Automated data processing: how it works, and why it matters

For most companies, the days of frantically collecting as much data as possible are in the past. The real challenge nowadays is ensuring information is reliable. But combing through all that data manually is both time-consuming and resource-intensive — that’s why so many data-driven organizations have turned to automated data processing. 

Collecting, cleaning, transforming, and analyzing information automatically helps you make data-backed decisions faster and frees your team up to work on what matters most to your business.

In this guide, we lay out what automated data processing is, discuss its benefits, and share some strategies you can employ within your own organization.

What is automated data processing? 

Manually sifting through data can be slow and painstaking, and even the best analysts make the occasional mistake. Automated data processing removes the fallible “human element” and shifts repetitive tasks into systems that operate programmatically and without the need for ongoing intervention. 

Replacing manual workflows with software-driven automation makes the whole process of data collection, preparation, and migration faster and more accurate. It's an indispensable tool when dealing with large, continuously growing datasets, powering both automated data management and automated data integration. 

What are the key components of automated data processing?

While your specific approach will depend on your organizational needs, most automated data processing environments share a few key elements, including: 

• Data ingestion pipelines: Pipelines extract data from source systems and make it available downstream. These systems rely on schedules or certain triggers, allowing data to flow continuously. 
• Validation and cleansing mechanisms: Automated environments include processes that automatically flag errors and inconsistencies. They’re also able to omit information before it’s integrated into the dataset. 
• Transformation and integration workflows: By converting raw data into usable formats and models, automated data processing enables integrations with various systems and datasets.
• Monitoring and governance tools: Data processing dashboards track whether automated processes are running as expected and identify misfires when they occur. 
• Automated data processing software: Software platforms coordinate ingestion, transformation, monitoring, and error handling through the data pipeline. 

Benefits of data automation

The most important impact of automation isn’t just the speed of delivery, but an increase in data quality. By removing the potential for human error from the process, you ensure repetitive steps like validation, formatting, and required updates are carried out the same way, every time. 

This consistency can unlock significant downstream benefits. With cleaner, more reliable data, teams can make decisions faster and avoid the delays that often come with second-guessing. 

It’s also far easier to scale your infrastructure. Without the need for human intervention, systems can handle larger and larger volumes of data, helping you grow your operations without additional labor hours or oversight. 

Automated data processing strategies

Your specific automated data processing strategy will depend on how you collect information and how quickly you need to process it. Here are a few of the most common approaches:

• Batch processing: Data is grouped into sets that are processed at scheduled intervals. This approach works well for large volumes of data, where urgent availability is not a critical factor. 
• Stream processing: Instead of waiting for information to accumulate, data is processed as it’s generated. This approach is commonly applied to live dashboards and other system monitors, where incoming data needs to be observed quickly. 
• Scheduled processing: Information can be processed at predefined times, providing predictable execution for recurring tasks. This method is typically used to run daily reports or other dashboard updates after business hours. 
• Event-driven processing: Rather than running on a fixed schedule, the process can be triggered by a specific event or change. For example, when a customer places an order.

Automatic processing examples

Different industries deploy automated data processing in different ways. Here are a few examples to help you visualize how you might deploy it within your organization.

Personalized marketing automation

Marketing teams often rely on automated processing tools to keep customer data accurate across systems. These platforms enable automatically personalized workflows that respond in real-time to customer behavior. 

Logistics and operational data synchronization

Operations professionals use automated data integration to keep logistics systems aligned. Product and order updates or changes to inventory levels are instantly reflected in internal systems, ensuring teams are always working with the most up-to-date information available. 

Customer relationship management (CRM) dashboard updates

Support staff can automate CRM dashboards, so they always show up-to-date lead or customer information. For example, a member of a sales team could record a new lead in a CRM, and those changes would automatically flow into reporting tools and other lead and engagement-tracking dashboards. 

Fraud detection

Finance teams deploy automatic data processing to analyze transaction data as it’s generated, allowing systems to flag unusual patterns and potentially fraudulent activity. This process surfaces issues earlier and far more consistently than manual detection efforts. 

How to implement automated data processing

Putting an automated data processing system in place starts with a deep understanding of your existing systems and approaches. Here are a few things to consider:

• Consider current workflows: Start by assessing how your organization currently manages data, including information collection, migration, and maintenance. This will help you identify potential bottlenecks and other points of friction. 
• Identify automation opportunities: Keep an eye out for processes that you could easily automate. This might be a task with a predictable workflow or a scheduled data transfer that could instead be triggered when a specified event occurs. 
• Choose the right software: Pick a platform that will reliably support your specific business needs. For example, extract, transform, and load (ETL) tools are indispensable when you need to standardize data processing across your systems. 
• Integrate with existing systems: Connect automation data processing tools to your current data sources and destinations, integrating them across all relevant entry points. 
• Monitor and optimize: Check in with your system’s performance frequently, tracking whether data is flowing as expected and for recurring errors. This ongoing attention will help you refine your processes and see maximum benefits. 

How Fivetran supports data processing workflows

Manual data processing and maintenance are costly and time-consuming processes that only get more complicated as you scale. Automation enables the collection, transformation, and analysis of vast amounts of data while eliminating the potential for human error.

Instead of relying on custom scripts, manual workflows, or complex orchestration, Fivetran uses pre-built connectors to extract data from a wide range of useful sources automatically. The platform also supports flexible scheduling, granting you control over how frequently data is synced. Fivetran Transformations also let you prepare data for analysis within your data warehouse directly, employing SQL instead of relying on external processing tools. 

See how Fivetran can help you streamline your data processing through automation by requesting a demo today.

FAQs

What’s the difference between automatic and controlled processing?

Once rules and triggers have been defined, automatic processing runs data workflows without the need for ongoing human involvement. Controlled processing instead builds manual reviews and interventions into specific stages of the data cycle, helping ensure accuracy and compliance. 

What are the best automated data processing methods?

The right data processing method will always depend on the specific circumstances of your business. To get started, evaluate how quickly you need to process your data and how you’re currently using it. Common approaches include batch processing for scheduled workloads or ETL-based pipelines that standardize data transformation and delivery. 

What is automated data preparation?

Automated data preparation uses automation software to clean, validate, and transform raw data before analysis. The approach helps to keep data reliable while avoiding the need for repeated manual formatting or checks. 

What is automated data entry?

Automated data entry captures data from sources like forms, applications, or system events using automation software. By reducing manual input, this solution improves organizational accuracy and speed. 

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