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Data collection: main sources and techniques

In today's data-driven world, online data collection techniques have become increasingly popular due to the vast amount of information available on the internet. Automated online data collection techniques from numerous sources, such as web scraping for internet websites, or APIs, CRM, ERPs, and databases, have made it possible to gather data in a faster, more efficient, and cost-effective manner.

However, there are several challenges associated with these techniques that need to be addressed.

Data collection techniques and sources

Web Scraping for external data, such as websites

Web scraping is the process of extracting data from websites using automated scripts. It is a powerful tool for data collection but can be a challenging technique due to the legal implications of accessing data without permission. Web scraping can also be technically challenging, as websites can change frequently, which may require constant updates to the scraper.


APIs for internal or external data

APIs, or application programming interfaces, are software intermediaries that allow two applications to communicate with each other. APIs can provide a reliable and structured way to access data, but their limitations can also be a challenge. For example, not all data sources have APIs available, and even when they do, there may be limitations on the amount of data that can be accessed.


CRM for customer related internal data

CRM, or customer relationship management, is a system that companies use to manage interactions with customers. CRMs can be a rich source of customer data, but accessing this data can be difficult due to privacy concerns and technical limitations. Additionally, the data in a CRM may not be easily accessible, as it may be siloed across multiple departments or systems.


ERPs for operations related internal data

ERPs, or enterprise resource planning systems, are software applications that companies use to manage various business processes, such as accounting, human resources, and supply chain management. ERPs can provide valuable data for analysis, but accessing this data can be challenging due to technical limitations and the complexity of the systems.


Databases for all sorts of internal data

Databases are a structured way to store data, but accessing data from them can be challenging due to technical limitations and the need for specialized skills. Additionally, data in databases may be siloed across multiple systems or departments, making it difficult to access and analyze.


Possible Data Sources

Despite the challenges associated with automated online data collection techniques, there are several potential data sources that can provide valuable insights. Social media platforms such as Twitter and Facebook can provide real-time data on customer sentiment and behavior.


Open data: an huge, untapped data source:

Publicly available data sources such as government websites and open data repositories can provide data on a variety of topics, including demographics, health, and economic indicators.

These external data sets can be collected and used alongside internal data for optimal results. The process to mix internal and external data is called “Data Fusion”.


Conclusion

Automated online data collection techniques such as web scraping, APIs, CRM, ERPs, and databases can provide valuable data for analysis, but they also present challenges that need to be addressed. Legal implications, technical limitations, privacy concerns, and siloed data are just a few of the challenges that need to be considered. However, by carefully selecting data sources and using the appropriate tools and techniques, businesses can harness the power of data to gain a competitive advantage in today's data-driven world.


Basedig provides data collection services. Do not hesitate to contact us to share your project.




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