# The AI-Powered Evolution of Big Data
## 4 Materials and Methods
### Traditional 4 Vs of Big Data
#### 4.2.4. Veracity  
Veracity refers to the authenticity and originality of the data as more sources are added. Correct and constant data are vital because inaccurate or inconsistent data lead to skewed analytical results and impaired decision-making. Issues such as mis-entry, duplication, and differing data formats usually emerge between these processes. We must use effective data cleansing, preprocessing, and validation techniques to solve these problems. There are already methods available to automate and bolster processes with artificial intelligence (AI) or machine learning (ML), enabling greater precision in gathering data. That way, the process remains short and sweet. AI-based outlier detection can be improved beyond the classical Z-score estimation methods. AI works well in more complex machine learning models in terms of detecting anomalies and generalizing them in large datasets.  
The traditional Z-score calculation is expressed as follows:  
\[Z=(x-\mu)/\sigma \tag{5}\]  
where \(x\) represents the data point, \(\mu\) denotes the dataset's mean, and \(\sigma\) stands for the standard deviation. While this method is effective, AI models can enhance this process by dynamically adjusting to different data distributions and improving the detection of contextually relevant outliers.  
Another AI-enhanced approach to maintaining data veracity is normalization, which is often achieved via min-max scaling. Traditional min-max scaling is defined as follows:  
\[X\_norm=(X-Xmin)/(Xmax-Xmin) \tag{6}\]  
where \(X\) is the original data point, \(X\)min is the minimum value in the dataset, and \(X\)max is the maximum value. AI can optimize this process by considering data distributions across different scales and by applying adaptive normalization techniques that are better suited to heterogeneous datasets.  
Ultimately, error correction methods, such as mean imputation, are essential for preserving data accuracy. The mean imputation can be expressed as follows:  
\[Value=(\sum\mathbb{E}\text{\emph{Non-Missing Values}}\mathbb{E})\text{\emph{)/Number of Non-Missing Values}} \tag{7}\]  
Figure 9: Various data modalities and AI.  
However, AI-imputation methods--e.g., K-nearest neighbors (KNN) or deep learning-based techniques--would provide superior estimations by taking intricate data patterns and relationships into account and, consequently, producing more robust datasets. Additionally, the use of AI to prove veracity is not confined to those simple methods. It can also read and continuously monitor data streams on the fly faster and correct any errors or anomalies. However, machine learning models have the capability to correct themselves and learn from past corrections to the data environment.  
Figure 10 presents AI integration in the data veracity component of the model.  
As can be seen from Figure 10, AI enhancement methodologies ensure that Big Data analysis is reliable, reducing the risk of error and increasing the credibility of the results. As data veracity is crucial for informed decision-making, the use of AI to maintain data integrity represents a significant advancement in Big Data analytics.