The Role of AI in Big Data Predictive Analysis

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Predictive big data analysis is a combination of two different, yet highly relevant techniques for businesses. The technique is the practical outcome of business intelligence (BI) and big data.

The predictive analysis made using artificial intelligence (AI) takes big data analytics to a whole new level, bringing deep insights about a business that can help in better decision making.

An Overview of the Role of AI in Big Data Predictive Analysis

Using AI in big data analytics helps in recognizing trends and patterns in data. The information is used to project the probability of different outcomes that can be used for more accurate decision making.

AI has advanced a lot in the past decades. Today, AI is used to bring new innovation in almost every sector.

The machine learning technique empowers technologies to make analysis that were previously made by data scientists. However, they complement rather than replace the roles of data scientists.

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Using AI, data scientists can present detailed insights into business performance to business owners. The technique helps in drawing a more meaningful conclusion from existing data. Data from various sources are collected and analyzed to identify behavior and patterns. The machine algorithm can sift through information and highlight areas that might otherwise have been overlooked by data scientists.

With AI, the time and effort involved in analyzing data are reduced. They empower data scientists to make an accurate judgment about different factors concerning the business. AI gives rise to many new insights that help in making an informed decision.

Examples of the Use of AI for Big Data Predictive Analysis

A lot of case studies can be presented that reflect the effectiveness of predictive data analytics using AI. Companies such as Amazon, Facebook, and Google are using AI to better understand data and make predictions. The proprietary software of these companies already uses machine learning to analyze big data for obtaining deep insights for the customers.

Let’s take a look at a few of the real-world examples of big data analytics using AI to better understand the concept.

1. Amazon Search

A lot of people don’t know that Amazon has incorporated predictive big data analytics using AI in its search engine. When you enter keywords like Android smartphone, pasta, sewing machine, the search engine lists the most relevant products.

The search engine algorithm combines different relevant features of a large number of products in its data. The structured data of the catalog reveals features that are related to each other. This information, in addition to past search patterns, is analyzed by the algorithm to predict what the customers want.

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The technique is also used in generating personalized recommendations for customers. These recommendations are based on machine learning, and they allow the company to rake in more money as research has shown that they increase sales by up to 30 percent.

2. Square Fraud Protection

The credit processing company Square also makes use of big data analysis and machine learning to detect fraudulent transactions. The system’s algorithm will flag transactions based on an analysis of past fraudulent transactions. Using AI not only reduces the risk of fraud, but it also minimizes the chances of valid transactions being falsely highlighted as fraudulent.

3. Facebook News Feed

Facebook uses big data predictive analytics using AI to personalize the feedback. This ensures that you see the most relevant posts that interest you. The same technique is also used in creating focused ads.

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The company announced a new AI project known as DeepText in 2016 that could understand textual contexts of a large number of posts with near-human accuracy. The posts are analyzed within a matter of seconds, resulting in deep insights. This technology is used by the company in its Messenger app to predict user intent.

The algorithm can differentiate between the phrases “I need a ride” and “I like cars.” This allows users to use Uber within the app.

Final words

Modern predictive big data analytics using AI can massively increase the accuracy of decisions. The technique can enhance decision making by better-identifying processes and reducing manual work.

The possibilities of using AI for predictive big data analysis are endless. AI can be used for pattern detection, anomaly detection, probability identification, and node relationship with a high degree of accuracy.

AI provides real-time decision support information to C-level executives. The technique accelerates the process of big data predictive analysis resulting in significantly improved business outcomes. The technique will prove to be revolutionary in more than one way. Adopting the technique will allow companies to see results faster and anticipate changes that can lead to breakthrough business solutions.

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