Unstructured Data: Every day, an enormous volume of data circulates on the internet and in private networks, which is therefore difficult to measure precisely.
By themselves, numbers, letters, and other graphic elements do not necessarily represent relevant information. For this, in addition to collecting, it is necessary to organize, i.e., structure it.
Isolated data, that is, unstructured, have little or no value, especially for market strategies. When analyzed together, they become information and thus can represent an essential instrument for a company’s market intelligence. Understand what they are, how to find them, and how to collect unstructured data.
Difference Between Structured And Unstructured Data
The Google search tool, and all the technology involved to present the results to users, is one of the best examples to understand the definition of structured and unstructured data. In a simplified explanation, the search engine works with the action of robots, web crawlers, and spiders, acting in the crawl for the collection of data in code that can be text, images, audio, videos, etc.
Using a technical programming language vocabulary (schema.org, a series of XML tags), the algorithm indexes and categorizes the collected data to present it in the structured format of search pages with links, snippets, and other types of results that enhance the best user experience and functionality. It is important to note that the ability to process a large volume of data into relevant information makes Google one of the most influential companies in the world.
However, there must be elements present in the code of the pages for them to be a page to be crawled and indexed. In this way, we can better understand the classification of the existing data types:
- Structured data has a defined and planned structure, considering how the information will be used from the start, like a database or an Excel spreadsheet. Therefore, it has an organized format and is easier to store, export, and analyze.
- Unstructured data represents an estimated 80% of existing data. It is a raw material that will be collected and organized to make sense of the objective. They do not have a defined form and therefore are presented without standardization. For example, when performing a market analysis, pdf documents, graphs, tables, and the like are collected from various sources and then studied in isolation. In this case, the data has no strategic value, but on a large scale and structure, it can change the strategic direction of a large company.
- Another category is the semi-structured data that occupies the middle ground. They have no defined form but are not disorganized. For example, an XML file.
Importance And Strategic Application For Companies
We can say that unstructured data is the most complex to collect, and even so, it has much more value, precisely because of the large volume available.
It is essential to consider that this data category can only have its full potential exploited when specific tools are used, which can search, collect, interpret and classify a large volume of information from different sources.
By going through this process, as a strategy for analysis, it is possible:
- Monitor the presence and price variation of competitors’ products on various marketplaces;
- Cross-referencing property value information and demographic data from different regions to define where to open shops;
- Understand the feasibility of importing or buying from domestic suppliers automatically;
- To understand the costs of operating an area, understanding all the variables that influence
- Check the documentation of the entire fleet and drivers in seconds before closing a freight;
Among many other practical applications that originate in the objectives of each business or area according to the demands for analysis.
The possibilities are endless, as is the volume of data that can contribute to strategic decision-making, generating more assertive market studies and new opportunities for developing products and services.
So how do you ensure that unstructured data can become valuable information for your company?
What Unstructured Data Is Used For
Automation for collecting unstructured data is a relevant point in the digital transformation process in a company. Manually, this type of data mining, that is, the procedure of research, collection, sanitization, and delivery of information on platforms and dashboards, may require an unfeasible demand for the back office services, regardless of the area.
A simple example: imagine that the financial department of a company with branches spread throughout nation needs to receive or download all the bills for fixed expenses such as water, electricity, telephone, internet, etc. From this, insert them into financial management software, considering the procedures to ensure everything is in order, the payments are made without delay, and the archiving of all this information.
In this case, the automation of data collection can be done through customized robots that access the company logins on the websites of the electricity, water and sewage, telephone, and similar companies and capture the accounts payable to deliver them directly to the management software for control.
This is just one of the countless possibilities that can serve the most varied interests of optimizing unstructured data collection procedures.
How To Collect Unstructured Data
The first step is strategic planning about your company’s demand, whether it is consulting prices and data about competitors’ products in e-commerce, downloading negative clearance certificates from official government sources, or whatever type of unstructured data.
Generally, organizations, areas, or procedures already operate or are implementing a tool, platform, or software for the organization of reports, dashboards, dossiers, or any other type of visualization, comparison, and data analysis, already in its structured form.
Also Read: What Types Of Data Are Used In Big Data?
