Automic Automation: The Key to Unlocking Data Pipeline Accuracy

Data pipelines are now indispensable to data management. By streamlining data collection and analysis processing, data pipelines provide the relevant, reliable information organizations need to make informed and agile decisions.

However, when implemented incorrectly, a data pipeline may dispatch data that’s inconsistent, irrelevant, and inaccurate. According to the research and consulting firm Gartner, “bad data” costs businesses an estimated average of $12.9 million per year.

If your organization contends with “bad data,” Automic® Automation delivers the solution—streamlining business operations, providing automated workflow solutions, and helping accelerate big data and AI initiatives.

So, let’s dive into data pipeline optimization, the issues organizations typically face when implementing new data pipelines, and how Automic Automation helps businesses unlock their data’s potential.

What is a data pipeline?

The term “data pipeline” effectively portrays data transportation, but that only captures some of the important steps in this process.

Data pipelines comprise a series of continuous, digital steps:

• Acquire and gather data from disparate sources.
• Port suitable data to a data lake or data warehouse.
• Clean, enrich, and modify data in preparation for analysis.
• Process data to extract meaningful insights, patterns, and other information.
• Deliver data to the appropriate personnel and locations.

In theory, the process sounds simple enough. And yet, the process of implementing and managing new data pipelines has proven notorious for introducing complications.

What are the most common challenges in data pipelines?

The more complex your IT environment (e.g., data silos, hybrid cloud, or multi-cloud infrastructure), the more common it is to face challenges with preparing data for processing.

• Implementing and managing new data pipelines can lead to several issues:
• Duplicate data
• Missing data
• Incomplete reporting
• Scalability issues
• Integration dilemmas

One of the biggest problems organizations face, though, is that data is generated from too many sources and stored in too many silos.

Put simply, this can cause data to become fragmented and difficult to gather, often leading to data that is inconsistent, inaccurate, or delayed, or that lacks quality and integrity. Accordingly, issues in your data pipeline can derail your team’s ability to make sound and knowledgeable business decisions.

What are the ramifications of data pipeline inaccuracies?

If your environment is already fragmented across hybrid and multi-cloud, then accelerating poor processes and bad data deliveries only results in messier outcomes. For example, if stores report sales hourly and you do not detect that a large store has stopped sending data, then incorporating this data to determine stock replenishment will produce misleading results. The consequences of using inaccurate data from your data pipelines can range from minor to devastating. For example, “bad data” may result in:

• Operational inefficiencies
• Security risks
• Prediction errors
• Lost productivity
• Reduced income
• Missed opportunities
• Monetary fines

How does Automic Automation enhance data pipeline accuracy and improve operational efficiency?

Automic Automation helps organizations revolutionize how they leverage data and workload automation across diverse environments. Automic Automation’s data pipeline orchestration capabilities help boost your data pipelines’ accuracy and efficiency, so you can achieve the following objectives.

Bridging technology and functional silos

Automic Automation equips you with the power to bring cohesion to your organization’s teams, tools, and infrastructure. This effective collaboration between “people, processes, and technology” provides a foundation for productivity, profitability, and cybersecurity.

But automating your information pipeline is just one part of the process. You need automation that goes beyond silos, integrates with your enterprise’s existing systems, and works alongside your continuous delivery platforms to streamline mission-critical processes.

Automating processes end-to-end

Automic Automation’s integrations enable seamless data flow, which creates a centralized source of truth. Further, its capacity to establish end-to-end automation of your organization’s data pipelines can:

• Curb the potential for human error.
• Enhance data quality and accuracy.
• Expedite data flows.
• Accelerate your data’s time-to-value.

Additionally, Automic Automation’s automated workflow capabilities help you achieve smoother, faster process flows that operate in the background. As a result, your teams can focus more on high-value tasks and reprioritize personnel.

Employing powerful analytics

Automic Automation also provides robust analytics that transform data into business intelligence. This gives you the data-driven insights you need to make more sound, accurate decisions.

Leveraging broad oversight

Organizations often struggle to utilize their data effectively or analyze it in the first place. One recent study conducted by Deloitte found that 67% of executives and managers lacked confidence in accessing or using data from their data analytics tools. Even if your organization manages to analyze the data obtained from your data pipelines, it may be incomplete or inaccurate.

This is where Automic Automation can be especially useful. By orchestrating all of your processes, Automic Automation helps you attain the reliability, control, and repeatability you require.

Build a robust data pipeline with Automic Automation

Data pipelines have become increasingly integral to IT and business operations—as much as the pipes are to any building. They provide centralized collection, improve data accessibility, promote collaboration, and fuel strategic decisions.

With Automic Automation, data scientists can harness and act on big data through visual workflows, self-service, and reusable building blocks. It also offers zero-downtime upgrades, governance and compliance, and mainframe-to-microservices coverage, among other key features.

Visit our Broadcom Software Academy to learn more about Automic Automation—and get the accuracy you need to thrive in our data-driven world.

Sources

BizTech. What are data pipelines and how do they strengthen IT infrastructure? https://biztechmagazine.com/article/2023/06/what-are-data-pipelines-and-how-do-they-strengthen-it-infrastructure-perfcon

Forbes. Why companies need to address bad data immediately. https://www.forbes.com/councils/forbestechcouncil/2024/01/10/why-companies-need-to-address-bad-data-immediately/

Coursera. What Is a Data Pipeline? (+ How to Build One). https://www.coursera.org/articles/data-pipeline

AWS. Challenges in building a data pipeline. https://docs.aws.amazon.com/whitepapers/latest/aws-glue-best-practices-build-efficient-data-pipeline/challenges-in-building-a-data-pipeline.html

Security Boulevard. Measuring People, Process, and Technology Effectiveness with NIST CSF 2.0. https://securityboulevard.com/2023/05/measuring-people-process-and-technology-effectiveness-with-nist-csf-2-0/

Forbes. 10 reasons why your organization still isn’t data-driven.
https://www.forbes.com/sites/brentdykes/2021/06/01/10-reasons-why-your-organization-still-isnt-data-driven/