AI is capable of processing both organized and unstructured data from a variety of sources.
Examples comprise:
- Text information from websites, blogs, tweets, papers, news articles, and discussion boards. Scripts, tags, and stylesheets are typically used to constrain the text on web pages. Rarely does text from these sources adhere to any rules or structures.
- Audio information from podcasts, videos, and recordings. After using speech-to-text converters to convert the audio to text, these data were collected. The quality of the output varies depending on the caliber of the converters and the input.
- The AI system needs to process visual data from pictures, videos, diagrams, screenshots, and infographics in order to comprehend it.
- Sensor data from a variety of IoT devices, such as temperature variations in a deep freezer in the kitchen of a large hotel depending on the sorts of raw foods stored.
- Geospatial information gathered from a variety of devices and systems, including GPS, smartphones, and compasses.
Issues with Unstructured Data
- For large-scale jobs at least, AI systems require a consistent data format, but implementing uniformity is difficult because data from various sources are obstinately inconsistent and challenging to organize.
- Pre-processing the data, which involves deleting errors, unnecessary spaces, and outliers, takes time but is necessary to get the data into shape.
- Additionally, data can be provided in a variety of formats, including JSON files, spreadsheets, APIs, and new data formats that develop over time, further complicating the issue.
- Data confidentiality can further complicate matters, thus service providers must exercise extreme caution to stop data leaks.
