One of the emerging topics in the information technology industry is the relationship between Big Data and AI, or artificial intelligence. While AI can be used for a variety of purposes, it is extremely useful for sorting, organizing, and managing massive quantities of information and generating predictions based on that information. The study of this potential is a field known as “Big Data”. In this article, we will discuss the concept of Big Data and how it interacts with developments in artificial intelligence.
- Big Data and AI
- Uses of Big Data
- Characteristics of Big Data
- The Future of Big Data
Big Data and AI
What is Big Data?
As mentioned previously, Big Data refers to the field of research that explores ways of gathering, storing, and analyzing massive amounts of information. As this amount of data cannot be handled by conventional data-processing applications, Big Data looks to provide a solution. Through the use of modern artificial intelligence (AI) and machine learning algorithms, Big Data has been revolutionized, with software capable of processing vast quantities of data and analyzing it along predetermined metrics.
Artificial Intelligence: Making Sense of the Data
Before the advent of modern machine learning algorithms, Big Data existed as a largely untapped reservoir of information. The volume and variety of data was simply too large for any single data-processing solution to handle, and the data remained unanalyzed for some time. Eventually, developments in machine learning algorithms, software automation, and artificial intelligence lead to the creation of programs that could navigate the vastness of Big Data and interpret it in useful, accurate ways. Now the reservoirs of information are able to be tapped and a number of uses for Big Data have been found. In the next section we will explore some of these uses and discuss some real-world examples of Big Data in action.
Uses of Big Data
In modern contexts, Big Data usually refers to the use of certain analytical tools that extrapolate useful information from large amounts of data. The two primary tools are predictive analytics and user behavior analytics.
This type of analysis involves the use of current and historical data to generate predictions. For example, a stock-trading algorithm may review the entire history of stock market transactions during a given time period and extrapolate predictions based upon that information. Another example can be found in medical contexts, where the analysis of epidemiological information can be essential to the prediction of future disease outbreaks. Predictive analysis exists as a powerful analytical tool that leverages the full potential of Big Data with the efficiency of machine learning algorithms to generate predictions with a high degree of accuracy.
User Behavior Analytics
This analytical strategy functions by observing user behavior and generating patterns based on those observations. User Behavior Analytics (UBA) is commonly used for helping companies identify consumer-buying habits and leverage marketing efforts more effectively. UBA is also used in security contexts, where user behavior is continuously analyzed and checked against expected patterns of behavior. In this way, threats from within a computer system can be identified and mitigated against, which is a notoriously difficult task to accomplish. Separating legitimate user activity from unauthorized activity can involve the review of hours of system logs, a time-prohibitive activity for most organizations. Through the use of UBA solutions, threats can be identified automatically in real-time and mitigated appropriately.
Characteristics of Big Data
Big Data contains a number of characteristics, as described below:
The 6 Vs of Big Data
The quantity of stored data, measured in bytes. Volumes of data larger than terabytes and petabytes are generally considered Big Data.
The type and nature of the data. This typically involves the relevant file formats and storage engines. For example, storing a terabyte of text is generally not considered Big Data. By contrast, storing a terabyte of text, images, audio, and video files could be considered Big Data.
The speed at which data is generated and processed. Generally speaking, Big Data is processed in real-time, with continuous inputs of data and is said to have a high velocity.
This characteristic deals with the reliability of the data, usually referring to the data quality. Big Data has a large amount of high quality data, as low quality data can adversely impact analytical outcomes.
This characteristic is derived from the worth of the information that can be processed and analyzed. This is generally calculated in terms of profitability, but can also be determined via an overall assessment of the characteristics of a given data set. In a way, the value of data is the sum total of all other characteristics listed here.
This characteristic deals with changing formats and data structures. Big Data typically contains a large degree of variability, with structured and unstructured data sets and a variety of formats.
There are additional characteristics of Big Data worth considering:
This characteristic deals with whether or not the data contains common fields that can later be used for meta-analysis between data sets.
This characteristic deals with whether or not new data fields can be added or changed easily.
This characteristic deals with the ability of the storage system to respond to increasing demands for storage space.
The Future of Big Data
With the rise of artificial intelligence and machine learning algorithms, Big Data is starting to become an increasingly important facet of modern computer infrastructures. As the tools for analyzing vast amounts of data continue to improve, so too will our ability to leverage that data for beneficial ends. Whether it involves managing your stock portfolio, fighting disease outbreaks across the world, or gaining insights into consumer-buying habits, Big Data and AI can provide countless opportunities. In the future, we can expect this trend to continue as new technologies emerge to enhance how we gather, store, and analyze data from a variety of sources.
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