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Buying vs Scraping Data in 2024: Exploring the Pros and Cons

Andrius Ziuznys

Updated on Feb 02, 2024
Published on Jun 28, 2023
buying vs. scraping data

In today’s data-driven business environment, data is often hailed as the new oil. But unlike oil, data is not a finite resource. Instead, it is a vast, ever-growing ocean that is constantly being refreshed and expanded.

Consequently, this brings us to a critical crossroads – to buy or to scrape? To dive in headfirst into this ocean and gather data yourself, or simply buy it from a vendor who has already done all the legwork for you?

Let’s break it down.

First priority – data quality

No matter the route you choose to acquire your data, its quality is paramount. Data serves as the foundation of your decision-making and strategic insights. Its accuracy, completeness, consistency, freshness, uniformity, and uniqueness are all crucial factors that determine the success of your data-driven endeavors.

Data quality, a pivotal factor in your acquisition decision, consists of six key aspects:

  • Accuracy. Check whether the data is authentic, correct, and accessible.
  • Completeness. Data should be complete and not missing any major elements.
  • Consistency. It doesn’t contain conflicting information or illogical entries.
  • Freshness. Data is current and up-to-date.
  • Uniformity. The datasets’ units of measurement are consistent.
  • Uniqueness. The dataset is original and does not contain duplicates.

data quality dimensions

Remember, having a large volume of data can offer a broader perspective, provided it's fresh, stable, and well-structured. 

Now, with this understanding, let's delve into the comparison of buying versus scraping data.

Buying data vs scraping data

So, are you going to scrape data yourself, or would you rather have someone else bring it to you?

Let’s have a look at the differences between the two in this comparison table.

Aspects Buying data Scraping data
Effort Low High
Cost Varies Varies
Freshness As per provider On demand
Stability Usually high Depends on scraping process
Structure Predefined Customizable
Quantity As per provider As much as you can handle

Buying data is similar to a prepared meal. It’s convenient, quick, and requires minimal effort on your part. You get a structured and usually stable set of data that you can readily use. The catch? It might not be as fresh as you’d like, and it might not cover all the specific areas you’re interested in.

On the other hand, scraping data is like cooking your own meal. It demands more effort and technical skills but allows for a greater level of customization. You get to decide what you want, when you want it, and how much of it you want.

However, the dish might not always turn out as expected. The stability of your data depends heavily on your scraping process, which can be impacted by website layout changes, anti-scraping measures, and other technical roadblocks.

From a technical perspective, scraping data is a difficult and ongoing process. Even if scraping helps you get the freshest data possible, that data must be re-scraped periodically for it to stay up-to-date.

However, if you only need a list of companies or employees for today and you don’t need that list to be updated, then it’s likely more cost-effective for you to scrape that single list yourself, provided you have the means for it.

All in all, if you’re looking for business growth, it’s often better to buy datasets and let data providers take care of the data’s accuracy, freshness, and overall quality.

How to know which one to choose?

The choice between buying and scraping data ultimately boils down to your specific needs and resources. But how can you define these? Let’s continue with the food analogy to make it clearer.

If you’re planning a feast with numerous intricate dishes (a project requiring diverse and up-to-date data), you might want to go to the market (scrape data) yourself. This allows you to handpick the freshest ingredients (data points) according to your unique recipe (project).

However, if you’re preparing a straightforward dish and guests are arriving soon (a specific project with a tight deadline), you might prefer to order pre-made ingredients or even a ready-made meal (buy data). This way, you get the meal prepared quickly, saving you the time and effort of shopping and preparing everything from scratch.

Consider the following factors to make an informed choice:

  • Project specifics. Is your project demanding unique, complex, and ultra-current data or would pre-packaged, structured data serve your needs?
  • Technical capability. Do you have the necessary expertise, tools, and resources to effectively scrape data?
  • Time. Do you have the luxury of time to gather, clean, and structure the data, or do you need immediate access?
  • Budget. Can you afford a potentially higher upfront cost for ready-to-use data, or would you rather invest time and resources to acquire it at a potentially lower cost?

data collection challenges

Challenges of data collection

Data collection, be it through buying or scraping, is not without its challenges.

When buying data, you might encounter issues such as:

  • Data relevance. Is the data you’re purchasing actually relevant to your needs?
  • Quality assurance. Is the data fresh, stable, and structured?
  • Cost. Are you getting good value for your money?

On the other hand, when scraping data, challenges might include:

  • Technical hurdles. Websites often implement anti-scraping measures, and their structures can change without notice.
  • Legal implications. Not all data is free to scrape, and privacy laws like GDPR impose restrictions.
  • Time and resource consumption. Scraping data is a time-intensive process that demands significant resources.


Both buying and scraping data have their pros and cons. However, at Coresignal we always lean toward buying data because we know that value comes from the data analysis stage and not collection.

Therefore, we’re here to offer the best of both worlds. High-quality, fresh, and structured public web data that eliminates the hassle of scraping while ensuring the data’s relevance and timeliness.

This way, you can focus on leveraging the data rather than acquiring it and improving your business operations and success.