With the amount of data almost any organization handles on a daily basis, it’s impossible to do operations with it manually. Even if you run a smaller organization like an ecommerce store, you’re likely dealing with:
- Product data that needs to be updated constantly
- Data on each sale made
- Data on lead activities on the website
- Paid ad campaigns performance data
- Social media performance data
- Email marketing data
Doing any operations with this amount of data manually apart from checking out the admin panel at one of the tools you’re using is impractical. This is why it’s a good idea to implement data automation.
This guide will walk you through what data automation is and how to implement it in your organization.
What is data automation?
Data automation is the process of manipulating data (uploading, transforming, etc.) with the help of specialized automation tools. An automation tool can be a no-code SaaS tool that only requires you to connect two apps to integrate data automatically or it can be custom-made code hosted on your organization’s server.
What is the purpose of data automation?
The main goal of automating data processes in a company is to make these processes more efficient, save company time, and as a result, decrease spending.
Manual operations with data are time-consuming and extremely inefficient. If you need to change pricing on hundreds of products or transfer data from sales and marketing software into a business analytics tool, you’re not just wasting hours of your time. You’re very likely to make a few mistakes that may compromise the quality of the data in the long run.
Data automation is virtually obligatory for companies and industries that deal with large amounts of data. But even a small organization can afford to implement automation of data to save time and money. This is especially crucial for solopreneurs and small teams as optimizing their time can lead to large improvements.
The five data automation techniques you should follow
Depending on how detailed the classification you’re using is, data automation can have dozens of techniques. But most will fall under these four categories.
Data extraction & transformation
Data extraction is the process of collecting data from the source. It also includes cleaning it from irrelevant data and organizing it before loading it into a data warehouse. This process mainly goes over removing certain data points from the dataset. It may be something as simple as removing duplicates or managing missing values. It can also be a bit more advanced like removing sets of values that you don’t need for the particular task, for instance, deleting the time of order for the sales report.
Data transformation involves transforming data from one format to another prior to loading. The most typical data transformation operation is aggregating or normalizing data. This can be useful when you don’t need raw data for analysis and only need aggregate numbers for a report.
Data loading is the process of transferring data from the data source to a destination. It’s the conclusive step of an ETL process. Depending on the type of process you use, it can be done either before or after loading. In the first option, you’ll be saving storage space.
Data analytics is in many cases the reason for data automation. It’s the process of applying analytical methods to the dataset to find a trend and base business decisions on it. For instance, data analytics can be used to form year-over-year growth figures. It can also be used for advanced predictive modeling or AI-based exploratory analysis.
Data visualization is the step that comes after data analytics to make the data more understandable. Instead of presenting data in the form of numbers, it can be presented in visual form, i.e., a graph, pie chart, or map. It’s useful for quickly assessing data.
What data should be automated?
Ideally, you could automate any operations with data. These include data inventory automation, data cleansing, data integration, data transformation, predictive modeling, and real-time analytics. As long as it’s not custom data mining that you only do once to check a certain hypothesis, it can and should be automated.
In case you have a lot of data operations that can be automated, here are several features of data operations that should be prioritized.
- Needs frequent updates
- Requires a lot of manual transformations before uploading
- Requires uploading large files
An example of data like this could be updating ecommerce store pricing and availability from a spreadsheet each week or forming a monthly sales & marketing report from several sources.
Data like this tends to take away too much time from you or your employees constantly interrupt their workday. If you want to implement the automation of data in your organization, focus on data processes like this and you’ll see the most return on investment.
How to implement automation of data in your organization
No matter how complex automation you’re developing, most automation processes go through these five steps.
Step 1: Identify the workflows
The first step is to identify and map out your data processes. In case you want to automate all handling of data in your organization, you’ll have to map out everything. If you’re only experimenting with data automation and want to focus on a specific process, you’ll have to single it out and register all operations that make it happen.
For instance, if you’re automating a simple sales dashboard, the workflow may look like this:
- Download a monthly sales report from a CRM
- Open the Google Sheets you use for reporting
- Paste the columns with the order value, location, and product categories into the document
- Calculate the sum total of sales and paste them into another column
- Refresh the document to see the new month-over-month growth rate and updated charts
- Save the main dashboard as a PDF file and share it with colleagues
Map out the process or processes you want to automate like this. Now, you have a blueprint for automation.
Step 2: Choose the software
The next step is choosing how you’re going to automate your data processes.
If you run a large cooperation with hundreds of data streams, it’s best to invest in proprietary software made by a team of engineers that can be hosted on your servers.
For most other tasks, you can use software for the automation of data like Coupler.io.
Coupler.io is a data automation and analytics platform that provides businesses with a complete set of tools and expert services to automate their data flows.
Step 3: Build and test the automation
Once you have the blueprint for the automation and the software, create the automation and test it.
The exact way to do this may vary depending on the type of software you’ve chosen. The goal here is to test how the software works without ruining your production documents.
Create a new file in the destination to test out the pipeline. Then build all the automation processes, and try running it a couple of times.
Check whether everything is imported and transformed correctly, and make necessary changes to the process if needed.
Step 4: Implement the automation
Once you’ve polished everything you need on the test grounds, you can implement automation into your core processes.
If you’re using Coupler.io and you are testing it with a new destination document, all you have to do is replace the destination in the automation importer.
In case you’re developing a custom automation system, it may take a bit longer to implement the automation as you may have to reconfigure some parts of the code.
Do a final test to see that the automation system performs well with your main destination, and set up the schedule for imports of data.
Step 5: Train the employees
The only thing left to do is train employees to work with the data automation system and fix it if necessary.
You’ll need to educate the people who are directly responsible for the workflow that’s being automated to make sure they know what can go wrong and what can be done to make it right. Or at least who to talk to in case a fix is needed.
Data automation examples
Complex things like the automation of data can be better understood with a few examples.
Tradezella app usage data automation
First, let’s look at how Tradezella increased its sales with data automation.
Tradezella is an app that helps its customers improve their trading strategies and patterns based on their results. In earlier stages, Tradezella wasn’t able to access data on the app usage as the software they were using only allowed viewing generalized reports.
With the Coupler.io team, Tradezella automated data imports of detailed app usage to BigQuery and analyzed them in Google Sheets. Gathering all the information in one place allowed them to achieve the following.
- Catch when customers churn and re-engage them
- Understand customer activity patterns and serve personalized content
- Understand what stage of the funnel customers are on and engage them correctly
All of this led to Tradezella being able to retain 2.5x more customers.
Terminal 1 reporting automation
Another example of how tedious work can be cut down severely with data automation is Terminal 1 automating their reporting system.
Terminal 1 is a Taiwan-based recruiting company and the amount of data they generated on candidates started weighing heavily on the admin team. Here’s what they had to do before implementing the automation of data with Coupler.io.
- Receive two automated emails from QuickBooks with financial reports
- Import these reports into Google Sheets document by document
- Merge some documents
- Go to Airtable and download some data not available on QuickBooks
- Merge it with documents imported to Google Sheets from QuickBooks
All of this had to be done day in and day out and with dozens of documents. Naturally, this amount of repetitive work was wearing the team out.
With Coupler.io, Terminal 1 automated Airtable exports, as this is where the bulk of company data is, as well as QBO exports. Now, all company data is automatically merged and refreshed in a multi-purpose dashboard that different stakeholders can view to access relevant data.
The admin team now has a lot more time on their hands to spend on working with data instead of exporting it. The data analysts can work with company data without having to check whether it’s fresh or not.
If you want to explore opportunities for your organization, talk to the Coupler.io data automation team to get a quote.
Disadvantages of data automation
It may seem as though data automation is a spotless solution. However, it does come with a few disadvantages.
- May be difficult to set up
- Can result in a moderate cost increase
- May create vulnerability to data leaks if set up incorrectly
When you’re setting up a system like that, you may make a mistake and leave one of its components without proper security, resulting in the potential for a data leak.
Another possibility is configuring the system in the wrong way and corrupting the data that goes through it.
Neither of these disadvantages of data automation will happen if a team of data engineering and analytics professionals are setting up a system for you.
Benefits of data automation
The benefits of data automation far outweigh the potential drawbacks. This is what it can do for your company.
- Saves time
- Decreases human error
- Saves money both on billed hours and potential errors
- Streamlines data analytics
- Makes business decisions more informed
Building a data automation system can cost you relatively little but can potentially save you large amounts of money and open up new opportunities for making more money as an organization.
Does your organization need data automation?
So should you implement the automation of data in your organization? For many companies, the answer would be yes.
If you have any type of data process that has to be done manually, automating it can free your time for tasks that require more mental involvement and save a lot of money on possible errors.
Data automation put to good use can potentially increase your business’ productivity and increase sales and revenue. In case you’re having doubts or think it’s too complicated of a process, give it a try with Coupler.io. Try automating one of your data processes and see how well it works for you.Back to Blog