Artificial intelligence is no longer a subject of science fiction. It's already revolutionizing industries far and wide. Every year, AI improves and paves the way for more innovation.
While most people think of what AI can do for healthcare, robotics and consumer technology, new developments are also changing the face of data analysis. Here's how.
Streamlined Data Preparation
We live in the age of big data, and organizations of all sizes have a repository of information they need to process. In the past, the only way to make use of any of that collected data was to prepare it manually. Data scientists had to generate reports and handle exploration on their own.
Fortunately, that's not the case now. AI can produce models and help visualize key information in only a few clicks. Companies investing in machine learning can also benefit from continued AI development. For example, AI labeling for computer vision significantly reduces the amount of manual work required to train models.
Better Data Accuracy
Another common problem in data analysis is potential inaccuracies. Inaccurate data can lead to false interpretations and countless flaws. It defeats the entire purpose of having the data.
Errors are more common than most realize. Humans aren't perfect, so issues are bound to happen.
AI can greatly reduce the number of flaws in massive data sets. The technology can learn common human-caused errors and detect them when they occur. The technology can detect and resolve deficiencies automatically, providing peace of mind that you're working with correct data.
Less Human Intervention
The biggest advantage of AI in data analysis is the reduced reliance on manual work. Not only is manual work expensive, but it's also prone to errors. AI takes most of the human aspect out of the equation entirely.
AI labeling for computer vision only needs manual intervention when clarifying errors, reducing the resources invested in getting systems deployed. AI can also surface insights from enormous data sets in minutes. Instead of manually sifting through numerical data, you can rely on technology to get the information you want accurately and efficiently.
Continued developments can help AI learn data nuances, allowing it to spot patterns, alert you to anomalies and more.
Read a similar article about tools for machine learning teams here at this page.
