Companies already understood that information is valuable. And, therefore, incorporating Big Data analytics strategies is key.
Especially startups, which since their birth understand the importance of having a cross-company data science department.
However, statistics indicate that only 10% of the data collected from the Web is used by companies to improve their decision-making.
What happens, then, with the remaining 90%?
Web Scraping: I squeezed the juice out of public data
What considerations should we make to convert the extracted data into information that increases the value of our products and customer confidence?
Blank or deleted data, spelling errors, local formats, and non-standardized information end up being stored in company databases, disproportionately wasting the amount and value of the information stored there.
In this flood of data, obtaining the correct information, useful for building learning models, has become a very costly task in terms of time and resources.
What types of datasets can be useful for my startup?
The heterogeneity of available data is such that it is very important to know, first of all, what our objectives are, that is, what we want to achieve with this information.
Thus, we will be able to outline a plan that maximizes results and limits the work of data collection, maintenance, and monitoring.
This task is very laborious since it involves the following challenges:
- Have up-to-date data
- Deal with infinite formats
- Be able to scale if the volume requires it
- Be ready for change
- Require extensive monitoring
We need to determine what we can download within the wide spectrum of information available on the web and how to use said data to achieve our goals. For this, it is very important to know in advance what our objectives are and their scope in the business.
Regarding what we can download, we can find very varied information:
- Product catalogs
- User profiles
- Comments / Opinions
- News
- Consuming patterns
- Personal information
Regarding how to use the data, it is also fundamental: the same type of data can arrive in multiple ways.
Therefore, it is important to know how the information we want to extract will help us to add value to our products in a cost-effective and fast way.
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