The issue with Big Data is that there is an excessive amount of information. According to statistics, data grows at a nearly exponential rate. This figure is unsurprising, especially when machine-generated data is taken into account.
According to IBM, stored data will reach 44ZB by the end of 2020, and people globally generate 2.5 quintillion bytes of data on a regular basis. If humans had to examine the data provided by individuals, firms, and machines, it would take far too many people and is just not cost-effective.
This is where Artificial Intelligence (AI) and Big Data come together. The only way to operate effectively with Big Data is to manage the massive volume of data utilizing AI software algorithms.
Big Data and AI are Mutually Beneficial
These two modern technologies (Big Data and AI) have formed an astonishing duo via machine learning. They now bring a whole new dimension to the computational process by employing the process of continual reiteration of data with minimal human participation. Here are several places where Big Data and Artificial Intelligence complement each other the best:
Finding forward-looking insights is where big data and AI shine.
People used to analyze data to figure out what had happened in the past. However, thanks to Big Data and AI, data analysis is now able to anticipate the future and understand what will happen. AI is now guiding Big Data decisions to make a more detailed predicted analysis by applying predictive analytics.
Traditionally, big data decisions have been based on historical and existing data points, resulting in a linear return on investment. However, with the introduction of Artificial Intelligence, this trend has taken on epic and exponential proportions. Big Data analytics can provide company-wide, forward-looking business insights using AI, which can lead to better decision making and considerable improvements in business results.
Big data and AI can assist businesses in increasing productivity and reducing labor-intensive operations.
Artificial Intelligence algorithms can now mimic certain behaviors in machines or computer programs that were previously impossible to do due to the vast amount of data provided by Big Data. With the application of AI and machine learning, data analytics is becoming less labor-intensive. These technologies allow businesses to evaluate their data more quickly and easily than they could otherwise.
It’s vital to remember that AI doesn’t draw conclusions as people do. Instead, AI learns via trial and error, necessitating the use of Big Data to improve the accuracy and precision of their data processing.
In addition, for safety, data tokenization is used. This is the process of replacing real values with opaque values for security reasons.
By cleaning data, Big Data and AI can work together to improve accuracy.
The quality and consistency of data defines its worth. If your data is of low quality, it will have limited use for Big Data analytics and AI because the insights it produces will be unreliable. Fortunately, machine learning and AI can be used to clean up Big Data. According to Ness Digital Engineering, the secret to successful machine learning projects is that the time spent cleaning and preparing the data accounts for about 80% of the overall project activity.
Furthermore, outlier values and missing values can be detected using AI algorithms as well as record duplication and data normalization. Now it’s evident that the combination of Big Data and AI has enormous potential to alter how businesses operate in the digital economy.
When Big Data and AI work together, companies will be able to properly assess the demands of their customers and deliver the best available response. Further, the use of these technologies can assist firms in understanding client expectations in the shortest period feasible, if possible in real-time.