AI Data Harvesting / AI Data Collection – Short Explanation

When discussing AI Data Harvesting or AI Data Collection, there is yet another term that comes to mind and that is Data Mining. Regardless of how it is named or called though, the function is the same. Simply put AI Data Harvesting/Collection or Mining is about obtaining/extracting useful information from existing data sources.

The amount of data being generated globally increases daily. IDC calculated the amount of global data in 2018 at 33 zettabytes and expected this number to grow to 175 zettabytes by 2025. Understanding, collating and categorizing this amount of data is an insurmountable task unless the right tools are used. This is where AI and ML come into the picture.

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.