What is data automation?

Many organizations rely on data automation as a key component of their data management strategies.

The IBM Data Differentiator reports that as much as 68% of organizational data never gets analyzed, meaning the business never realizes the full benefit of that data.

Automation helps businesses improve operational efficiency and process growing volumes of data so they can extract valuable insights and make faster, better-informed business decisions.

Specifically, data automation can help streamline the ETL process that data must often undergo before a business can use it. ETL includes extracting data from its source, transforming it into a usable format and loading it into the target app or database.

By eliminating time-consuming, repetitive tasks that used to require manual intervention, data automation technologies free data engineers and data scientists to focus on higher priorities, such as data analysis and artificial intelligence (AI) and machine learning (ML) projects.

Data automation also improves data quality by minimizing the possibility of human error during data processing.

Data automation is important for businesses that must process, analyze and act upon rapidly expanding data volumes from multiple data sources. Roughly 402.74 million terabytes of data are generated every day, much of it in raw or unstructured formats that are difficult for IT systems to read without data processing.¹

Businesses require clean, accurate data for a wide variety of use cases, including operations, supply chains, marketing and sales, corporate governance and more. Today, as many businesses start artificial intelligence (AI) initiatives, even more massive amounts of data are needed to train large language models (LLMs).

Before data automation, processing data was complex, labor-intensive and prone to errors. Data workflows such as data collection, data preparation and data integration relied on hand-coded scripts that had to be created, maintained and frequently updated. Different data sources required custom coding to make them compatible with other parts of an organization’s data pipeline.

Automated data processing tools can provide a no-code solution to these issues. Businesses that adopt a data automation strategy can reduce processing time, increase worker productivity, improve data quality and analyze more data faster. In an age of AI and big data analytics, data automation is considered an essential capability.

Data automation works by establishing a data pipeline that automatically collects data from various sources, processes the data for use and delivers it to the repositories and tools that need it.

Data sources can include databases, web applications, application programming interfaces (APIs), cloud services and many other different sources. The final destination of the data might be a data warehouse, analytics application, business intelligence tool or an AI or ML model.

As the data flows through the data pipeline, different automation technologies work together to complete each step.

For example, data connectors can retrieve data from any source without the need for custom code or manual intervention. Robotic process automation (RPA) can perform repetitive tasks such as locating specific data in a spreadsheet or an invoice and moving it to an application.

Artificial intelligence and machine learning are also important technologies for data automation. They can automate complex data entry tasks, perform sophisticated data transformations and automatically adapt data processing parameters when circumstances or business needs change.

One of the primary methods for processing data sets for use is known as ETL, for extract, transform and load. Data automation helps streamline these and other key steps of the data management lifecycle:

• Data integration: Bringing disparate data stores together.
• Data extraction: Collecting raw data from various sources.
• Data transformation: Transforming data for its intended use.
• Data loading: Moving transformed data to its target location.
• Data analysis: Generating insights from data.

Data integration is the umbrella term for collecting, combining and harmonizing data from multiple sources into a unified, coherent format that can be used for various analytical, operational and decision-making purposes.

Data integration involves a series of steps and processes including data extraction, data transformation, data loading and data analysis, which are described below.

Raw data is copied or exported from various sources, such as SQL and NoSQL databases, web applications, APIs, cloud services and spreadsheets. The types of data extracted might include both unstructured and structured data formats, such as JSON, XML, relational database tables and more.

Automated data extraction tools can recognize and extract data from these disparate sources without the need for human intervention or custom coding. They can locate and retrieve specific pieces of information within large volumes of unstructured data, such as business documents, emails or web pages. Some extraction tools can even work with handwritten text and low-resolution images.

Data transformation is a critical part of the data integration process in which raw data is converted into a unified format or structure. Data transformation helps to ensure compatibility with target systems and enhances data quality and usability. Depending on its destination, data can undergo multiple transformations to prepare it for use.

Data automation tools can perform data transformations such as cleaning data to remove errors and inconsistencies, reformatting data such as removing columns from a spreadsheet and aggregating data by combining multiple records. Automation tools can also enrich data by adding relevant information from other sources.

When the data is transformed, it is loaded into its target destination, which is often a data warehouse, analytics app or other tool that enables users to access and work with the data. Typically, this process involves an initial loading of all data, followed by periodic loading of incremental data changes and, less often, full refreshes to erase and replace data in the warehouse.

Automation tools can schedule data loading to take place automatically based on time intervals, such as once or twice a day. They can also initiate data loading when triggers are activated, such as when new data is added to storage or a document is updated. Some tools can also automatically generate custom code to properly load different types of data assets.

After extraction, transformation and loading, the data is ready to be analyzed to uncover trends, patterns and correlations to help businesses make data-informed decisions. Data automation tools can perform many data analysis tasks automatically to help data scientists work faster and more effectively.

Automation tools can encode or convert data into a numerical format, split data into subsets, isolate variables, impute missing values, and generalize large data sets into high-level abstracts. For business users, data automation can create data visualizations to help them understand and take advantage of data-driven insights.

The key benefits of data automation include:

• Increased efficiency
• Reliable data quality
• Cost savings
• Faster business insights
• Faster decision-making
• Stronger data security
• Flexible scalability

Moving and processing massive amounts of data through a data pipeline can be a complex, time-consuming process. Automating the many tasks across the data pipeline dramatically simplifies and speeds processing time.

Removing human intervention from the processing of large volumes of data also removes the possibility of human error. Data automation tools can also perform data validation to prevent errors in data and maintain consistency with business rules.

Data automation eliminates the expense of employees spending time and effort on data processing tasks. For example, automation tools can help with data entry, fixing errors and formatting data to make it compatible with other systems and tools.

By automating analytics tasks that used to require manual intervention from data teams, data automation accelerates the discovery of data-driven business insights, often in real time.

Faster business insights mean that businesses can make real-time, data-driven decisions to seize new opportunities, improve the customer experience and mitigate the risk of acting without understanding potential consequences.

Data automation tools can help protect data during processing by automatically encrypting sensitive data, authenticating and auditing data to comply with regulations and restricting access to data sources.

As data volumes grow and business processes evolve, data automation provides organizations with the ability to scale data processing efforts while maintaining performance requirements.