What is data automation? Guide + examples

There was a time when I didn’t have enough information. I’d struggle to remember the name of a quarterback or who starred in a niche sitcom so I could win an argument. Today, I have the opposite problem: I’m drowning in a non-stop firehose of information blasted at me from half a dozen devices. And it’s a lot harder for me to win arguments.

Businesses are in the same boat. They’re inundated with information across customer interactions, sales figures, and marketing campaigns (not to mention all those LLM outputs). Trying to stay on top of all this data manually just isn’t an option, and that’s why data automation is a thing.

In this guide, I’ll break down everything you need to know about data automation, and show you how you can bring it to your own data management workflows with Zapier. 

Table of contents:

What is data automation?

Data automation means letting technology handle repetitive, time-consuming data management tasks. This includes everything from collecting and processing data to transforming, storing, and analyzing it. The goal is to make all these processes flow with minimal human intervention.

Automation often starts with ETL (Extract, Transform, Load) pipelines, which are the engines behind a lot of data processing tasks. ETL is designed to extract data from various sources, transform it into a different (and presumably more useful) format, then load it into a destination like a database or analytics platform. When done correctly, these pipelines help make sure your data is clean, consistent, and ready for analysis before a human being even looks at it.

But data automation can also refer to the process of collecting, processing, transforming, and storing data without relying on ETL. Zapier, for example, connects thousands of data sources and automates data workflows that run seamlessly between teams, apps, and systems. 

For data-driven businesses (also known as every business), data automation can replace hours of manual work with a streamlined system of processes that run in the background.

How does data automation work?

Data automation works the same way most automations work: by telling your systems, “When this happens, do that.”

Different businesses use data automation in different ways, so it doesn’t necessarily “work” in just one way. Instead, data automation usually involves a range of processes that follow a well-defined sequence of steps. I’ll break down those steps here in more detail.

1. Data collection and extraction

By automating data collection, you can extract information from wherever it lives—databases, APIs, cloud applications, or even forms—and bring it into a centralized system. When you’re working with massive, disparate datasets, automated collection is a must.

Imagine, for example, you’re using a form to gather customer feedback on a new product you’ve launched, but you need those responses organized in a spreadsheet for analysis. Rather than trying to keep your records up to date through hours of manual, error-prone updates, you could turn to data automation instead.

With Zapier, you can collect data from thousands of apps and unite it in a single source of truth, whether that’s Zapier Tables, a spreadsheet app, your CRM, or anywhere else. Here’s a template to get you started.

2. Data processing and transformation

Sadly, there’s no ironclad set of rules for data formats that everybody follows. Dates, for example, might be YYYY/MM/DD or YY-MM-DD, and months might be spelled out (“October”) or abbreviated (“Oct.”).

These kinds of differences need to be standardized before datasets can be used for analysis, and that standardization is known as data transformation. Automation tools can scan huge databases for improper data formatting, input errors, and other inconsistencies, then fix them automatically based on rules you’ve set. Or they can make the changes as they’re transferring data.

Zapier Formatter can automatically format data to get it working the way you need. 

3. Data transfer

With processing out of the way, it’s time to move your newly clean data to a central location, such as a CRM, data warehouse, or analytics platform. 

Data transfer automation ensures that any data being moved meets specific standards, e.g., it’s formatted correctly, doesn’t have missing values, falls within certain date ranges, and passes whatever validation checks you’ve defined before it lands in its destination.

Zapier can automatically transfer data across thousands of apps, and with formatting and AI steps built in, you can be sure the output is exactly what you need, where you need it. 

Here are a few popular examples of transferring data from one app to another, but you can extend this to complex systems across your entire tech stack.

4. Data analysis

You’ve got your data how you want it and where you want it, but it won’t bring you any value unless you analyze it. Data automation software helps turn raw data into real insights by calculating performance metrics, creating visualizations, and feeding information into predictive models for forecasting. 

With the proper setup, you can automate your data pipeline all the way from initial extraction to pushing actionable insights right to the desks of decision-makers.

For example, you could use Zapier to automatically capture form submissions, convert them into a usable format, and push them to a tool like Databox for analysis. Or use Zapier Agents to analyze customer sentiment or sales calls, for example. Here’s a template to show you how it works.

Data automation tools

The world of data automation tools is a crowded one. You’ve got plenty of software to choose from, from intuitive no-code platforms to sophisticated tools that let you perform the equivalent of brain surgery on your data.

The following is a quick overview of some industry-leading options:

• Zapier: Zapier is a no-code AI orchestration platform that connects over 8,000 apps to integrate automated data workflows throughout a business. Zapier is ideal for teams and enterprises that want to unify fragmented systems and orchestrate end-to-end data processes without needing to code.

• Microsoft Power Automate: Part of the Microsoft ecosystem, Power Automate has low-code and RPA capabilities to enhance productivity for teams using Microsoft tools like Outlook, Excel, and Teams.

• Apache Airflow: Apache Airflow is an open-source platform designed for building and monitoring complex data workflows. Favored by data engineers working on ETL pipelines, it does require much more technical expertise to operate. But if you know how to use it, it’s very powerful.

• Fivetran: Fivetran automates data extraction and loading, focusing on the ELT (Extract, Load, Transform) stage of data pipelines. Unlike ETL tools that transform data before loading, Fivetran loads raw data first and lets you handle transformations within your warehouse using SQL or other tools.

• Airbyte: Airbyte is an open-source data integration platform supporting both community-built and custom integrations. It’s well-suited to working across fragmented data ecosystems without guardrails.

• Informatica: Informatica provides enterprise-grade data management functionality for scaling, governance, and compliance. It’s trusted by large organizations that have the (equally large) engineering teams to manage an absolute behemoth of a tool.

Opt for purpose-built data automation software (like Informatica) if you’re wrangling datasets so big and messy they make your head spin. But go for an AI orchestration solution (like Zapier) if you want to integrate and automate data flows across the cloud.

Common challenges and solutions for data automation

Understanding data automation is just step one; actually implementing it within your business is where things start to get tricky. Here are some common data automation hurdles and strategies for clearing them. 

Investment cost

There’s no such thing as a free lunch, and deploying new software requires an investment of time and money. You’ll need to pay for licenses, and there are usually different subscription tiers to choose between (think free, premium, enterprise). Here are a few tips to help you avoid taking on unnecessary costs:

• Identify your priorities by asking yourself: What’s currently eating up the most time, and where would I get the most value out of automation? To find out, perform a “task audit” by carefully watching a team perform a set of data tasks to identify bottlenecks and pain points.

• Prioritize your automation efforts by goal, working on low-lift or high-impact areas first and proceeding down the list from there. Focus on the data workflows that eat up the most hours or lead to the most errors.

• Roll out data automations in phases to test their effectiveness and build momentum. There’s a reason software engineers “iterate” by solving problems in pieces, and it applies as much to automation as it does to building networks. 

Zapier is a cost-effective choice when you want to streamline workflows across a suite of existing apps, AI tools, and data sources.

Upkeep, maintenance, and scalability

As your business grows, maintaining and scaling automated data systems can become a heavy lift. You’ll need staff and resources to oversee your data automation software, troubleshoot issues, and adapt to evolving needs. To manage this effectively:

• Allocate a set budget and hours toward routine maintenance. An ounce of prevention is worth a pound of cure, and if you have a cadence for checking on the health of your data automations, you’re more likely to catch issues before they flare up.

• Plan for scalability from the start by factoring automation capacity into your growth projections. If you’re expecting to double your data volume, make sure your tools can handle it. It’s much easier to build in that elasticity upfront than to scramble when usage suddenly spikes.

Zapier’s enterprise automation ensures that you can focus on your core work, while your software takes care of maintenance. And with thousands of integrations and enterprise-level security and governance, you know it can scale with you.

Role changes and training needs

Automation often leads to shifts in responsibilities. This can be good (less work for your data team), but it also raises questions about who owns what in a post-automation environment. Use these tips to ease the transition:

• Schedule ongoing training sessions to keep your team fluent in automated workflows. When people understand how the systems work, they can spot issues faster and suggest improvements.

• Maintain living documentation that evolves with your automations. SOPs should be treated as active references. Update them whenever workflows change so new employees and veterans alike have accurate information at the ready.

• Review your documentation and training gaps quarterly or twice a year. Regular check-ins help you catch outdated processes and identify where your team needs support as data workflows evolve and automations grow in complexity.

Automate your data with Zapier

Over the past decade or so, data automation has moved from being a top-shelf luxury to a table-stakes necessity. By automating repetitive tasks like data collection, processing, and analysis, you free up valuable time for more valuable, innovative work. 

With Zapier, you can weave thousands of applications together to create a fabric of workflows tailored to your needs. Whether you’re updating databases with form submissions, ensuring consistent formatting across platforms, or importing records into your database, Zapier brings this into reach with just a few clicks in an intuitive interface. 

If you’re ready to take the next step, visit Zapier’s library of pre-built automations and see how Zapier can automate data management, storage, migration, and formatting.  

Related reading: