Product Data Collection in 2024: What it is & Top 3 Methods

We have talked about what product data management (PDM) is. It’s an important business function that impacts almost every component of the value chain. From manufacturing the product to selling and improving it, all functions benefit from production data management. 

But since you can not make bread without flour, you need to collect the product data before you manage it. As the importance of product data collection is dawning on business leaders, they are showing more interest in the topic (see Figure 1).  

We have curated this article to Explore what product data collection is

• Why is it important? 
• The top 3 methods of collecting product data
• And the pros and cons of each method 

What is product data collection?

Product data collection, or product data gathering, is a prerequisite of PDM. As the product/service goes through the different stages of its lifecycle, a significant amount of data is generated. This data includes:

• Details on product specifications,
• Material procurement information,
• Product design details,
• Price information,
• Information regarding the selling channels, etc.

Product data collection is the process of gathering all this data from different parts of the value chain, cleaning it, and onboarding it into your enterprise platform. 

The process might sound simple but can become complicated when the company has diverse product lines and works with a supplier network, each generating its own data.

Why is it important?

Product data collection is different from AI data collection since the data is not gathered from an outside provider but is generated within the product’s supply chain. 

For instance, since many companies work with third-party vendors/suppliers, product data collection is required to gather data from those suppliers and store it in the company’s own platform. Therefore, product data collection allows access to data to all relevant departments in the supply chain. For instance, if the e-commerce management team requires product specification data from the manufacturers, they can easily access it from the central platform.

Top 3 methods of product data collection

Product data collection can either be outsourced/crowdsourced, done manually, or automated with software.

1. Outsource or Crowdsource

If investing in expensive software and tools is not feasible for you, then you can outsource or crowdsource product data collection. You can work with a third-party service that specializes in collecting and organizing product data. 

These services typically provide a database of product information that can be accessed by users, who can then add, edit, or delete entries as needed. They can also gather data from different parts of your supply chain, organize it and transfer it to your ERP system.

Pros of outsourcing or crowdsourcing

• Cheaper than investing in expensive automation software
• Allows transfer of the responsibility of extracting and organizing the data to a third party
• Good for organizations with diverse product-lines
• If you are working with a crowdsourcing service provider, then they can also offer product data transcription in multiple languages.

Cons of outsourcing or crowdsourcing

• Data privacy can be an issue if the service provider does not follow data protection measures.
• The quality of the work can be compromised if the service provider does not follow quality assurance measures.

2. Manual product data collection

This is the usual pen-and-paper method. Like many manual business tasks, this also involves human errors, which cause inefficiencies in the process. A recent survey¹ found that almost half (48%) of the companies surveyed (500 companies) still relied on manual or excel based data entry methods. 

Consequently, around 88% of those companies could not take action from their data insights automatically.

Pros of manual production data collection

• Cheaper to implement and use
• No extra skills or pre-training required

Cons of manual production data collection

• Error-prone
• Relatively more time consuming
• Causes inefficiencies in the business
• Difficult to access manually collected data by other departments.
• Difficult to integrate into the company’s ERP platform 

3. Automated product data collection

Product data collection software

Product data collection can be automated in a number of ways. One way is to use product data collection software, which can be programmed to automatically collect data from a variety of sources, including online stores, manufacturer websites, and other online sources.

Using RPA and OCR 

You can also use robotic process automation (RPA) to automate product data collection. RPA software can be programmed to automatically extract data from online sources or the integrated ERP system of the supplier and then gather it into a database or spreadsheet. RPA can also be combined with OCR to capture unstructured product data such as paper-based supplier invoices

Using Web-scraping

Additionally, you can use web scraping techniques to automatically collect product data from online sources. Web scraping involves using a software program to automatically extract data from websites. This can be done manually or by using an automated tool.

Pros of Automating production data collection

• Eliminates human-errors
• Takes less time than other methods
• Improves overall business efficiency
• Good for organizations with diverse product-lines

Cons of Automating production data collection

• Expensive to implement automation solutions
• Sometimes requires re/up-skilling the workforce

To learn more about data collection automation, check out this quick read.

For more in-depth knowledge on data collection, feel free to download our whitepaper:

You can also check our data-driven list of data collection/harvesting services to find the option that best suits your project needs.

Further reading

• Top 4 Data Collection Methods
• Top 6 Data Collection Best Practices

If you need help finding a vendor or have any questions, feel free to contact us:

References

1. Challenging the One-Size-Fits-All Approach to Digital Transformation Post-2020