How to cope with difficulties of AI-based data gathering in 2023

Contents of aticle:

Data collection is a crucial business tool to be applied by small businesses and large corporations which buy HTTPS proxy lists. The development of automation methods result in machine learning  implementation. Dexodata’s best datacenter proxies in 2023 play a noticeable role for scaling business along with AI-powered web scrapers. Today our article is devoted to the handled complexity and the challenges yet to be overcome.

AI-based scraping solutions, trusted proxy websites, and issues solved by them

Acquiring public web data comes with obtaining hundreds of residential proxies, so free trial is crucial before purchase. Leveraging intermediate servers allows performing ML-driven procedures effortlessly. Today these techniques can cope with:

  1. Requiring only reliable URLs
  2. Applying and managing the most suitable proxies
  3. Sparing time and resources.

AI-based solutions create a reliable crawling path of URLs to same-themed sites. Inactive addresses are excluded while Natural Language Processing algorithms (NLP) determine relevant content.

Dynamic proxies for YouTube, Facebook, or Amazon deliver information despite limiting defensive systems on target web pages. Artificial intelligence decides if datacenter proxies are the best choice or is it necessary to buy HTTPS proxy lists of residential and mobile IPs. API is the method to:

  • Automate changing external addresses
  • Increase the number of hosts
  • Adjust digital fingerprints to geolocations of cheap social media proxies via antidetect browsers.

Intelligence obtained during machine learning is adjusted by experience gained in process. AI-enhanced data gathering models detect repeated patterns and apply this knowledge to similar target pages. Apart from sparing time for data processing, it also saves financials. Same is true for tagging collected data.

AI-driven tools have come a long way, but there are still some difficulties to overcome.


What obstacles are AI-powered data scrapers and geo targeted proxies overcoming now?


Ambitions to scale up must be followed with data-driven decisions. Harvesting public information online via YouTube proxies combined with AI is the optimal way to business insight. Taking into account the achievements of the described method, some downsides should be stated. We will list them in a shortened form as:

  1. Cost
  2. Access
  3. Efforts
  4. Excess
  5. Bias
  6. Lack.

Further we provide an explanation for these terms.


1. Cost


Implementation of AI-driven web analytics can be expensive according to amounts of information used during:

  • Machine learning
  • Collecting info phase
  • Structuring and storing it.

To deliver a stable connection one also needs both reliable costly hardware and software. It is important to enter into a contract with reliable load-resistant infrastructure. Ask for a free trial of residential proxies, datacenter or mobile IPs to choose the best datacenter proxies at the most reasonable prices.


2. Access


Required intelligence web categories may be challenging to obtain at scale. Online mobile platforms and e-commerce sites deploy defending filters. They interrupt web sessions characterized by multiple requests sent. AI-powered enterprises utilize advanced technologies to succeed. Still filtering systems are constantly developing, and it requires precise algorithms’ adjustment.

What challenges do AI-based data harvesting tools cope using proxies?

The list of complexities one faces during AI-based automation of data gathering procedure is extensive, but these difficulties can be overcome

Legislative uncertainty is another obstacle on the way of developing AI-driven data analytics systems. While public information was declared as free to extract, the definition of privacy is still uncertain.


3. Efforts


AI-driven initiatives may be unsuitable for collecting online info because of efforts needed to deploy, integrate, and maintain such a sophisticated tool.

Demand for highly skilled specialists with expertise in both data processing and ML implementation is among other drawbacks. It will take months before technology will become both affordable and easy enough to introduce in decision-making processes.


4. Excess


Big data market contains a variety of applicable insights. But excess of unstructured and semi-structured information needs rigorous diversification. AI-based scraping solutions should be implemented with structuring algorithms to interpret raw sets from data lakes. Other challenges are connected with:

  • Wide range of dynamic web sites and apps infrastructure
  • Checking the relevance of info sources
  • Seamless integration of multiple results.


5. Bias


Biased data is unsuitable due to lack of objectivity. The reasons include:

  1. Human interference
  2. Vague or outdated sets for machine learning
  3. Single decision commitment 
  4. Ways of obtaining, formatting and presenting outcomes.

Bias is observed in implementing intelligence gathered with AI-driven techniques. Shortcoming of transparency may turn management in charge against these tools or lead to their wrong interpretation.


6. Lack


Unbiased information, however, does not guarantee the accuracy of results. Every fourth enterprise which operates data-informed decisions faces lack of relevance in collected online materials, according to McKinsey research.

Other significant hurdles, which prevent AI-powered data collectors from further distribution, are lacks of:

  • Professional skills
  • Experience and knowledge
  • Unbiased sets for machine learning.

Insufficient awareness of AI-based advantages also prevents companies from transferring a range of functions to automated online data harvesters.


The future of ML-driven data management


Business intelligence and data-inspired predictions are based on the gathered info validity. AI-based solutions for processing unstructured big data have mainly dealt with acquiring reliable URLs, boosting the configuration and maintaining procedures, and managing the best datacenter proxies.

Remaining downsides will be overcome too. Global history of proxies for YouTube, socials, stock markets, etc. is an encouraging example. Because it is common practice to buy HTTPS proxy lists in 2023 for business purposes, not the lack of these solutions ten years ago. Dexodata provides free trial of residential proxies for enterprises and individuals to be convinced in the range and sustainability of our solutions.


Data gathering made easy with Dexodata