Modern businesses collect huge amounts of data. Think about all the apps and software you use every day to run your business. You receive payments from Stripe and PayPal, then process your invoices through invoicing and accounting software.
You also use Google Ads, Facebook Ads, and other platforms for online advertising. The list becomes even longer if you add your CRM, customer support solutions like Zendesk, and so on.
All of these apps collect data. Guess what? Collecting and understanding this data is the key to making your business more successful.
Think about all the things you can do with your data. Let’s imagine a man named John, who owns an e-commerce business. On his website, John sells T-shirts, tote bags, and visor caps.
John goes to his e-commerce dashboard in Google Analytics. He finds out that the most purchased products are T-shirts and tote bags. According to his data, visor caps don’t generate much revenue.
Also, he discovers that customers spend way more time on pages dedicated to T-shirts and tote bags, rather than on those dedicated to visor caps. Most customers who land on the visor caps’ category pages immediately abandon the website.
Now John knows that he should focus more on tote bags and T-shirts, and reduce the assortment of visor caps. Or, stop selling caps at all. This is why analyzing data is important.
But, let’s consider another aspect. John has not only data from Google Analytics. He also needs to regularly check data from Google Ads and Facebook Ads, to find out if his campaigns are successful.
He has a newsletter subscription form, as well as useful customer data from Zendesk. Lastly, he needs to closely monitor data from PayPal and his invoicing software to make sure everything is okay.
Keeping up with all this data takes up way too much of his time. Let’s take a step forward. What if John had three e-commerce sites rather than one? Checking all data manually would be impossible.
Here’s where data automation comes in handy. Data automation is simply the process of collecting, storing, handling, and processing your data without human intervention.
How does data automation work?
Let’s say John just purchased a data automation tool. He would need to connect the tool to all the apps he wants to collect data from. Most data automation tools have pre-built connectors to common-use business apps. This means that John will probably be able to connect all the apps in just a few minutes, without the need to write code.
The data automation tool automatically collects metrics from all the apps, processes them to make them easy to understand for humans, and then gathers them in the same place. Usually, this place is a data warehouse. A data warehouse is a hardware or cloud-based storage solution meant for large amounts of data.
Data automation mainly consists of three steps: Extract, Transform, and Load (ETL). Let’s explain these in more detail.
This step is as simple as it sounds. Extracting simply means collecting data from all the sources, i.e., the business apps you use.
This is when John’s data automation tool retrieves data from Google Analytics, Google Ads, Zendesk, and so on. If his tool offers this option, John can schedule automatic data extraction at certain intervals. For example, he can have the tool extract data from Google Analytics every hour, and data from Google Ads every day.
During the first step, the tool extracts raw data from the apps. Raw data has not undergone processing yet and is difficult to understand for humans. It’s useless for John to see raw data as is because only a data scientist can understand and analyze it.
So, John’s automation tool performs the transforming process to turn raw data into useful information. The tool will remove redundant values and inconsistencies, and standardize the metrics.
Let’s explain what standardizing means. One of John’s marketing apps records dates in the mm/dd format. Another one records dates in the dd/mm format. If you gather metrics from both apps in the same place, there will likely be a lot of confusion!
John’s marketing tool will standardize all dates into the same format, let’s say mm/dd. This way, data becomes easy to understand.
After the transforming step, the data automation tool gathers data from all apps into the data warehouse. From here, John can move data into a Business Intelligence (BI) tool that will help him make sense of his metrics and derive insights. The end goal is to make his business more successful.
Benefits of data automation for your business
Historically, companies would collect data, such as the customers’ phone numbers, then have the employees enter it manually into a computer. Most of us can certainly remember the times when data entry jobs were a thing!
The process was long, tedious, and expensive because you had to hire people to do it for you. Today, data automation makes everything much faster and easier. Even a small business owner like John can afford to collect, store and make sense of his data, without having to hire additional employees.
At this point, it’s pretty obvious that data automation saves you lots of time and money. Here are other good reasons why you should consider using it.
Reduced human error
Back in the 1980s, it was very common that data entry employees made mistakes when recording the customers’ phone numbers or addresses. Today, data collection errors might cause the loss of important business insights.
Imagine if John’s employees had to collect manually all data about purchases on the online store. Crazy, right? Let’s pretend that one of the employees confuses the EAN code of a T-shirt with the one of a visor cap.
For days and possibly weeks, the data mistakenly shows the visor cap as the most sold item on the site. While in reality, the trending item was the T-shirt. If this happens often, John’s data will not mirror the real situation of sales on his website. This can lead him to make wrong decisions about which products to sell in the future.
Of course, we exaggerated things to make our example as easy to understand as possible. Still, for modern businesses, data loss or errors are a real problem. You want to avoid them as much as possible.
You don’t want to hire skilled accountants, salespeople, and IT personnel… only to have them perform boring data entry tasks, right?
With data automation, your employees can focus on what they can do best without worrying about mundane data entry tasks. This will make them more satisfied with their jobs, which may help improve your employee retention rate in the long run. And you won’t have to pay them that much overtime!
Let’s pretend for a moment that data automation doesn’t exist. And John wants to launch two new e-commerce sites other than the one that sells T-shirts. He would have to hire at least 5 new employees to handle all the data from the two new sites.
With data automation, scaling up is much easier and cheaper. John can keep using the same tool as before. Most data automation tools have pay-per-usage pricing plans, so he’ll only have to upgrade to a plan that includes more data rows.
We hope this article helped you get a better understanding of data automation and how you can use it to make your business more successful. With all these advantages, we would never go back to manual data entry, would you?