The Difference Between Structured and Unstructured Data
Real estate buyers and sellers can now make real-time decisions by using updated data. This is possible because data is now available at a faster rate. In the past, buyers make their decisions after 30 to 90 days. Now, people can decide in just a day or two. Big Data has made this possible.
There are two kinds of data. Structured data is one and unstructured data is the other. There is a big difference between the two. This article will explain this difference.
What Is Structured Data?
The typical structured data is the one that you see listed in tables. Field names define the data listed on the table. People collect big data on computers. Then they list the contents in tables to organize the information. They do this so that they can extract big data for different applications.
They organize the data fields across several collection points. After this, they use the information in various applications and processes. It is important to know the data fields that need to be included. This will provide the basic structure of the table that will be normalized or organized.
Examples of Structured Data
Here are some examples of structured data. You can better understand this type of data with these examples.
- Shopping records of shopping malls and supermarkets
- RFID (Radio Frequency Identification) logs
- GPS signals from mobile phones that show who called whom
- Search Indexes
- Rides that rental drivers have provided passengers
- Criminal records
- In the case of the real estate industry, structured data can include
- Property tax loans
- The time when the last child of a family leaves home
- Marital status
- First child at home
- Any information that can cause a change in the ownership of a home
What Is Unstructured Data?
Unlike structured data, unstructured data is not found in a table or in an Excel document. This data may include posts on Twitter, Facebook, and other social media platforms. Unstructured data also includes videos, photos, text documents, and information in appraisal reports. This type of data is difficult to retrieve.
Information collected from the internet is a good example of unstructured data. Organizations use only about 5 percent of the data available to them. This is according to Forrester Research. The data has an internal structure which is very hard to fit in a spreadsheet or a database.
Unstructured data is not in order, but it has its own importance. You can find unstructured data in data sources that are a bit complicated.
Examples of unstructured data are:
- Social media information
- Multimedia content
- Email statistics
- Sales automation
- Web logs
- Interactions in customer service
Those who are not technically inclined will not understand unstructured data. They will not be able to use and prepare this unstructured data for analytical purposes. There is a huge volume of this type of data. Data mining strategies usually leave out important unstructured data. This makes the analysis of unstructured data time consuming and expensive.
These two types of data will continue to expand in the future. Our capabilities in analyzing unstructured data will continue to improve. This will make big data more relevant and searchable.
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