While many data practitioners understand the concept of data automation, few grasp the intricacies of putting it into practice. A successful automation endeavor requires careful planning and a methodical approach. This isn't merely about replacing manual tasks with software; it's about optimizing the entire data lifecycle, ensuring each stage, from ingestion to utilization, is streamlined and effective.

This article aims to shed light on the steps involved in automating data and real-world examples of its application.

A Note on Our Perspective of Data Automation

When we talk about "automating data" in this article, our intent isn't to promote the notion of replacing human involvement. Instead, we advocate for the strategic utilization of technology to streamline and enhance data-related processes. Data professionals often find themselves entangled in repetitive and time-consuming tasks. With the right automation tool, these routine operations can be efficiently handled, allowing professionals to focus on projects that truly matter. This not only facilitates impactful business decisions but also fosters innovation. For a more in-depth exploration on this topic, feel free to dive into our dedicated article: Data Automation: What It Is and Why It Matters.

‍Read More: Data Automation: What It Is and Why It Matters

Steps for Automating Data: A Detailed Walkthrough

When seeking to implement data automation, it's vital to approach the process in a systematic and thoughtful manner. Our roadmap to automating data is strategically structured into two distinct phases: laying the groundwork and collaborative implementation.

Phase 1: Laying the Groundwork for Automation

This phase is about introspection and clarity. By evaluating the current state of your data ecosystem and establishing explicit objectives, you set the stage for a successful automation transition. Additionally, considerations around data governance and initial workflow design ensure that when you do move forward, you do so with confidence and direction.

1. Evaluate Your Data Ecosystem

Before automating, understand the volume, variety, and velocity of your data. Recognize where manual processes slow down operations or lead to inconsistencies.

• Consider Current Data: Begin by listing all your data sources. This could be spreadsheets, databases, CRMs, etc. Determine which data is critical for your operations and which might be redundant or obsolete.

• Determine Data Quality: For each data source, assess its accuracy, consistency, and relevance. Identify gaps, inconsistencies, or errors that might impede automation.

• Understand Data Flow: Once you understand where your data is coming from, define how it flows through your ecosystem. How is data currently being transferred from one point to another? Where is it stored? How easy is it to access business-ready data?

2. Establish Clear Objectives

Once you have a clear understanding of your current data ecosystem, identify the gaps and clarify your objectives for automating data.

• Define Goals: What do you aim to achieve with data automation? This could range from speeding up data entry processes, to ensuring data consistency, to near real-time data analysis.

• Expected Outcomes: Define the tangible benefits you expect from automation. Whether it's a 20% reduction in data processing time or a 15% increase in data accuracy, having measurable outcomes can guide your journey.

• Stakeholder Involvement: Involve key stakeholders from different departments to understand their data needs and challenges. This collaborative approach ensures a more comprehensive automation plan.

3. Data Governance and Compliance

• Develop a Data Governance Strategy: This involves establishing who is responsible for what data and setting protocols for data access.

• Regulatory Compliance: Be aware of any data-related regulations in your industry. For instance, if you're in healthcare, you'd need to consider HIPAA when automating patient data.

4. Integration and Workflow Design

• Map Out Desired Workflow: Before diving into tools, sketch out how you want data to flow once automated. Who inputs it? Where is it stored? Who accesses it and for what purpose?

• Address Integration Points: Identify where data needs to be shared between systems or departments. Think about how to make this transfer smooth, ensuring data integrity.

• Scalability: Consider the future. How will your data sources grow? Will the workflow you're designing today accommodate tomorrow's needs?

Read More: How to Ensure Data Integrity at Scale

Phase 2: Collaborative Implementation

Now, with a clear roadmap in hand, the journey transitions to the implementation stage. This phase is about bringing your well-laid plans to life. While the specifics of the tool aren't our focus here, the automation solution you opt for should support and streamline these steps. From pilot testing to ensuring your team is well-equipped to work in this new automated environment, it's a collaborative endeavor. Emphasis is on working in tandem with the chosen tool, your team, and the established protocols, ensuring that the entire automation process is seamless, effective, and value-driven.
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5. Pilot Testing and Iteration

• Test on a Small Scale: Before fully automating, define the primary use cases you'd like to automate in a pilot or trial. This allows you to catch issues early and refine the process.

• Collect Feedback: During and after the pilot, gather feedback from users. What worked well? What challenges did they face? This feedback is invaluable in fine-tuning the process.

6. Prepare Your Team

• Educate Your Team: Automation often brings about change in how tasks are done. Ensure your team understands why changes are being made, and work with the platform to train them on any new processes.

• Provide Continuous Support: As the team adapts to automated data pipelines, be ready to offer support. This might involve troubleshooting, further training, or making tweaks to the automation workflow based on practical experience.

7. Monitoring and Maintenance

• Establish Key Metrics: Define what success looks like. This could be faster data processing times, fewer errors, or more timely reports.

• Schedule Regular Audits: Even post-automation, periodically review your data for quality and relevance. This ensures that your automated processes continue to add value over time.

Read More: Automated Data Pipelines: What You Need to Know

Navigating Common Challenges When Automating Data

Embarking on the journey of data automation can be a transformative endeavor for many organizations. However, it's essential to be aware of the challenges that may arise. By identifying and addressing these challenges proactively, you can ensure a smoother transition and a more successful implementation.

Data Quality and Consistency:

• Challenge: Even with sophisticated automation tools in place, the age-old adage "garbage in, garbage out" holds true. Inconsistent, outdated, or inaccurate data can compromise the results of your automation efforts.

• Solution: Regularly audit your data sources to ensure accuracy and consistency. Establish protocols for data validation and cleansing before integrating them into automated workflows. Look for tools that will automatically stop the propagation of bad data before it reaches end consumers.

Integration Complexities:

• Challenge: Data resides in various systems, databases, and applications. Integrating these disparate sources can become a significant hurdle.

• Solution: Make sure the platform you choose can act as a connector between different data sources.

Resistance to Change:

• Challenge: Resistance to change is a human tendency. Transitioning from manual processes to automated ones can face resistance from team members accustomed to traditional ways of working.

• Solution: Engage your team early in the automation journey. Provide training, communicate the benefits clearly, and ensure they understand the value automation brings to their daily tasks.

Defining Clear Responsibilities:

• Challenge: With automation handling tasks that were once manual, there can be ambiguity about team responsibilities.

• Solution: Redefine roles and responsibilities as necessary. Make it clear who oversees automated processes and who is responsible for intervening when things go awry.

Scalability Concerns:

• Challenge: What works for today's data volumes and processes might not be suitable for future requirements, especially as businesses grow and evolve.

• Solution: Opt for scalable solutions and architectures. When designing workflows and choosing the platform you're going to work with, always factor in potential increases in data volumes and complexities.

By understanding these challenges and planning for them, you can ensure that your automation efforts yield the desired results, enhancing efficiency, accuracy, and value across your organization.

Real-World Examples: Automating Data in Action

Diving beyond theory, let's explore how real-world companies have successfully automated their data. These examples provide tangible insights into the benefits and outcomes:

Packaged Goods: Harry's

Harry's, a leading force in men's grooming products, stands as the second-largest player in the US men's non-disposable razor market. Their pivot to diverse retail channels necessitated a comprehensive omni-channel marketing attribution strategy.

Read More: Harry's Accelerates Global Marketing Analytics

The Challenge

Transitioning from a direct-to-consumer model, Harry's needed to efficiently assimilate varied data types, spanning sales, market analytics, and marketing spends, for optimal omni-channel marketing spend attribution.

The Data Automation Solution

Data automation transformed Harry's data processes. Tasks previously taking months were reduced to mere hours or days. Automation allowed for streamlined data collection from varied sources, reducing potential errors and bolstering scalability.

Impact & Results

Data automation played a pivotal role in optimizing Harry's substantial marketing expenditures. With faster data processing and a reduced need for manual maintenance, Harry's gained a competitive edge in the market.

"So we got out of the business of maintaining Python scripts, and got into the business of SQL transformations, which has been a huge upgrade for us in terms of maintenance and scalability."

William Knighting, Analytics Platform Lead at Harry's

Healthcare: Biome

Biome, a clinical performance management company, strives to enhance healthcare outcomes and eliminate billions in unnecessary costs from the US health system. They focus on helping hospitals and health systems deliver optimal care affordably.

Read More: Biome leverages Automation to expedite their data pipeline and support rapid growth in data volume.

The Challenge

Biome needed a robust solution to handle increasing data volumes from hospitals and expand the cardiac procedure information they process. Their aim was to analyze data more efficiently, support the growing needs of healthcare providers, and highlight opportunities for change.

The Data Automation Solution

With data automation, Biome supercharged their data processing capabilities. What once took 20 hours for each customer now takes only three hours for all customer data. This enabled quicker insights and actionable recommendations for hospitals and clinics. Additionally, their analyst team, without developer backgrounds, could easily manipulate data. Operational efficiency was significantly improved by parallel processing, error handling, and restart capabilities.

Impact & Results

Data automation permitted Biome to handle more data, meet growing client demands, and discover new performance opportunities. With enhanced scalability and reliability, they consistently added new data pipelines, ensuring cleaner data, valuable insights, and client cost savings.

"We have been able to optimize our data pipelines, reducing processing time and enabling us to deliver data products faster. It has accelerated our cycle time and positioned us for future growth and scalability."

Stuart Jacobson, CEO at Biome

Partnering for Future-Proof Data Automation

Navigating the vast landscape of data automation requires more than just technical solutions; it necessitates a thorough understanding of the unique challenges and aspirations of your business. Identifying gaps in your current processes and articulating the outcomes you aim to achieve with automation is a critical internal endeavor. However, once this foundational work is done, you're not left to journey through implementation alone. At Ascend, we believe in partnership.

Our data pipeline automation platform isn't just about meeting today's objectives but about future-proofing your operations. From ensuring that automation aligns seamlessly with your strategic goals to offering comprehensive training for your team, we're by your side. With world-class support at every juncture, we're dedicated to ensuring that the transition to automation is not only smooth but also transformative for your organization.