6 benefits of data pipeline automation

Every transaction, decision and interaction within your enterprise relies on the integrity and reliability of data. When it flows seamlessly from one point to another and is consistently accurate, you can rest easily knowing you’re protecting your business and customers.

Yet, data volumes are skyrocketing, and the need for real-time data processing is more pressing than ever. The business intelligence that fuels your next move depends on it, and your customers expect quick and reliable service.

Safeguarding your assets, reputation and future, therefore, means prioritizing data pipeline management and, in turn, the files you transfer in your data processes.

Why automate data pipelines?

The concept of a data pipeline may be simple — it’s the system or process you develop to move your data from various sources to destinations. But, establishing and maintaining steady and precise data movement requires constant attention. 

As the amount of data created, consumed and stored continues to expand dramatically and workflows increase in complexity, the pressure exceeds what a typical business can maintain with manual methods. Timely processing and error mitigation are not guaranteed when trying to piece together the capabilities of disparate tools.

Furthermore, delivering a superior customer experience (CX) in any industry depends on real-time data availability.

Scaling data pipelines to meet demands and stay competitive becomes impossible without automation.

Benefits of data pipeline automation

1. Increase efficiency and productivity

Automation eliminates repetitive manual processes, allowing you to better utilize human resources for your most important strategic tasks. A simple shift in how you apply your workforce can drive innovation and greatly enhance your service delivery. 

When someone who once dedicated a significant portion of their time to data entry, validation and transfer gets to focus on more creative work, for example, you could develop fresh solutions to internal and customer-facing issues while accelerating project timelines.

In action: A manufacturing company reduces data processing time by 40% by automating data management tasks, enabling data engineers to focus on product innovation instead of time-consuming data handling tasks like manual data ingestion and validation.

2. Improve reliability and reduce errors

When you automate data pipelines, you mitigate mistakes. The best data orchestration and workflow management solutions have built-in error detection and correction mechanisms to improve data quality and consistency. They monitor data flows around the clock to identify anomalies and correct issues in real time.

As a result, your teams can achieve accurate reporting and maintain regulatory compliance in the decision-making process. Reliability ultimately translates into trust — in both your datasets and your systems.

In action: A financial institution achieves 99.9% data accuracy by automating its data pipelines. Its leaders can produce reliable reports and stick to important industry standards around data security.

3. Enhance scalability and performance

As you implement automation with powerful job orchestration tools, you’ll find managing big data spikes and variations in data loads is no longer stressful. Optimizing resource usage improves your overall system performance and can reduce costs.

If, for example, your business experiences a surge in customer transactions during a major sales event, it could be risky to try to handle the increase in data volume without a snafu. Automation helps you maintain a smooth and efficient CX and generates accurate numbers on the back end.

In action: A hotel chain scales its data pipeline to accommodate a 200% increase in booking data during peak seasons.

4. Provide visibility and monitoring

Automated data pipelines offer comprehensive data flow and system performance tracking. The best platforms offer clear, accessible insights into your pipeline operations so you can preempt issues. Visibility is key for operational integrity.

Especially with real-time dashboards and detailed analytics, you get a transparent view of your entire data pipeline, including where you may have bottlenecks before they escalate. The same level of business insights isn’t attainable in a manually-driven pipeline. 

Proactive monitoring is also invaluable for the health of your data infrastructure.

In action: A utility company uses dashboards for real-time monitoring and reduces system downtime by 30% to ensure uninterrupted service delivery.

5. Simplify workflow management, scheduling and dependency handling

Automation simplifies complex workflows and scheduling, so it’s easier to coordinate data-related tasks, file transfers and other key actions across your entire organization. By facilitating the integration of various data sources into a central data warehouse, automation also encourages consolidation and removes data silos.

With automated scheduling, you can ensure your data gets processed and delivered at the right time for every stakeholder. Managing dependencies between different data processes becomes more straightforward in automated workflows. These simplified IT and operations tasks make it possible to interweave various business processes with less effort.

In action: A food processing company improves workflow efficiency by 50% through automated scheduling of production and distribution data, resulting in more timely deliveries.

6. Enhance fault tolerance with built-in detection and recovery

Your pipelines will always be at risk without fault detection and recovery plans. Data pipeline automation tools are made to minimize downtime and data loss. They offer automated alerts and notifications to minimize response time.

Resilience is crucial for maintaining uninterrupted service delivery and protecting the integrity of your data.  Fault tolerance keeps your data secure in the face of unexpected events.

In action: A retail company reduces system downtime by 25% with automated fault tolerance in its data pipeline. The outcome? Consistent customer service and operations.

Steps to effectively manage data pipelines with automation

Achieving the benefits of data pipeline automation requires a strategic and thorough approach.

The first step is to assess your current data movement processes. Are some of your data transfers reliable while others are inconsistent? An initial assessment can give you a clear picture of where your data practices stand and help you identify areas for improvement.

Once you have a comprehensive understanding of your current state, the next step is to identify your goals. Your objective is to ensure you can support all business functions with secure and consistent data movement protocols. 

This involves defining specific targets such as:

• Reducing error rates
• Improving data processing speeds
• Ensuring compliance with regulatory requirements

Having clear goals can help you formulate a precise action plan and tangibly measure your success.

Finally, transitioning fully to an automated data pipeline system means investing in workload automation (WLA) software with integrated managed file transfer (MFT). MFT can ensure all file transfers are secure and compliant. Whether you’ve been engaging in data streaming or store-and-forward methods of file transfer, a tool with integrated MFT can add a layer of reliability to your use cases.

➡️ Consider that a WLA solution can often be used to automate extract, transform, load (ETL) processes. These are fundamental for proper data integration, which keeps your data up to date across all systems.

The future of data movement

As multi-cloud environments become more prevalent, increasing data volume and complexity will drive an even greater need for easy-to-implement low-code or no-code WLA as a proactive approach. Your data pipelines are some of your most valuable assets and, managed well, they can pave the way for sustained growth, increased customer satisfaction and other positive business outcomes.

To dive deeper into what intentional data pipeline management with MFT solutions could look like for your organization, read Data in Motion, our in-depth report on enterprise data movement. Learn about the impact of multi-cloud environments, workload automation, data volume and complexity and more on IT leaders’ data movement strategies.