Defining, Collecting, Structuring, and Delivering Data

Data collection entails crawling the internet as a real human would and retrieving open-source datasets. It also requires cleaning duplicate information as well as structuring data with tools like Natural Language Processing (NLP). Data Collector does all of this autonomously.
Defining, Collecting, Structuring, and Delivering Data
Nadav Roiter - Bright Data content manager and writer
Nadav Roiter | Data Collection Expert
17-Nov-2021
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In this article we will discuss:

The methodology of data collection 

Bright Data’s data collection solution fully automates the data collection, cleaning, and structuring process so that your teams and algorithms gain access to ready-to-use datasets.

But what is the methodology behind this data collection process?

The Data Collector (DC) crawls the internet like a real user. It gathers public information in the same way as a human would with a mouse and keyboard. DC loads a starting page, clicks buttons, waits for loading pages to disappear, and finally decides that the crawl is done.

Once the crawl is completed we pull the interesting data points out of the pages that the server sent us over the course of the browsing session.

A simple data collection operation will just send HTTP requests to a website using something like Python or Nodejs. Any site that pays attention to the signals of automation will immediately know that this isn’t a real user and will try to hide the data so that it can’t be seen. The Data Collector tool uses real browsers and carefully manages their fingerprints and geolocation in order to avoid this problem completely.

Data Collector uses layers of behavior modification systems to adapt to new ‘data hiding methods’ and ensure that you can always gain access to the public data you are targeting.

Our system uses a flexible, scalable browser pool to ensure that the collector is never your bottleneck in collecting the online information you are after. Additionally, it will automatically calculate a respectful rate limit that doesn’t damage the target site so that you can set it, and forget about it.

Data cleaning, and structuring

Once the target data is collected it then needs to be cleaned and structured. When datasets are first collected it very often contains:

  • Duplicate information
  • Incomplete data points
  • Corrupted files
  • Data that is incorrectly formatted
  • Information that is mislabeled

The latter is very common in the music industry, for example. In this context metadata such as ‘artist name’ or ‘record company’ can be mis-cataloged due to mislabeling causing companies to lose out on huge sums of money in terms of royalty payouts. 

Working with pristine datasets is a very crucial part of utilizing data that will provide your corporation with valuable output. AI, and ML-based algorithms, for example, are trained by being fed data enabling them to identify, and analyze patterns in their operational or ‘maturity phase’. If the data fed to algorithms is corrupted in some way during the training phase (e.g. significant time lag or geolocation errors) then output, insights, and business decisions will be skewed. 

There is no ‘one-size-fits-all’ data cleaning methodology as this process differs based on the target dataset in question. Here are a few examples of techniques that may be implemented in the data cleaning process:

  • Fixing structural/naming inconsistencies – Datasets need to be categorized in one way or another which is where naming conventions can come in handy. A good example of this is a Software as a Service (SaaS) platform looking to identify competitor pricing in order to inform its dynamic pricing strategy. They may collect data from different competitor sites where monthly plans are listed as Price per month, PPM, $500/m and other variations of the same monthly pricing scheme which is labeled differently. Unless these are fixed, they will be categorized differently and your comparative pricing won’t be accurate.  
  • Removing duplicate or irrelevant information – Many times data is collected, and cross referenced from multiple sources such as different social media platforms pertaining to the same subject matter. This creates a possibility for your team to catalogue duplicate data points such as vendor data. Irrelevant information may include social posts that appear on an account but do not pertain to your product offering. This information needs to be searched for (either manually or automatically) and deleted in order to streamline the efficacy of the programs ingesting this information. 

Structuring unstructured data 

A large majority of the web data that is currently available for collection on the internet is unstructured. Merrill Lynch put that figure at a whopping 80%. Unstructured data does not have a data model, meaning there are no labels, fields, annotations, or properties which help machines identify data points, and how they relate to one another. Unstructured data can very often have a lot of text or be in HTML format which is easy for people to understand but hard for machines to process. This means that in order for data to have value for your business it most likely needs to be structured. 

There are many ways to go about structuring unstructured data, here are a few examples:

  • Finding patterns of interpretation using methods such as Natural Language Processing (NLP), and text analytics 
  • Manual tagging of metadata or parts of speech for further text or tag-based structuring 

Data collection automation 

As mentioned at the beginning of this article, Bright Data’s Data Collector tool completely automates the data collection, delivering datasets to team members, and algorithms in a ready-to-use format of your choice:

  • JSON
  • CSV
  • Excel 

You can also define your delivery preferences meaning if you want to receive real-time data as it is collected or an entire dataset once the collection job has finished, deciding where exactly you want it sent:

  • Webhook
  • Email
  • Amazon S3
  • Google Cloud
  • Microsoft Azure
  • SFTP

Data Collector runs a sophisticated algorithmic process based on industry-specific know-how in order to seamlessly clean, match, synthesize, process, and structure the unstructured data before delivery. It is a tool that takes the entire process described above and automates it in order to provide you with a real-time, zero-infrastructure operational data flow. On top of that, it also uses retry logic, adapting and readapting itself to site blockades so that you always gain access to the open-source data that you are targeting. 

The bottom line 

Data collection is part art, part science –  however you choose to approach it, no matter your data methodology, it is a labor-intensive endeavor. Data Collector provides an alternative for companies that want to have ready-to-use datasets delivered directly to team members and algorithms so that they can focus on strategy, creativity, and core business models. 

Nadav Roiter - Bright Data content manager and writer
Nadav Roiter | Data Collection Expert

Nadav Roiter is a data collection expert at Bright Data. Formerly the Marketing Manager at Subivi eCommerce CRM and Head of Digital Content at Novarize audience intelligence, he now dedicates his time to bringing businesses closer to their goals through the collection of big data.

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