This is a typical data collection budget for most companies:
- 78% of data collection budgets are spent on data specialists who spend most of their time unblocking target site architecture and cleaning/formatting Datasets.
- The second largest expense (14%) is ‘server maintenance,’ which includes housing the servers and running cooling systems (as they overheat easily).
- Network cybersecurity typically costs 5% and includes firewalls and keeping outward-facing servers separate from internal-facing ones that host sensitive information.
- The smallest expense (3%) is for ‘software licensing fees, including a fee to integrate a data collection program with on-site hardware.
What expenses can companies cut from their budget?
Companies can cut their data collection costs by up to 54% by outsourcing this service. Purchasing ready-to-use Datasets will allow your company to get rid of the top three highest expenses, including:
- Data specialist salaries
- Server maintenance
- Network cybersecurity
Here is what the potential data collection savings may look like for your budget based on the cost of three different, ready-to-use Bright Datasets:
This estimate is based on the cost of three different, ready-to-use Bright Datasets, including:
- Sales volume (units and dollars), price, and product details for the most popular Amazon products.
- Top-ranked Crunchbase companies Dataset.
- Manta business Dataset of relevant industry companies located in Texas.
Other benefits of outsourcing data collection
When performing data collection in-house, companies must be GDPR (General Data Protection Regulation) and CCPA (California Consumer Privacy Act) compliant. This includes not collecting any Personally Identifiable Information (PII), and/or password-protected information. Companies that fail to do so risk future legal action that can seriously harm their business reputation and finances.
Bottom line: Outsourcing web data collection allows your company to shift legal compliance responsibility to the third-party data provider. You are no longer liable for any privacy issues that arise from collecting the very data your business relies on to make strategic decisions.
Companies that perform in-house data collection risk being exposed to low-quality data. Data collection networks perform real-time use case vetting, due diligence, and code-based abuse prevention. They also employ Machine Learning (ML) technology to validate target quality data before it is collected.
Bottom line: When outsourcing, companies can be confident that Datasets have been Quality Assured (QA), saving them time and other negative side effects that arise from using low-quality data.
In-house companies need to worry about network security constantly. When outsourcing to a third party, they review user activity logs, ensuring that any illegal/compromising network activities are shut down immediately.
Bottom line: Data network ‘log monitoring policies’ help give companies peace of mind regarding the security of the networks they use to route their traffic.
When an organization outsources data collection to another company, it allows them to focus on its core business, which in the end, boosts efficiency in operations. It should also be noted that companies/services that specialize in data collection do the job more efficiently than an ordinary company that is trying to collect data by itself.
Bottom line: Outsourcing data collection helps organizations to focus on what they do best while allowing the data collection service providers to deliver all the data they need to make crucial business decisions.
What to expect when working with a data collection service
This is the typical workflow for businesses working with a third-party data provider:
Step 1: Define the target website and Dataset, e.g., Amazon, top-selling items.
Step 2: Decide which format your team needs the Dataset in (e.g., JSON, CSV) and how often it needs to be updated (daily, weekly).
Step 3: Receive the pre-collected, ready-to-use Dataset directly to your team’s inbox or data bucket of choice (Amazon S3-AWS, Azure).