be as easy as converting complicated acronyms into full-text names or as complex as transforming relational DB data into a CSV file. It is crucial to identify the correct transformations to get the desired results during data automation, or else your entire set of data may be erroneous.
3. Develop and Test the ETL Process
Based on the requirements of data automation outlined in the previous two steps, select an ETL tool with all the essential features required for processing or updating data while retaining quality.
4. Schedule Data for Updates
The last step is to schedule your data for timely updates. For this step, selecting an ETL tool with process automation features like job scheduling, workflow automation, etc. It ensures process execution without any manual intervention.
Keeping in mind these four steps will help develop a successful strategy for your data processes.
Future of Automation Tools
The increasing popularity of automation science has paved the way for exciting concepts in machine learning models called automated feature engineering. It’s the process of extracting features from raw data through data mining techniques.
Although automated feature engineering is a comparatively new method, it can solve several data science project difficulties using real-world data sets.
Start Data Automation with Astera Centerprise
Data analysis and data automation are imperative for the longstanding sustainability of your data-driven business initiatives. Manually handling data can increase the chances of errors, execution time, and resource investment. As we move forward, the need for quality data for business reporting will only increase. Businesses are relying more on data quality today. It can’t be recorded through manual processes, and automation solves this problem once and for all.
By eliminating the repetitive manual tasks, data automation streamlines your business processes and enables you to focus on business growth. Automated data analysis further helps business users make critical business decisions promptly in real-time.
Astera Centerprise allows data automation through job scheduling. You can easily create data maps and automate them on events/triggers/actions, such as file drop, email, or value change. Learn how this automated solution can help you extract quality insights for business improvement.