Big data and artificial intelligence are hot topics on the minds of business leaders. Together, they significantly impact a company’s ability to collect and analyze data. There are many examples of artificial intelligence and big data going hand in hand in today’s environment. However, they have evolved as different technologies and they have differences between them.
What Is Big Data?
Since the emergence of the digital age, big data has been around and refers to large amounts of data characterized by three elements known as the ‘3Vs’: volume, velocity, and variety. Big data sets are distinguished from other data sets by their size (volume), their rate of growth/change (velocity), and the variety of structured, unstructured, and semi-structured data in the data set.
The advantage of large data is that they may contain hidden patterns and trends that are only visible in such large data sets. However, because of the size and complexity of big data, its value lies not in the data itself but in its analysis, which is a difficult task. Big data is so large and complex that traditional data processing and analysis methods cannot extract business value from such large data sets.
So far, companies have spent most of their time in this area. In the past, companies had to spend a lot of time, money, and resources analyzing data to extract valuable insights.
Fortunately, the advantages of big data strategies have enabled researchers to aggregate large data sets for practical analysis. That’s why big data analytics can transform large amounts of data into easy-to-understand formats that businesses can fully leverage and integrate technologies such as artificial intelligence and machine learning to extract other valuable insights.
What is Artificial Intelligence
The study of “intelligent” problem-solving behavior and the creation of “intelligent” computer systems. Artificial intelligence (AI) deals with methods that enable a computer to solve those tasks that, when solved by humans, require intelligence.
The term artificial intelligence is applied to the machines’ ability to autonomously execute a set of tasks on the basis of algorithms and to adaptively react to unknown situations. They therefore behave in a similar way to humans: not only performing tasks repetitively, they learn from their successes and failures and modify their actions accordingly. In the future, AI should be able to think and communicate like humans.
The big difference between big data and artificial intelligence is that big data is raw input data that needs to be cleaned, structured and integrated before it becomes useful, while artificial intelligence is the result, the intelligence derived from processed data. This makes them inherently different.
Difference Between Big Data And Artificial Intelligence
The terms Big Data and artificial intelligence (AI) are often used in the same breath in political and social discourse. To avoid the appearance that these two terms are synonyms, this section addresses the term AI to help distinguish it from the term Big Data.
The term big data initially refers to a description of data based on various data properties. However, it is often used synonymously for its processing, application and analysis.
The concept of artificial intelligence, on the other hand, does not focus on data or a set of data, but on algorithms that use this data as input factors. To put it briefly: Big Data is a prerequisite for artificial intelligence; but artificial intelligence is not a prerequisite for Big Data. Big Data can therefore exist without AI. For good results in the sense of sufficient data volumes for learning, AI cannot do without Big Data.
The most important foundations for AI as a sub-field of computer science are sub-symbolic pattern recognition, machine learning, computational knowledge representation, and knowledge processing, which includes methods of heuristic search, inference, and action planning.
On the one hand, this shows the characteristic that AI is a technology or application. On the other hand, this technology uses data sets in its processing. This is the essential difference between the two terms artificial intelligence and big data, which has already been briefly touched upon.
In this analysis, the difference between the two terms AI and Big Data is primarily that AI is application or algorithm, while Big Data describes data and its processing. AI is understood to be a rule- or data-based application that makes decisions, while Big Data primarily involves the generation of information.
In another definition, it refers to “very large and heterogeneous data sets” further indicating that Big Data is given a significant role as a necessary input for AI.
Artificial intelligence is not a new phenomenon, but was used decades ago. In the early days, there were many applications of AI to human games such as chess. This application area is suitable because of the simple rule system and the thus clearly describable options for action.
These are searched by the algorithm in their combinations until a desired result is achieved. This was followed in time by the AI applications of machine learning. Here, the algorithm learns independently from the results it has generated. The algorithm learns feedback, which it uses to make optimizations. The latest development is Deep Learning, which works with the help of neural networks. The structural design of the neural networks is based on the nerve cell connections of the human brain.
The algorithm’s learning process uses multiple layers that are interconnected. Learning is done from the data and results are calculated for further explanation.
AI And Big Data Solutions
Big data and artificial intelligence will continue to evolve and play an essential role in business solutions. Discover how Existbi’s big data solutions can make your job easier. Turn raw data into valuable insights.
