Data Collection Cost – Estimate and Reduce

Data collection is essential to any organization hoping to find value in the information it holds. However, if your company wants to set up automation capable of extracting large amounts of data from web pages, you need a thorough understanding of what those costs will entail. While the up-front investment in data proxies and technology can result in more profitable decision-making, responsible CTOs still want to ensure they’re responsible when budgeting for such projects.


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Establishing a Data Collection Plan

Establishing a Data Collection Plan

Successful data collection efforts start with a well-thought-out plan. Identify what information assets you want to collect from the internet. You also need to consider who will use the information, how to keep it viable for interested parties, and how you will break the data down and make it useful for your organization.

Setting up a data collection plan helps you identify the costliest phases and ways to streamline your expenses.  You can figure out ways to be more efficient and not waste company resources on anything irrelevant to your data collection needs. You can ensure that the money spent on any information-gathering project brings real value to your business.

1. Identify the right questions to answer

Develop questions that cover your reasons for setting up data collection using web scraping tools or other technology. The goal is to ensure you pull in data relevant to your organizational needs. Center your questions on the current state of your data processes and how you can improve. You also need to identify essential metrics that determine whether your efforts are successful.

2. Determine what data Is available

Once you’ve outlined your questions, go over what data exists that answers the questions you outlined in the first step. One single data point may be enough to cover more than one answer. Next, list all essential data points needed to cover the needs of your data collection project. Again, you want to avoid wasting money pulling information that will not be relevant to business, analysis, or technology users.

3. Decide how much data you need

How much data is going to be necessary to create informative data sets? The amount of data brought in should be extensive enough to help your analysts spot patterns and trends that help drive decision-making. Write out how much information you’ll need for each data element.

4. Decide what technology to use

Websites are rich resources for gathering information like statistics and product price points from competitors. Websites owned by business service providers, government entities, and other organizations also offer open-source access to their data. Web scraping tools, supported by web proxies, are an effective way to collect information quickly and much faster than having multiple employees manually copy and paste data into spreadsheets.

5. Think about where you’ll collect the data

Once you’ve decided upon what technology to use for your data collection process, map out the websites containing the information your company needs. Automated software goes a long way toward helping you quickly extract and download information from target websites into organizational databases for further analysis.

6. Determine how to analyze the data

Are you planning to review only samples of the data or hoping to parse through the entire population? Your answer here may cause you to go back and refine your answers to previous questions. If it’s impractical to measure every element of data, then you want to think of what you’ll need to set up relevant data samples, which sampling method to use, and the sample size.

Achieving Success Through Data Collection

Achieving Success Through Data Collection

Establishing a data plan helps with making sure you’re covering the following goals for your company’s data goals:

  • Collecting information relevant to your specific products or service
  • Extracting all the data points needed to hit your business targets
  • Ensuring that you’re not missing out on crucial information necessary to make informed data-driven decisions

Data collection requires extensive knowledge. It’s good to have someone experienced driving the project and helping your company find the most effective ways to collect data. Examples of that include the following:

  • Ensuring you’re tapping into the site map and important directories of a website
  • Making sure your web scraping tools go through all page categories
  • Setting up algorithms to discover relevant URLs

Factors Influencing Data Collection Costs

Factors Influencing Data Collection Costs

You must also identify every factor influencing the overall data collection price tag. Let’s review some of the most common issues that can add to your overall data acquisition costs.

1. Collection issues

Many companies use anti-scraping techniques to prevent automation from capturing the information stored on their website. One popular method employed is blocking the IP address used by the web scraper. Another is to look for unusual movements that don’t resemble a human’s. If the site detects what it believes to be a robot, it deploys a test to confirm that suspicion.

Other issues that a web scraper might run into while collecting data include the following:

Dealing with a dynamic site — Many websites use JavaScript to render content. While it makes a website more user-friendly and appealing to users, it can be challenging for web scrapers to manage. Scrapers typically send an HTTP request and get an HTML response back. However, that may not happen If the site constantly loads information while executing JavaScript within the browser.

Header and server limitations — A website’s server may place restrictions designed to an IP ban or a header check to block web scrapers. A header check involves looking at the information contained within the HTTP header. If the site finds anything suspicious, it might display HTML that provides no useful information or ban the automation entirely.

CAPTCHA — Many websites use CAPTCHA to validate whether a bot is attempting to access their site. However, it’s often challenging for web scrapers employed for legitimate purposes to get past the safeguard. Failing a header check is usually enough to trigger a CAPTCHA check. There’s also reCAPTCHA, which forces a web scraper to select a single checkbox to prove it’s a human. It’s designed to look at the path used by the automation, including mouse movements.

IP blocking — Failing a CAPTCHA test or other test sent out by websites can lead to blocking the IP used by the web scraping automation. Ideally, you’ll have technology that helps you avoid blocking an IP address. The availability of a rotating proxy pool and a legitimate fingerprint for your automation can help you avoid that fate. You should factor the cost of maintaining a proxy pool into your overall data collection costs.

2. Technology

Technology selection also significantly affects how much you pay for your data-gathering efforts. While it might be tempting to go with the least expensive alternative, you should evaluate your choices in comparison to whether they’re capable of helping you hit the goals outlined in your data collection plan. In addition to the web scraper, you should consider the following elements when estimating your costs:

  • Proxy servers — Proxy servers are essential to successful web scraping sessions. Datacenter proxies are ideal for companies looking to conduct ethical web scraping for business purposes. The way your proxy setup helps you deal with issues that your web scraper might encounter. The basics you’ll need from your proxy choice include access to many IP addresses, a way to execute precise targeting, and the ability to open multiple sessions simultaneously.
  • API detection — Application programming interfaces (APIs) act as middlemen between software components. They make two-way communication possible and are critical in helping companies save money and optimize resources for data collection.

3. Data Cleaning

The data cleaning process is essential when turning the mountains of information returned by web scrapers into valuable business data. It involves figuring out what’s incomplete, whether the web scrapers brought back duplicate information, and if there are inconsistencies or unwanted data to remove. As a result, data cleaning increases data quality and prepares it for further analysis.

Even the most precise web scrapers sometimes bring back information irrelevant to an organization. For example, your automation might look for industry sales statistics from a specific site and get other data back into its web net. Therefore, you’ll need a way to extract the information you want so that your final datasets only contain relevant data points.

Below are some reasons why data cleaning is essential after you’ve brought back data with web scrapers:

  • You get a more accurate picture and ensure you have reliable information on which to base business decisions
  • You speed up the ability of other departments, like sales or marketing, to review data and use it to create reports or target potential leads
  • Your financial department is more capable of assessing risks that might impact the company
  • You improve customer service by getting a clearer picture of your audience and a better understanding of what they like or would like to see improved
  • Your organization is better positioned to meet any compliance standards by working to ensure the accuracy of your data

Data cleaning keeps you from using incorrect data sets that may affect business processes. You also build confidence in business users, helping them avoid the frustration of realizing they’ve been using flawed data in their daily workflows.

Checking data accuracy after web scraping sessions prevents customer frustration because of poor customer service. In addition, providing your organization with valid data saves you money by keeping you from wasting time and resources trying to clean up after a major information mishap.

Web Scraping and Data Collection


Now that we have a better understanding of different ways to establish cost-effective data policies let’s take a closer look at web scraping, one of the most effective ways of collecting data from websites. It’s especially ideal if your organization is focused on collecting qualitative data, information that’s not defined as a number or other quantifiable value.

Examples include information stored in case studies, photographs, or product descriptions.  Web scrapers remain popular because it’s possible to adapt them to various use cases for different industries.

People and machines generate a staggering amount of information every day. Even if you narrow that down to only the data required for your organization, think of how many surveys you’d have to send out to collect the same information you could get from a website through web scraping.

The web scraping process relies on automated robots designed to crawl through websites, extract data, and send it back for storage. They start by breaking down the website to its basic construction, including the HTML, then look for data to gather based on predefined parameters. Once it’s found, they store the information in an Excel or CSV file to make the information readable.

Well-developed web scrapers save companies a lot of time and money. For example, you don’t have to pay employees to locate and copy website information. In addition, web scrapers move much faster and more precisely, which cuts down on human errors that might add to the costs of data cleanup.

You can set up web scrapers with specific directions about what information to target. That helps you avoid bringing back unwanted information that must be sorted out. It’s easier to gather multiple data categories, including qualitative and quantitative information needed for business processes.

Online data collection tools benefit organizations and save you money by:

  • Facilitating analysis — Having reliable web scrapers and data proxies at your organization’s disposal improves the reliability of your business data. Data cleanup becomes faster and more efficient, allowing analysts to create relevant data sets for forecasting and other business processes. As a result, you can improve the quality and accuracy of the information passed around your organization, strengthening your business output.
  • Improving understanding of customers — Today’s customers expect a lot from businesses and have no qualms about going elsewhere if they don’t meet their needs. Web scrapers help you set up a lean, efficient method of gathering consumer information. Instead of flying blind, you have a clearer picture of what your company may be doing right and ways you can make improvements that stop customers from fleeing to competitors.
  • Helping you find better business solutions — Web scrapers can help you track the success of your business, like the effectiveness of marketing campaigns and how much traffic gets generated by your latest content marketing efforts. Use web scraping tools to determine the reception of your latest campaigns across different channels. As a result, you can detect issues, analyze the problem, and produce better results.

Going In-House Versus Outsourcing

Going In-House Versus Outsourcing

If you’re thinking about going with web scraping to save on data collection costs, you’ll need to decide whether to develop a custom solution or outsource the process to someone else. First, think about what you’ll need from your web scraper. Use the questions covered in your data collection plan to help you determine what information you’ll need, how fast you need it, and the difficulty in getting data from your chosen web targets.

Going with an in-house solution means you can customize it to fit your business needs. If something changes in your requirement, you can direct your developers to make any customizations and deploy the changes on your timetable. You’ll need a team who understands how to make the technology effective and can make refinements over time as needed.

You’ll also need to determine if your in-house team would be capable of scaling up solutions to grow with your data needs. For example, if they lack the necessary experience or time to build and maintain web scrapers, you might have more success working with a third-party provider.

If you go that route, look for someone with the time and skills required to build your optimal solution. Another advantage of outsourcing your web scraping needs is that the outside provider can prioritize them.  That frees your other IT personnel to focus on other critical technology functions.

Tips on Estimating Data Collection Expenses

Tips on Estimating Data Collection Expenses

As we stated at the beginning, well-defined requirements and a detailed data collection plan are vital to helping you figure out your overall project costs and the technology you’ll need. Make sure you capture information like how much you’ll have to pay for effective data proxies and what it will take to gain access to your required data.

One way of calculating overall costs is multiplying the number of data sources you’ll need by how much it will cost each month to access the information. You’ll also have to look at what your organization would pay for expenses like building additional infrastructure, accessing APIS, and setting up computing resources.


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Lower Data Collection Costs With Better Proxies

Lower Data Collection Costs With Better Proxies

Your choice of data proxy goes a long way in determining the success of any web scraping technology employed for your data collection needs. Rayobytes is known for its quality selection of reliable proxies that support your choice of web scraping technology. Our data center proxies offer organizations multiple benefits, including the following:

  • Maximum redundancy by providing 9 ASNs
  • Proxies available from 26 countries
  • Over 300,000 available ISP to help you avoid downtime because of bans
  • 25 petabytes of space per month to support your data collection needs

Click here to get more information about pricing for Rayobytes proxies and available features.

The information contained within this article, including information posted by official staff, guest-submitted material, message board postings, or other third-party material is presented solely for the purposes of education and furtherance of the knowledge of the reader. All trademarks used in this publication are hereby acknowledged as the property of their respective owners.

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