How to Harvest Localized Data for Business Intelligence

Localized data refers to information or content obtained from and restricted to access from a specific location. Data of this nature contains aggregated information that could provide businesses with insights into customer behavior and preferences of specific populations.

On the other hand, Business Intelligence (BI) refers to methods and means by which companies facilitate data collection, collation, analysis and, presentation. However, no definition or concept of BI is complete without including the data and the quality of the data.

For an international business, localized data is especially important to the quality of BI systems because of its peculiarity to the population of interest. However, countries are increasingly localizing data, citing security reasons.

This article will explain how localized data impacts business, what they are used for, and how to harvest them with a location proxy.

How Does Localized Data Impact Business Intelligence

In business, data helps identify patterns and correlations that are not usually obvious. Based on the scope of data collection, data can be local or international. Local data originates from the home country of the business, while international data is obtained from foreign markets.

Localized data is the type of data that businesses seek to obtain as part of an international data collection operation. With proper analysis and interpretation, this type of data can give businesses an edge in a business sphere that can seem crowded. That said, here are specific ways that Localized data can affect business intelligence:

• Improved accuracy of insights. Data informs insight, and accurate data means accurate insights. Localized data is the most accurate form of data obtainable on a population. For instance, any survey of a population can identify customer distribution, but localized data can accurately chart the distribution in a region.
• Specific cultural and regional relevance. Localized data can point to cultural beliefs and preferences affecting consumer behavior. For instance, an international fast-food business looking to operate in India might be aware of the general stance on beef consumption. However, localized data could help point out that the Hindus make up the vast majority of the population, thus ruling out beef-containing meals as a potentially nationally successful meal.
• Accurate assessment of expansion prospects. Businesses planning to expand into new markets can better assess the security of their position by using localized data. In this case, they can use localized data to understand local competition, determine demand and preferences, evaluate regulatory measures, and optimize the efficiency of their operations.
• Fairer analysis of global performance. Using localized data, businesses can assess the performance of products and services with more contextual knowledge. So they can make smarter decisions and develop strategically sound plans.

How Web Scraping and Proxy Servers Help Localized Data Collection

Localized data may frequently have geographical restrictions to prevent residents outside the region from gaining access. A web-scraping tool and a location proxy can help businesses work around these restrictions if they exist.

The location proxy server receives the internet traffic from the computer collecting the data. Once received, it then forwards the traffic (in this case, a scraping request) to the target servers of the website. This means that the target website would recognize the traffic as originating from the location of the proxy server.

On the other hand, after the target website receives the scraping request, the web scraper downloads the HTML contents of the site and extracts the needed data from within. In this way, the location proxy and web scraper complement one another to bypass geographical and IP restrictions on data and automatically retrieve them.

How the Quality of Proxy Providers Affects Localized Data Collection

A large pool of IP addresses and variability in locations within the pool are attractive features for business proxies. A location proxy provider with many addresses in many countries is beneficial for localized data collection in the following ways:

• Access to more locations. More variation in the countries means the business can access localized data from more locations if needed. Some proxy providers have access to over 150 countries. Such a proxy would allow its users to access data from a vast pool of locations, increasing the reach of their data collections. For instance, a business owner in the UK can extract information from a US website using a US proxy and vice versa.
• Efficient load distribution and scalability. Any proxy provider with many addresses allows users to rotate their proxies during data collection. However, the size of the address pool within a location can make rotations more efficient. A larger pool means that users do not have to reuse an address (and risk an IP block) very often.

Conclusion

There is no business intelligence without data, and the more accurate the data collected, the more valuable the intelligence obtained would be. Considering the importance of localized data to BI, businesses prioritize obtaining them, regardless of the obstacles or restrictions surrounding their acquisition.

The result of this in the business world is that companies hire the best people, utilize the best technologies, and try to collect localized data. Web scraping and proxy providers work in tandem to make this possible. Any business looking to positively distinguish its performances from those of its competitors should be doing the same.