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Data Collection Automation: Pros, Cons, & 3 Methods in 2024

Data collection has become a core function for many businesses. From gaining consumer insights to developing and improving on AI/ML models in the business, fresh data is regularly required. 

However, manual data collection can be challenging, especially when the use case is unique and complex.

Automating your data collection process can help bypass some of those challenges by streamlining the process. Prior to leveraging automation in your data pipelines, learning its pros and cons can be worthwhile before initiating any investments in automation tools.

To help you better understand data collection automation, this article explores:

  • What is data collection automation? 
  • What are its pros and cons?
  • What are the methods of automation data collection?

What is data collection automation?

Automated data collection involves harvesting data from multiple sources without any human intervention.

This is done with automation software powered by machine learning. The machine learning model is trained through an algorithm that extracts the required type of data from online sources. Usually, in data collection automation, various methods are used to automatically extract data from online websites. 

This data can be structured or unstructured. In the latter case, the unstructured data is collected and processed into structured data. This can also be automated by combining  RPA and OCR.

What are some data collection automation pros and cons?

This section highlights some pros and cons of automating data collection in your business:

Pros of data collection automation

1. Reduced human errors

To err is human. Manually collecting data can be tedious and error-prone, leading to: 

  • Mistyping of data,
  • Duplication of data,
  • Missing out data, etc.

It can be commonplace. Automation can eliminate such errors.  

2. Improved data quality

Reducing the aforementioned errors can have a significantly positive impact on the overall quality of the dataset. This will ultimately result in more accurate results in data-hungry projects, such as a higher-performing machine learning model. To learn more about data collection quality assurance, check our this quick read.

3. Saved time and maintenance costs

Gathering data is a time-consuming and labor-intensive task if done in-house, especially in use cases where the data required is diverse.

For instance, if you wish to implement a speech recognition model in China, assigning your workforce to record audio data in Mandarin Chinese can be a challenge. Automating this can save your team’s time and allow them to tend to higher-value tasks. 

Using data collection automation tools also reduces maintenance costs. This is because the data needs to be regularly updated. If this is done manually, the data collector will have to recruit new contributors to maintain the dataset, which will increase the costs. 

Using automation tools can save the time that is consumed in maintaining such datasets.

Cons of data collection automation 

1. Quality issues

While automated data collection tools reduce human errors, they can also reduce the quality of the overall dataset. This is because raw data requires a quality screening process. For instance, when automated tools are gathering large-scale data without any human intervention, it can become difficult to screen the data for quality.

2. Costs of automating

While automation can be cost-effective in the long run, implementing automation tools can be expensive, and not everyone can afford them. Although the costs vary from the scale of the solutions that are being bought, Therefore, it is important to calculate ROI on such investments, and if the costs are unjustifiable, then other options should be considered. 


If quality screening and costs are important factors for your data-hungry project, then working with third-party data collection service providers can be beneficial. 

What are the three methods of data collection automation?

There are a few different methods of automating data collection, but the three most common are:

1. Data or web scraping

This method is used to extract data from sources that are not intended to be accessed or read by machines. Web scraping can be done manually but is often automated through the use of scraping bot that can mimic human interactions with a website or application.

2. Data or web crawling

Web crawling is a technique that involves automatically visiting websites and extracting data from them. This is different from web scraping because web crawlers will typically follow links from one page to another, while web scrapers will only extract data from the pages they are explicitly told to access.

See our guide on web crawling vs. web scraping to learn more about the differences between them.

3. Using APIs

Another common method is using APIs to extract data from online sources. Most data sources provide an API that can be used to access their data. This is the most direct way to collect data from a source and is usually the easiest to automate. That’s because data is typically collected in a structured format that can be easily parsed by a computer. 

For example, an API can be used to retrieve data from a remote database and then format it in a way that is usable by the local software program. This can save a lot of time and effort that would otherwise be required to manually collect and process the data. 

However, some data sources do not have an API, or the API is not well documented, making it difficult to use this method. 

You can also check our data-driven list of data collection/harvesting services to find the option that best suits your project needs.

For more in-depth knowledge on data collection, feel free to download our whitepaper:

Get Data Collection Whitepaper

Further reading

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Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Shehmir Javaid
Shehmir Javaid is an industry analyst in AIMultiple. He has a background in logistics and supply chain technology research. He completed his MSc in logistics and operations management and Bachelor's in international business administration From Cardiff University UK.

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