Machine learning algorithms often get the majority of attention when people discuss machine learning; however, success depends on good data. There are mainly two types of data, structured data and unstructured data. In this article, I’ll walk you through how to identify your data.
Understanding your data is critical to your success. If you build a model based on bad data, your predictions will be inaccurate. You should also think about what data to include in your machine learning application.
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Identify Relevant Data: Structured Data and Unstructured Data
Business decisions must be made based on constantly changing data from various sources. Your data sources can include both traditional systems of record data (such as customer, product, transactional, and financial data) and external data (for example, social media, news, weather data, image data or geospatial data). Also, many data structures are essential for analyzing information, including structured data and unstructured data.
Structured Data Sources
Structured data is generally stored in traditional relational databases and refers to data that has defined a certain length and a format. Most organizations have a large amount of structured data in their on-premises data centres. Here are some examples of structured data:
- Sensor data: Examples include radio frequency identification (RFID) tags, smart meters, medical devices, and global positioning system (GPS) data.
- Blog data: when servers, applications, networks, etc. work, they capture all kinds of data about their activity.
- Point of Sale Data: When the cashier swipes the barcode of any product you purchase, all data associated with the product is generated.
- Financial data: Many financial systems are now programmatic; they operate according to predefined rules that automate the processes.
- Weather data: Sensors to collect weather data are deployed in towns, cities and regions to collect data on things like temperature, wind, barometric pressure and precipitation. This data can help meteorologists create hyperlocal forecasts.
- Click Flow Data: Data is generated every time you click a link on a website. This data can be analyzed to determine customer behaviour and purchasing patterns.
Unstructured Data Sources
Although unstructured data has an implicit structure, it does not follow a specified format. Unstructured data is still vastly underutilized by businesses and offers a great opportunity for monetization. Cloud, mobile and social media have contributed to a huge increase in unstructured data. Here are examples of unstructured data:
- Internal text of the company: Think about all the text in documents, journals, survey results and emails. Corporate information today represents a significant percentage of textual information in the world.
- Social media data: As the name suggests this data is generated from social media platforms, such as Facebook, Twitter, YouTube, LinkedIn, etc.
- Mobile data: This includes text messages, notes, calendar entries, images, videos, and data entered into third-party mobile apps.
- Satellite imagery: This includes weather data or data that the government captures in its satellite surveillance imagery.
- Photographs and video: this includes security, surveillance and traffic data.
- Radar or Sonar Data: This includes vehicle, weather and oceanographic data.
Also, Read – Machine Learning Interview Questions.
I hope now you understood what are the types of data Machine Learning Experts use, and what’s the difference between structured data and unstructured data. I hope you liked this article on structured and unstructured data in Machine Learning. Feel free to ask your valuable questions in the comments section below. You can also follow me on Medium to learn every topic of Machine Learning.
