Published: 9 August 2024
Contributors: Teaganne Finn, Amanda Downie
Data automation is a data management technique used by organizations to store, process and analyze data by using technology tools and software. Organizations are striving to find more efficient and effective data analytics and business processes as the amount of business data increases.
This increasingly popular technique can automatically collect, process, transform and analyze data without the need of human intervention, and as a result can streamline critical workflow tasks. While it isn’t a one-size-fits-all solution, data automation can perform a range of jobs that are critical to data quality, data governance and data processing capabilities.
Separately, data automation can automate repetitive tasks and time-consuming tasks and optimize the way a business handles its data storage. Tasks such as data ingestion, transformation, validation, cleansing, integration and analysis can all be automated under this technique, helping organizations get the best value from their data and enhance data-driven decision making.
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The main purpose of data automation is to create an efficient data pipeline that seamlessly works from start to finish to store and manage a business’ data warehouse. The overall goal is to be able to move data from various sources and then transform that data into usable information.
The data automation technique has become even more critical as data sources and data types continue to grow, and organizations must harness the proper tools to help them transform that data in real-time. Data automation can be used on different data sources including internal and external databases, cloud-based data sources and data that originates from third-party applications and APIs. There are several technologies that can be used to implement data automation, including as Robotic Process Automation (RPA), artificial intelligence (AI) and machine learning (ML).
The three main elements of data automation are Extract, Transform and Load, also referred to as ETL. The traditional methods of ETL required data collection done manually. This is a tedious and time-consuming job that can be prone to human error, and that’s why businesses are starting to shy away from the manual processes and repetitive tasks. ETL can be automated with no-code, low-code or full-code.
Extract: This is the first step in the ETL process and involves data extraction from different sources. The data might be from databases, web services or third-party apps, among others in the aggregating process.
Transform: This refers to the data transformation step in which raw data is converted or modified to a format that is the same for the entire data set in the system. In this step the data is cleansed and mapped to meet business needs.
Load: This is the final step in ETL and refers to the loading of the transformed data into the target database. The complexity of this step depends on the requirements of the application.
A data automation process has the potential to generate data reports, valuable dashboards and visualizations depending on which tools the business chooses to use. A data automation strategy requires teamwork and patience if a business hopes to reach its databases full potential. It requires the entire data team and data scientists to be open to the transformation.
There are several data automation techniques to choose from depending on your business’ needs. Some of the most common types include:
Data integration
This is one of the more crucial parts of data automation. This process involves identifying and taking different data sources, and then readying them for a central database. In this step the data is cleaned and validated and ETL mapping will occur so all data flows to specific repositories.
There is more than one method for data integration; however, there are specific steps that stay true in all data integration processes, including the use of various data sources, master nodes and users accessing data from master nodes.
Benefits of effective data integration might include increased efficiency (time saving), greater reliability and higher-quality data delivery.
Data transformation
For data to be added to a target repository, it must be converted into a format fit for analysis. This can require data transformation automation to come into play and use tools to convert data from one form to another. An example is unstructured text to structured data and transforming it into a consolidated form.
The consolidated data should be done in a systematic process so that when one change is made to one table it impacts all related tables. There is an order of operations to follow; any task should be done based on how time-consuming or error-prone it can be, starting from most bothersome to least.
Benefits of effective data transformation might include more efficient data management and organized processes.
Data loading
In data loading, the data is cleansed and transferred into a data warehouse. Once it’s in this form, it allows for processing data more efficiently and eliminates the need to transfer data back and forth from one study to the next. Data loading becomes more or less relevant depending on the volume of data to be analyzed.
Once in the database management system, data can stay there and be in the proper format without the need to reload. Automating this process gives businesses greater real-time collaboration and allows for multiple users to access the same dataset without having to comb through multiple versions of it.
Benefits of effective data loading might include streamlined data processing and easier collaboration from one user to the next.
Data analysis and visualization
This process occurs after the datasets have been transformed and involves statistical testing to identify relationships and patterns. This analysis step—and its success—depends on the insights drawn from business intelligence tools and then using those results to inform actions that make a meaningful impact.
Data visualization can streamline the decision-making process by thoroughly analyzing those datasets and providing insights that may not have been discovered without such automated analysis.
Benefits of effective data analysis can include streamlining the analytical process and providing insights quickly.
When done effectively, key benefits of data automation include:
- Enhanced analytics speed
- Improved accuracy
- Better decision-making
- Increased cost savings
- Improved data security
- Bigger competitive edge
Enhanced analytics speed
The limited amount of human intervention allows for data scientists to perform analytic tasks quickly. Automated processes also allow for computers to handle the complex and time-consuming tasks that humans may find challenging.
Improved accuracy
Data automation can mitigate and/or help with human errors in data entry and improve overall productivity. The automated data entry can lead to more accurate metrics and insights.
Better decision-making
With real-time insight into data, an organization can make better decisions that are more aligned to its needs.
Increased cost savings
Computing resources are oftentimes more cost-effective for data analysis tasks and can save the business time and money.
Improved data security
Automation technology can implement strong security measures to protect the organization’s data, such as enhanced access controls and encryption.
Bigger competitive edge
Access to real-time insights can set an organization ahead of their competition and help them make more informed decisions.
There are several data automation tools to choose from depending on your business’ needs. Some of the most common types include:
Before choosing a data automation solution or altering an organization’s data operations it’s important to make a plan that fits within an organization’s data management approach. Data automation is one aspect of the broader approach to a technology driven enterprise data management strategy. Below are the steps to use when developing a data automation strategy.
Prioritize data processes for automation
Rank and evaluate which data processes are most time-consuming. A good place to start is pipelines, which are frequently running and involve a number of manual steps that might be automated. This results in freeing up data engineers for other complex or strategic duties.
Identify specific tasks to automate
Once the organization has decided which process to automate, the next step is to look at the manual tasks in each process and decide which task should be automated first. Take account of each task and decipher which might be more complex and which might be simpler to approach.
Choose automation tools wisely
While an organization can understand the specific requirement of your process, it’s important to find the right data automation tool to use. Select a tool based on its use cases, pricing, security and capabilities beyond just what the process requires.
Stay on top of trends
The technology landscape is always evolving, especially in data automation. Some of the key trends that are likely to gain more traction are the inclusion off artificial intelligence (AI) and machine learning (ML) technologies. While these technologies are already being implemented, the algorithms are getting more sophisticated as time goes on.
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