Data automation is considered crucial for business sustainability. According to the International Data Center (IDC), the global data sphere is expected to 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 all together for data analytics and automation?
That’s a lot to deal with, isn’t it?
This is where data automation comes to your rescue.
In this blog, we’ll explain what data automation is and how it can help improve the efficiency of your integration processes for automated data analytics. Plus, we’ll look at which type of data requires automation and a quick step-by-step guide to help you get started with a data automation strategy using appropriate process 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 process can be 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 not only saves time and money but also improves business efficiency. Data automation also helps reduce errors through data validation and ensures that data is loaded in a structured format. Collecting important business insights from your data is necessary for the company to progress in the right direction. Thus, having an automated data analytics process would help 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 different examples of automation, let’s discuss the benefits of data automation.
Advantages of Data Automation: Why Is Data Automation Important For Your Business?
Data Automation offers great incentives for a business. It is a productive and cost-effective solution for an organization. Enterprise can benefit by saving costs and increasing work efficiency by automating some of their processes. This could also be beneficial for the employees, who can focus on challenging and high-stimulating activities rather than doing repetitive, boring tasks.
Furthermore, data automation ensures consistency. Maintaining work quality is crucial for businesses, which could be compromised by carrying out manual processes.
Data Automation Improves Integration
Data automation helps improve the integration of data from multiple data sources to a single one. Here are some ways in which it can revolutionize business processes:
1. Reduces 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. It has to be standardized and validated before being loaded into a unified system.
Automation saves a lot of time in 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 data tasks consumes time and requires significant expertise.
With automated data integration tools, loading data and managing CDC simultaneously is just a matter of dragging-and-dropping objects on the visual designer, without writing any 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, include it in the automation pipeline too.
This is because automation 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 right 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 greater in size, is most likely a candidate for data automation.
Data Automation Strategy: What You Should Know
Without a proper data automation strategy, your company will deviate from the right path and this can result in time and resource wastage. 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 data automation strategy to use.
1. Classify data
The first step in data automation is to categorize source data according to the priority and ease of access. Refer to your source system inventory and identify the sources that you can access. If you are using an automated data extraction tool, make sure it supports the formats that are integral to 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 difficult 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 that has 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, it is recommended to select an ETL tool that has process automation features like job scheduling, workflow automation, etc. This ensures process execution without any manual intervention.
Keeping in mind these four steps will help develop a successful strategy for your data automation process.
Future of Data Automation Tools
The increasing popularity of automation data science has paved the way for an interesting concept 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 has the potential to solve a number of data science project difficulties using real-world data sets.
Start Data Automation with Astera Centerprise
Data analytics and automation is 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 is something that simply can’t be recorded through manual processes and data 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 analytics further helps business users in making 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. For example, a file drop, an email, or a value change. Learn more about how this data automation tool can help you extract quality insights for business improvement.