Interdependence of Artificial Intelligence and Big Data

Every day knowingly and unknowingly, we sift through piles of data. But little do we realize how significant each data set is. Humans are limited by their mental muscles to process large amounts of data. IT professionals and data scientists have realized the significance of parsing data to make data-driven and informed business decisions. This marks the dawn of artificial intelligence and big data to derive meaning and actionable insights. Machine learning and artificially intelligent algorithms accomplish the task of analyzing enormous amounts of data.   

Artificial intelligence solutions are the most sought-after services in the business world for mastering business analytics and big data. Companies are enhancing their AI capabilities to broaden their horizon and gain benefits from this technology. Their goal is to leverage all the data accumulated from computers, mobile phones, smartphones, tablets, laptops, and IoT devices. Data scientists are in demand as more and more organizations try to catch up with the AI trends.   

What is Artificial Intelligence? 

In layman’s terms, AI refers to the capabilities of IT systems to perform human-like functions. It is capable of performing tasks that are otherwise associated with super-intelligent beings. Artificial intelligence is like a robot acting like a human. It asks questions, provides answers, discovers and tests hypotheses, makes decisions, and uses analytics and processing power to derive conclusions. 

What is Big Data? 

Even before the term big data was coined, big data existed, and humans were using it. They just didn’t know they were dealing with big data. Big data is an accumulated amount of information that could present answers and reveal actional and valuable insights. It reflects the information of the industry, market, company, and field it belongs to.  

Big data comprises massive, complex, structured, unstructured, and structured data. The zest of big data lies in three Vs – volume, velocity, and variety.  

• Volume refers to the volume of data accumulated.  
• Velocity is the speed at which data comes and is processed.  
• Variety refers to the types of data mentioned above.  

Big data or big data analytics, also known as BDA, is useful in digital solutions and tools and performs predictive, descriptive, and prescriptive analyses. It discusses the storage and processing of massive data derived from different walks of life. Data holds grave importance for giant corporates and organizations because it offers valuable insights about the company, market, industry, environment, and customers. Not many are aware of the fact that big data empowers AI-driven decision-making. In big data, companies use processes, methods, and technologies that include AI and ML to analyze and detect patterns. These patterns help in the development of business acumen and important decisions. It is one of the main drivers of efficiency, revenue, productivity, and profits.   

What is the role of Big Data in AI? 

We established in the above information that Big Data fuels most AI-related decisions and solutions. But how exactly? IT systems or machines that possess AI capabilities require data to develop intelligence subsequently and constantly. The data fed to a machine only increases its learning capacity and intelligence. The more data it accesses, the better the learning. It reflects in the accurate and efficient solutions and services provided by AI. AI is a continuous learning process where the machine gains intelligence with less human intervention as time progresses due to the machine’s capability of learning from the data. It requires lesser process control, machine monitoring, and manual dialing.   

Artificial intelligence reciprocates the same empowerment for big data. Data will completely go to waste if there is no mechanism and process to extract value from it; it would have no value. Scientists and engineers would not be able to unlock the potential and insights of the huge data sets because there would be no AI models to judge the nature of data. In the realm of healthcare, AI medical billing plays a crucial role in this process. The ability to analyze and interpret large volumes of billing data ensures accurate and efficient billing practices. Without such advancements, the process of transformation would cease to exist.

It would not be wrong to assume that artificial intelligence and big data are interdependent and need to co-exist. Each discipline holds a separate personality and demeanor, and the two cannot be confused with each other. They are distinct, but the presence of one is essential for the existence of the other. The capability of a machine that harnesses AI is directly related to the amount of information that goes into it to generate actionable insights. And big data provides the necessary fire or fuel to make matters smooth and get things going for artificially intelligent systems.  

The two together are very resourceful as they help with: 

• Making informed business-crucial decisions  
• Providing better user recommendations 
• Finding ever-increasing and improving efficiencies in the models 

We have a list of parameters for understanding the co-dependence and functioning of BDA and AI. These include: 

1. Identifying and Establishing Patterns 

It does not get said enough that the role of big data is as important to AI as gas for the car and vice versa. Together they both establish patterns over time as the machine learns. While the primary concern of big data is to collect and identify data sets, AI peeks through this data and analyzes it. It then notices the patterns, trends, and homogenous categories. This is crucial for the understanding of the duo. The AI trends chalk out patterns that are in the system as more information stays in there. It also facilitates and encourages data filtration for more accuracy and timeliness.  

2. Negating Anomalies 

Another important function that defines the interdepended and functionality of the duo is anomaly detection and negation. An anomaly is an unusual piece of information or a peculiarity in otherwise normal data. It deviates from the normal and expected. While parsing and processing data, AI establishes patterns and blurts out unusual, unexpected, and out-of-context information from the data. It further leads to the negation of anomalies which cleanses the data which is otherwise full of redundant and useless information and impurities. While BDA is accountable for the safe storage of data, AI leads by detecting the unusual bits from the normal bits of this stored data. Negating anomalies ties the two functions together.  

3. Generating Algorithms 

The third function that combines the power of two for better results is the power of generating algorithms. Artificial intelligence software feeds itself the stored data (big data), which comes out in the form of advanced AI algorithms. These algorithms are used by companies and businesses in different fields and departments. It is safe to assume that it is a three-tier process. In the first level, the duo establishes patterns. In the second step, it detects anomalies, and in the final part, together, they produce algorithms that segregate the data. AI tools and software empower big data and help it become widespread for global implementation and operations.  

What are the Benefits of AI Applications on Big Data and Vice Versa?  

1. Probability of future result and outcome: AI undertakes a known condition that could have an impact on the X probability of a future outcome. It then determines the probability of the expected outcome.   
2. Better customer service: The duo together can accomplish great things by working together in the department of customer success and relationship management. They help organizations to make better decisions regarding high-quality customer service. For example, the customer and market data will assist AI software to perform analysis which suggests recommendations to the companies for improving customer service strategies. These strategies further align with the corporate strategy.  
3. Fewer labor inputs: The biggest benefit of any technology and software is that it decreases the burden and labor-intensive tasks on humans. The automation and machine capabilities take charge of everything and thus require lesser labor inputs. The duo helps to reduce the burden on humans by minimizing the workforce. Human intervention is minimalistic or insignificant in most Edge AI and BDA functions. Machines have crossed the human realms and operate independently, allowing humans to have more time of their own. IT systems take up work responsibilities and are actually more accurate and useful than humans because they make no mistakes. However, it should be noted that humans are only disburdened and not completely exempted or disbarred.  
4. Verified data analysis: In the end, everything comes down to data analysis because it is the most important aspect of artificial intelligence and big data. Humans are prone to making mistakes. To err is human but to be perfect is unique to machines and IT systems. These tools produce guaranteed verified analysis.  
5. Predictability potential: Together, BDA and AI can predict future patterns, trends, and records. How amazing is it that they can also predict future data? It is a great milestone. There are a few tools as of now that can predict data, but the possibility of better and improved tools cannot be negated in the future. It is one of the benefits that make the duo a hit with data scientists and engineers.  

Xavor and Artificial Intelligence  

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