Guide to Data Automation: Process, Examples, and Types

Data is moving faster; if you can’t keep up, your insights will be old before you’ve gathered them. According to Deloitte, automating data and gaining real-time analytics will become a strategic imperative for most businesses in the coming years and will eventually become a core technology. Here’s what data automation is at its foundations and how it can help your organization gain competitive insights faster. 

What is data automation?

Before we dive into the benefits and the nitty-gritty data automation process details, what is data automation? Data automation is the process of collecting, transforming, organizing, and analyzing data without human intervention. When companies invest in data automation, they set up processes to gather the data on their own, which usually involves writing scripts and creating algorithms to do the work without people needing to be involved. 

Data automation helps companies score insights with greater efficiency, productivity, security, and faster scalability. No matter your industry or the size of your data loads, data automation offers a more flexible way to get the most from your data. 

Elements of data automation 

Data automation is becoming the new way of doing ELT (extract, load, transform). Previously, data analysts would manually have to go through each of the ELT steps: 

• Extract data from a variety of sources, such as CRM, ERP, emails, etc. 
• Load the data into a data warehouse or data lake.
• Transform the data, which includes cleaning the data set of errors and duplicates and formatting the data so it’s consistent and in the correct format for the storage destination or target system. 

ELT provides a variety of benefits to companies, so why not increase those benefits by speeding up the data insights? By automating the ELT process, data analysts — and their organizations — get more insights faster. Data automation allows a script to do all the ELT steps on its own, saving companies time and money, giving companies real-time information to make better business decisions, and saving data analysts from doing tedious work. 

How does data automation work? 

Automating data starts with collecting data. The first step is deciding your company’s goal and what kind of data you need to collect. This can include data from APIs, CRMs, ERMs, emails, third-party applications, databases, and more. Then, you can start writing a script. As you write a script (or, more likely, multiple scripts) for the project, you can make sure the script automatically collects data from all of your sources. The data can be either structured or unstructured.

Then, after the data is gathered, the next step is to create a sender script that integrates the data. This step aims to integrate data from multiple places, formats, and processes into a single, usable state. Depending on your goal with the data, this often involves reformatting the data for consistency and compatibility with your target destination. This integration step is where the usual ETL or ELT processes happen. 

The gathered data fulfills the “extract” phase, and now the scripts can load, transform, clean, de-duplicate, and map the data without humans needing to step in. If your company deals with highly sensitive information, such as protected health information (PHI), you may need to add steps to mask the data. The clean data can then be validated to ensure it was transformed properly. 

The integrated and cleaned data then needs to be categorized and stored in a data warehouse or data lake. Scripts automatically place a database entry into a taxonomy using defined rules. The categorization helps keep the data organized, searchable, and retrievable.  

Once you have those processes in place, your job is to keep repeating and refining those steps. You’ll likely need to monitor and maintain the scripts and automation tools to ensure the processes continue. As business needs change, you may need to slightly adjust how the data is formatted, such as by adding data sources, modifying scripts to improve data quality, changing data loading techniques to scale up processing, changing output formats, and making other modifications. Because all the previous steps run largely independently, data automation frees data analysts up to focus on this strategy, refinement, and scalability phase. 

Types of data automation

There are four main types of data automation techniques: data integration, data transformation, data loading, and data analysis. Each of these techniques can help you gain business insights and efficiencies. 

Data integration

Data integration focuses on integrating data from different sources into a single view. The integration process makes it easier to understand the data, gives a comprehensive view of what’s happening, and uncovers insights for BI teams. 

There are many ways to integrate data, but the main pieces of this technique include multiple data sources, master nodes, and users who request data from those master nodes. Data analysts can access data from the master nodes. When data is integrated automatically, data analysts can understand insights faster because the data is already gathered in a single place.

Data transformation

Data automation can transform data from multiple sources into a single format that matches the target destination. For example, data transformation can make sure all dates are formatted in the same way (day-month-year or month-day-year) or that all sales numbers from multiple countries are converted to a single currency. Transformation can also be ordering data in a certain way, formatting it to be stored in a certain file format, combining repetitive categories, and organizing demographic information. Rather than doing this transformation manually, data automation can do it for you, saving time and reducing the chance for errors. For companies in regulated industries, data transformation is also essential for staying compliant. 
Data loading
Data loading automatically loads data from various sources into a single data warehouse. Data loading has several advantages. No worries about your computer crashing; data loading frees up your computer’s memory. Because it’s easier on computer memory and is usually cloud-based, data loading also helps organizations scale up and start handling larger amounts of information. When data is loaded into a common place like a data warehouse or data lake, multiple people can work on the same data set simultaneously, which increases collaboration and real-time progress. Automatically loading data ensures you’re always dealing with updated data without uploading the data set again every time there’s a change. 
Data analysis 
All the data in the world won’t do you any good until you analyze it. Without analysis, data is just a pile of puzzle pieces that don’t form a picture. Data analysis is one of the most important types of data automation because it’s where the insights come into focus. Data analysis can include identifying trends, visualizing and comparing information, implementing neural networks to uncover hidden connections between data points, finding ways to reduce costs, personalizing information for your customers based on their interests, and more. 

Benefits of data automation

Data automation offers many benefits to organizations. Here are some of the advantages your business will start to reap immediately after implementing data automation processes:
Fewer errors
While manual ELT processes work, they’re not perfect. Anytime humans are involved, there’s a chance for error. Perhaps the data didn’t get cleaned properly or the data analyst didn’t thoroughly validate or standardize data before loading, which can compromise accuracy and consistency. Automating data gathering, integration, and transformation ensures accuracy, objectivity, and consistency over human judgment. 

More efficiency and productivity

Data automation is much more efficient. Automated data scripts run without needing breaks, taking PTO, or getting distracted. Because data automation runs smoothly on its own, the efficiency ripples through to other areas of the organization to improve productivity. Data analysts don’t have to spend their time on rote tasks and repetitive manual data uploading and can focus on strategy, implementing changes based on insights, and improving data quality. 
Better scalability and flexibility
Scripts can scale with your company’s data needs. Data automation can continuously load data to handle larger data sets and more complex transformation tasks. Data automation is flexible, too; if you need to change data formats for a different target destination or hone data quality, you just need to adjust a script, and the rest takes care of itself. 
More precise insights
The improved data quality and real-time results of data automation mean your company gets more precise insights. With better insights, your executives can make more informed decisions, personalize offerings to customers, and prepare for future industry trends. 
Higher profitability
Skyrocket profits and gain competitive advantages when you implement data automation. The insights companies get from automated data help executives understand market trends and industry opportunities, manage risk, and understand consumer behaviors to boost sales. 
Compliance and security
Without human errors, data automation is safer from accidental data exposure. Data automation can also make sure scripts include precautions for regulatory compliance, such as data masking for health information and compliance with GDPR.  

Common data automation challenges 

While data automation offers many advantages, it’s not without its challenges. One major hurdle companies may face is data security and privacy concerns. While data automation is generally much more secure than manual ETL processes, there are still significant data security risks. Automated data scripts have the potential for security-threatening errors and biases in decision-making processes. Because the scripts are automated, analysts may not check the data very often because they assume it has already been validated. 

Other companies may run into integration and compatibility issues. Data integration is an essential part of data automation. If your data automation tool can’t handle certain file types you need to include in your extraction processes, you lose insights from that data set. This is also a future-proofing issue. As new tools become available, you’ll be working with new types of data, and you’ll want to make sure that future data can play well with your data automation pipeline and any other systems you have. 

The adoption of data automation tools is also a common challenge for companies. Fear or worker displacement can cause hesitancy among many teams. If the tool is difficult to use, employees may experience a steep learning curve or not use the tools to their full potential. 

Data automation strategies 

Get the most out of your data automation tool. Here are some data automation best practices to help you gain the most insights. 

• Identify automation opportunities in your business. 
• Choose the right data automation tools and technologies that will integrate well with your existing systems. 
• Implement a successful data automation strategy. The majority of successful data automation planning happens before you even buy the data automation tool, so know beforehand what kinds of outputs you’re looking for and what insights your strategy requires. 
• Prioritize data governance and data quality management. These processes are much easier to set in place before implementing data automation than after! 
• Monitor and optimize data automation processes for continuous improvement and more savings. 
• Stay updated with emerging data automation trends. There are new tools, strategies, data types, and techniques emerging all the time! Don’t get stagnant, or you’ll lose your competitive edge. 

What to look for in a data automation tool

There are dozens of data automation tools available on the market. The one that’s best for you may depend on a variety of factors. When you’re researching tools, ease of use should be a priority. Ease of use can include the simplicity of data visualization tools, configuration options, and training support offered by the vendor. Ease of use is one of the best qualities to look for in a data automation tool because the easier the tool is to use, the higher the adoption rates will be among your employees. To get the full value of what the tool can do for you, you need as many employees using the tool as possible. 

You’ll also want to make sure the tool will integrate well with your data sources and existing systems. Depending on how complex your data sources are, you may also consider how flexible and customizable the tool is. 

Security is another factor you should be aware of. Especially if you work in an industry that deals with sensitive information, such as healthcare or government, you’ll want to know the tool can protect data and keep your company compliant with regulations. 

Finally, you should consider the elephant in the room — the price point. Data automation tools aren’t cheap. You should know what kinds of data automation you want the tool to perform and what the expected savings would be if you invested in this tool. Come prepared to shop for data automation tools with a budget in mind, how many users you’ll want to be involved in the tool, and what your business goals are. 

Examples of data automation

How could data automation transform your company? Here are some use cases of data automation that showcase the benefits companies gain when they invest in automation. 
Healthcare
The healthcare industry’s complex juncture of many moving parts is an excellent example of how data automation is beneficial. Healthcare companies can automate patient data, making patient information available to multiple providers and insurance companies quickly. Information like patient health history, test results, and bill payments can be streamlined and stored together. 
Consumer packaged goods
When working in the consumer packaged goods industry, you’re dealing with a complex supply chain of product materials, packaging materials, and shipping coordination. Data automation can provide insights on warehouse inventory, opportunities to cut costs, ways to make shipping more efficient, and when you may need to order more supplies. You’ll also get more details on consumer behaviors and market trends so your products are competitive. 
Manufacturing
Data automation is foundational to success in the manufacturing industry. Data automation can provide advantages with predictive analytics that help keep your machinery repaired and in good shape. By automating workflows, your teams can work better together to assemble products. Data automation also helps you order raw supplies more efficiently. 
Accounting
Accounts receivable is a breeze with data automation. Income, expenses and taxes are all easily calculated with data automation. You’ll get a holistic view of your budget, sales pipeline, anticipated expenses, and other financial information so you can make smarter investments for your company. 

Understand and apply your automated data

Use Domo’s data automation tools to gain deeper insights, understand consumer behaviors, and be prepared with predictive analytics. Have two minutes? Watch a free Domo demo on what makes our tools uniquely efficient at automating data pipelines. To see what kinds of benefits data automation could bring your organization specifically, contact a Domo rep today.