How To Lower The Cost Of Data Collection

Crawling a target’s site map or directories?; Maintaining an extensive team of engineers, and DevOps personnel?; Cleaning, and enriching raw data? Ready-to-use ‘Datasets’ puts all these in the rear-view mirror allowing you to focus on your core business
How To Lower The Cost Of Data Collection
Aviv Tal
Aviv Tal | Director of Data Partnerships

In this article we will be discussing four aspects of how Bright Data’s pre-collected, ready-to-use Datasets can reduce your company’s data collection costs:

The cost of know-how 

Being able to achieve full discovery of all relevant pages in order to attain your company’s data-driven goals entails a lot of work. 

  • Whether you are trying to collect all products that are relevant to your digital retail business on an eCommerce marketplace
  • Trying to extract complete company profiles from a business directory
  • Or looking to map the social sentiment pertaining to your specific product/service by collecting comments and posts on social media influencer accounts 

All these types of data collection jobs require extensive know-how, and experience in terms of finding the most efficient and effective data collection methods. One example of this, are well developed discovery methods based on crawling the target’s site map or directories (if they exist), scanning all page categories, and sub categories or using semi-random URL discovering algorithms.

When purchasing a ready-to-use dataset, you can enjoy Bright Data’s extensive experience, and technological capabilities. This includes enjoying the output of our proven discovery (finding all pages in a domain) algorithm, retry logic, and CAPTCHA-resolving techniques (implemented on a per-domain basis) that help achieve quicker results, and attain higher success rates. 

All of this data unblocking, and site mapping have already been dealt with, and the datasets delivered to you are ready to be used by your team. 

The cost of technology

Data collection is a costly process when performed in-house. It requires an extensive team of engineers, as well as IT and DevOps personnel. It also requires building, and maintaining relevant hardware, and software. This includes:

  • Cloud servers
  • Networks
  • Application Programming Interfaces (APIs)
  • Ongoing operational changes and code enhancements (especially target site architecture changes) 

‘Datasets’ is offered as a ‘managed end-to-end service’ meaning that Bright Data maintains an army of developers, deals with network maintenance, has cloud infrastructure, and data centers located around the world. Simply put, at Bright Data we have the infrastructure and high-end technology, making this available to you without you having to take on the burden of maintenance, and upkeep, 

On the operational maintenance end, Bright Data has code-based prevention and technological response mechanisms. Practically speaking we employ a custom made Build-and-Test (BAT) system, enabling us to release almost 60 upgrades to our systems on a daily basis.

All of this carries with it immense operational costs, and overhead as well as ongoing investment in Research, and Development (R&D). When you buy ready-to-use datasets you don’t need to think about any of this, and are afforded budgetary agility on a per-project basis. Instead of constantly maintaining your systems, and teams, you can simply leverage ‘Datasets’, so that you get to decide when you need access to data, and when you do not.  

The power of many 

The ‘power of many’ is a principle which is gaining popularity as seen in the context of the sharing economy. When you and 50 other people stay in a holiday rental located on Madison Avenue, the cost is manageable as it is divided up amongst a large consumer group. It gives access to parts of society who otherwise could only dream of spending a weekend sleeping in one of Manhattan’s most sought-after addresses. 

This same principle applies to data collection – when you perform data collection yourself you are very limited in terms of scale, access, and upkeep. When purchasing a Dataset, particularly a more popular one, the cost of building and maintaining the dataset (i.e. ensuring that the information is updated on a regular basis) is shared among all the customers of the dataset, thus reducing the cost for each individual participant. 

The cost of data cleaning, and enrichment 

Raw, open-source data collected directly from websites, typically requires further processing such as:

  • removing duplicate data points/values  
  • finding and cleaning corrupted data files/fields
  • enriching data with additional information (either from within the dataset, such as calculating a social media profile’s engagement score or from external sources, such as adding the main headquarter address to a company profile). 

Additionally, when attempting to collect data from an entire website or even a large subset there is a lot of data that gets caught in your ‘data net’ that is irrelevant to your goal. For example, if you are scanning eCom product listings and are particularly interested in pricing, shipping time, and model/make, you may also have product images and product SKUs (stock-keeping units) in the mix. You then need to have your teamwork on extracting only the data points relevant to your business.

‘Datasets’ are sold after all of these processes have been skillfully carried out, eliminating the effort, and time required to clean and enhance your raw data. We also allow smart filtering on the dataset, allowing you to focus only on records and data points relevant to you.

The bottom line

Data collection is a massive undertaking that requires time, technical expertise, demands maintaining a team of skilled labor, and the hardware/software needed to successfully complete complex jobs. Datasets help you push the ‘fast forward’ button so to speak, they allow you to eat the fruits without having to cultivate the orchard.

Aviv Tal
Aviv Tal | Director of Data Partnerships

Aviv Tal is the Director of Data Partnerships at Bright Data. His background is in the retail, IT, payment, and automotive market segments. He mainly focuses on defining our company’s vision, formulating an agile roadmap, and orchestrating deliverables through internal development, acquisition, and partnerships.

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