Building Efficient Data Pipelines with Automic Automation

Building efficient data pipelines with Automic Automation

A recent McKinsey report states that data will fuel “every decision, interaction, and process” by 2025. In the meantime, additional research indicates that data-driven businesses prove 23 times more likely to secure new customers. That research also indicates that businesses relying on data-driven processes achieve considerable performance improvements: six times higher likelihood to retain customers and as much as 19 times greater profitability.

However, organizations don’t achieve these kinds of benefits without high-quality, relevant data. In fact, estimates suggest that poor data costs the U.S. economy more than $3.1 trillion per year.

Orchestrating efficient data pipelines with Automic© Automation provides a solution. Below, you’ll discover the solution's value—and learn why developing streamlined data pipelines is essential to your organization’s success.

What are data pipelines—and why are they important?

It’s certainly no secret that reliable, valid data remains crucial to a thriving enterprise. Leveraged effectively, data helps people in organizations make knowledgeable business decisions, enhance customer engagement, pinpoint new opportunities, and stay ahead of their competition. Indeed, an organization’s future growth hinges on its leaders’ ability to obtain accurate data.

And yet, business leaders often struggle to derive value from their data. In fact, many IT organizations may spend as much as 80% of their time wrangling data rather than gaining insights from it.

This is where data pipeline orchestration comes in.

Put simply, a data pipeline comprises a series of ongoing processes in which raw data is taken from various sources, transformed into the requested format, and then sent to a data lake or warehouse for analysis.

Modern data pipelines depend on five primary capabilities:

1. Data ingestion, or the act of collecting data from various sources.
2. Data storage, in which appropriate data is sent to a repository for analysis.
3. Data transformation, during which data is cleaned, modified, and prepared for analysis.
4. Data processing, or the phase of extracting meaningful information from data to detect patterns and draw insights—tasks that are supported by artificial intelligence (AI) and machine learning (ML).
5. Data delivery, in which the processed, analyzed data is submitted to the right people and location.

What are the challenges of implementing new data pipelines?

Big data may be a boon for businesses, but it also introduces new struggles to IT organizations trying to manage data dispersed across complex landscapes.

Here are a few of the most common problems businesses encounter when adopting new data pipelines:

• Inconsistent and slow implementation
• Reduced confidence in analysis and results
• Incomplete reporting
• Duplicate data
• Poor data quality and integrity
• Delays in finalizing analysis and identifying trends, which may lead to missed SLAs and revenue loss, as well as heightened risks in production

Fortunately, orchestrating your data pipelines with the right automation solution can curb (if not eliminate) some of these challenges.

What is the role of automation in data pipelines?

Research demonstrates that automating processes becomes increasingly paramount in our digital-first world—and this extends to data pipelines. And yet, other recent research by EMA found that 42% of businesses face difficulties when trying to run application modernization strategies with their current automation model. This calls for transitioning from legacy systems to an advanced, automated solution that provides increased control over automated processes.

The benefits of automating data pipelines are legion. In addition to having access to near real-time information (which is critical to making wise business decisions), the leading perks include:

• Improved data quality and consistency—Automated quality control measures and standardized cleaning measures enhance the reliability and relevance of your data. Plus, by cutting out the need for manual efforts and the human errors that come with them, you obtain more trustworthy, valuable information.
• Accelerated data processing—Automating the flow of data as it’s generated in real-time allows businesses to make faster, more agile decisions. It also helps them remain in step with changing market dynamics.
• Streamlined data handling—Too often, organizations wrestle with knowing they have data somewhere but remain unsure where it is, when data and insights will be delivered, or if all teams have access to the same data. One of the biggest advantages of building efficient data pipelines is bringing data together to establish a single source of truth. In turn, this considerably decreases duplicate data, improves data accessibility, and elevates transparency across your organization.

Further, best-in-class automation solutions can foster scalability, increase profitability, bolster team collaboration and productivity, and help you maintain compliance with data security and regulatory standards.

How Automic Automation can simplify and strengthen your data pipeline

Broadcom’s  Automic Automation facilitates the synchronization of high-quality data and enables you to achieve the control and consistency you need.

Broadcom has over 25 years of experience crafting a product that has the speed and agility most Fortune 100 businesses require. Our automated solution makes information valuable to data scientists. This is accomplished by orchestrating disparate sources automatically and through the use of:

• Visual workflows
• Reusable building blocks
• Self-service portals

Automic Automation also solves several of the most prevalent challenges in contemporary data pipelines. It bridges technology and functional silos, automates end-to-end processes, and accelerates data’s time to value. Automic Automation can be integrated with big data stores and open-source tools. Further,  it can also provide the framework for consistent and accurate training of ML models.

Improving outcomes with Automic Automation

Organizations that implement Automic Automation can anticipate the following outcomes:

• Unified workflow orchestration—Automic Automation employs API-driven architecture to standardize control across multiple environments, including mainframe, distributed, virtual, and cloud platforms. We also offer a full suite of integrations that enable unified scheduling across data pipeline tools, cloud-based technologies, and more. With Automic Automation, you’ll also get managed file transfers with encryption, compression, and checkpoint restart.
• Simplified workload automation—Automic Automation also allows you to automate routine, repetitive IT and business tasks and initiate workload execution.
• Enhanced workload visibility—Automic Automation leverages trend analytics to help users identify patterns, swiftly spot the cause of issues, and make accurate predictions.

Conclusion: Enhance operations with Automic Automation

Efficient data pipelines ensure you can gain profound insights that benefit your organization. Automic Automation helps you orchestrate the tools, teams, infrastructure, and data that are necessary for achieving this. Further, our product suite offers an array of features to boost visibility across hybrid cloud environments.

When a global media organization first came to Broadcom Automation, they had a massive amount of data from a vast array of sources. They wanted a centralized method to extract raw data and move it through the big data pipeline. We provided several solutions to overcome these challenges:

• Automating the full data pipeline so data scientists could conduct data analytics.
• Increasing workflow executions to more than 6,000 per day.
• Establishing a portal that gives more than 100 data scientists self-service access to  reports and analyses.

Ultimately, this decreased the complexities this media giant was facing and helped ensure the right data was delivered in time. This paved the way for improved productivity of data scientists, enabling them to deliver the intelligence that fueled optimized operations and enhanced customer services.

Available on-premises and in SaaS, Automic offers robust error handling and recovery. Additionally, it provides ERP automation, automation-as-code, zero downtime upgrades, lifecycle management, and governance and compliance. All told, you will experience increased operational efficiency, improved staff productivity, and reduced costs—and the level of agility you need to succeed in our data-driven economy.

Learn more about Automic Automation on Broadcom Software Academy and start paving the way toward both optimal data orchestration and streamlined business operations.

Sources:

Forbes. Why high-quality and relevant data is essential in today’s business landscape. https://www.forbes.com/councils/forbestechcouncil/2023/04/17/why-high-quality-and-relevant-data-is-essential-in-todays-business-landscape/

Neil Patel. How the right analytics can strengthen customer engagement. https://neilpatel.com/blog/analytics-can-strengthen-engagement/

Quantum Black AI by McKinsey. The data-driven enterprise of 2025. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-data-driven-enterprise-of-2025

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

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

Forbes. How robotic data automation could automate data pipelines. https://www.forbes.com/councils/forbestechcouncil/2021/08/03/how-robotic-data-automation-could-automate-data-pipelines/

Insights Success. The major benefits of a data pipeline in today’s industry. https://insightssuccess.com/the-major-benefits-of-a-data-pipeline-in-todays-industry/

InsideAI News. Bad Data Costs U.S. Companies Trillions – How Data-Quality Audits Can Help. https://insideainews.com/2022/06/05/bad-data-costs-u-s-companies-trillions-how-data-quality-audits-can-help/