The best business decisions are based on facts, facts created from concrete, real-time, user data gotten from different parts of the internet. These decisions can influence sales, revenue and promote growth.
Every wise business owner that prioritizes business intelligence (BI), therefore, knows that acquiring useful user data regularly and frequently is an important task. This process, known as data acquisition, is not as straightforward as we would like to think and is divided into several steps and processes, including web scraping and scraping API.
Executed properly, all processes combined can yield tremendous results. Still, a less than full understanding of the concept can easily lead to several challenges, such as failure to achieve anything significant.
Today, we will consider what data acquisition is as well as consider how it is done.
What is a Data Acquisition?
Data acquisition is a three-part process that involves data extraction (web scraping), data transformation, and loading of the extracted data. The process extends from when the data is retrieved to the point where it is processed and stored in a local computer.
The extraction is usually done using web scraper bots and proxies to target different data sources and retrieve data (usually in HTML formats) from them. These data sources generally include anything from websites to social media platforms; wherever useful data can be gathered.
The transformation stage generally includes data cleansing. Data is usually obtained in a raw, unstructured form and can only become useful to businesses after it has been cleaned and transformed. This transformation helps convert the unstructured data into a structured, usable format such as CSV or spreadsheets.
The final stage includes loading the transformed data into whatever medium the company has provided as their data warehouse. Data stored in this place is easily accessible and can be used for analysis immediately or later.
Why Data Acquisition is Important for Businesses
Data acquisition is a crucial business operation for several reasons, and below are some of the reasons why businesses consider it as highly important:
Brand Monitoring and Protection
As a known fact, the cheapest and most effective way to protect a brand in today’s digital world is to monitor the brand closely and see where on the internet it is appearing as well as what people are saying about it. Information acquisition can be correctly used to gather data that can effectively monitor a brand to protect its image and other valuable assets properly.
Price and Competition Monitoring
Brands also use data acquisition to monitor different prices in different marketplaces to maximize sales and profitability. It is seen as one of the best ways to gather enough information about the competition – information that can outperform the competition and stay ahead of them.
Conducting Market Research
Deep and comprehensive market research is an important exercise that a brand has to perform before manufacturing a new product or breaking into a new market and the best way to gather enough data for successful market research is by acquiring user data regularly.
Dynamic Pricing Strategy
A dynamic pricing strategy sees smart businesses set flexible and fluctuating prices depending on demand, supply, seasons, consumer behavior, and other factors. And this strategy relies largely on data acquisition.
Web scraping is a process that is used to collect important data from several sources at once. The data collected is also through data acquisition and is used for making more customer-oriented decisions that help drive a company in the right direction.
How Does Data Acquisition Work?
As earlier mentioned, data acquisition begins at extraction and ends at data loading. The process by which it works is described below:
- The target destination(s) is/are noted, and requests are made
- The requests are normally routed through proxies to make the task automatic and easier
- Aided by the proxy, the scraping bot reaches the server and commences retrieving the data
- Then the extracted data is parsed back to the user just before it is transformed
- Following transformation, the newly structured data is loaded and stored in a medium provided
- Since the process is automatic and the extraction continuous, the bot returns to the server every time newer data has been added to extract and update the storage
Main Steps of Data Acquisition
There are three main steps to data acquisition:
This is the initial step in data acquisition, where the data is retrieved from the source and parsed into a staging area. Keeping it in the staging area is important to validate the extracted data to ensure it is safe, secure, and relevant.
Data transformation is usually done in the staging area and involves turning the raw extracted data into more usable forms. The data is first cleansed, mapped before it is transformed into an easy to access format
This is the final step in data acquisition and involves moving the transformed data into the available storage medium. This storage medium which could be a data warehouse must be easy to access and have high security to prevent data integrity loss.
Using In-House Proxies vs. Using Web Scraper API
The decision of whether to use an in-house proxy or a web scraper API should be based entirely on the size of your establishment.
For instance, large corporations generally opt for in-house proxies as they have the means, manpower, and technical infrastructure to maintain them. Not only can they afford in-house solutions such as datacenter and residential proxies, but they also have the teams to run and maintain them.
Smaller brands, on the other hand, usually go for less complex options such as a web scraping API. An affordable solution such as a real-time crawler can sufficiently serve data acquisition for smaller enterprises.
If you’re interested in the topic and want to learn more about web scraping API, then find this in-depth article and find out more.
Data acquisition is important for several operations, including web scraping, as we have discussed above. However, the tool to use depends largely on the company’s size and, to some smaller extent, on what the data will be used for.