Understanding AI Data Harvesting / AI Data Collection
With AI data harvesting and AI data collection, there is one key point to understand. The information and analysis conducted is only as good as the data provided. A phrase common amongst data mining and collection is GIGO. This refers to Garbage In, Garbage Out and basically implies that if the data provided is incorrect, the information provided from its analysis will be similarly flawed.
When looking at different data harvesting or data collection methods, there are three different types of techniques used:
1. Classification and Prediction
With this type of technique models are used to predict where a set of data would fall if its overall class is unknown. This technique relies on different decision tree formulas to ensure that data is correctly categorized.
2. Association Analysis
With association analysis data is reviewed and categorized based on other similar data sets. This type of analysis is often used with sales transactions.
3. Regression Analysis
Regression analysis looks at the correlation between multiple different data sources. A good example of regression analysis is a comparison of property prices and a correlation to income level.
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AI Data Collection and AI Data Harvesting – Use of data
Regardless of the method used to collect data, it is important to have an understanding of how the data will be used. With this understanding established, the next step is to determine if the data already exists within the environment and what the quality of that data is. If it does exist, there is a potential to simply reformat the data versus spending the time and resources to collect it once more.