When Big Data and Artificial Intelligence Complement Each Other
The problem with Big Data is that there is too much of it. Analytics suggest that data grows at an almost exponential rate. This number is no surprise, particularly if you take machine-generated data growth into account.
VisualCue analysis indicates that stored data will rise to 44ZB by end of 2020 and there are 2.5 quintillion bytes of data generated by people worldwide on a regular basis, according to IBM.
It would take too many personnels if humans had to analyze the data generated by companies, individuals, and machines, which is just not economically worth it.
This is where Big Data and Artificial Intelligence work together. Managing this large amount of data using AI software algorithms is the only way to work effectively with Big Data.
Big Data and AI work in reciprocality. Via machine learning, these two modern technologies, (Big Data and AI) have formed an impressive pair to provide the computational process at a whole new dimension by utilizing the process of continuous reiteration of data with minimum human intervention.
When Big Data and Artificial Intelligence complement each other, here are some areas where the two work best together.
In the past, people are analyzing data to understand what has happened. But using Big data and AI, it is now possible to make a future prediction and understand what is going to happen.
Now, by using predictive analytics, AI is driving Big Data decisions to make a predictive analysis in more detailed ways. Big data decisions have historically been focused on historical and existing data points, usually resulting in linear ROI.
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However, with the implementation of AI, this trend has grown to epic and exponential proportions. Leveraging AI, the analytics of Big Data has the ability to provide company-wide forward looking business perspectives that can lead to better decision making and significant improvement in business results.
With Big Data providing a large volume of data, artificial intelligence algorithms are able to reproduce certain behavior in machines or in computer programs that were not feasible in the past.
Data analytics is becoming less labor-intensive with the use of AI and Machine Learning. These technologies help organizations to analyze their data more efficiently and easily than what their employees usually do.
But it is important to understand that AIs do not make conclusions like humans, but they learn by the process of trial and error, and as a consequence required Big Data to increase the accuracy and the precision of their data analysis.
The value of data is determined by its quality and consistency. If your data is poor in quality, then it means it has very little value for Big Data analytics and AI, because the insights that come out of it are not trustworthy.
Luckily, Big Data can be cleansed using machine learning and AI. Based on Ness Digital Engineering, the secret of successful machine learning projects is that the amount of time spent to clean and prepare the data is usually 80% of the overall project activities.
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AI algorithm can detect outlier values and missing values, locating record duplication and normalize data to the standard terminology.
Now it has become more clear that the combination between Big Data and AI has massive potential to transform how organizations work in this digital economy era.
When Big Data Indonesia and AI work together, it will allow companies to better consider the needs of their customers and to provide the best available response.
In addition to that, the use of both technologies can help businesses to understand the customer expectation in the shortest time needed, and in real-time if possible.
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