It isn't easy to keep up with the volume of data generated by organizations in today's world of lightning-fast computer access. While humans can produce outcomes considerably better and more accurate than artificial intelligence, this is not always the case.
Most data analysis work is automated using artificial intelligence (AI) technologies. Even large datasets seem to be little work, and the technique is scalable to any need. The most significant part is that they are pretty simple to use.
Reasons to Use AI in Data Analytics
Any data source may be analyzed using AI-powered software, providing actionable insights. Data examined with AI is highly insightful and may impact product development, boost team performance, and assist companies in understanding what works best for their customers and their customers' businesses.
Using sophisticated algorithms, artificial intelligence (AI) is a data science subject that allows computers to learn independently. In contrast, data analysis is transforming unstructured data into useful information. Using AI-guided systems, your data may be automatically cleaned, analyzed, explained, and visualized.
A human role is required at all times with traditional software. An engineer must physically edit the code to add or alter an existing process or function.
However, there is no need for further human input with AI software that uses machine learning. Machine learning algorithms may be trained by giving them labeled text samples, known as training data. Patterns and techniques for analyzing data are taught to them using human-tagged material.
Combining Artificial Intelligence and Data Analytics
It was impossible to imagine how far artificial intelligence and machine learning had progressed in data analysis a few years ago.
In my opinion, we're witnessing the beginning of the golden era of AI.
"This is only the beginning; we've just scratched the surface of what we're capable of with the breakthroughs we've seen recently,"
said the Amazon CEO Jeff Bezos
As organizations realize the advantages of AI, they adopt it more often. Now, we are going to see some examples of what you can do with that:
AI Text Analysis
Machines can organize and "understand" human speech with the help of the AI machine learning area known as natural language processing (NLP). It is possible to extract new insights from documents, social media, internal communications, etc., via NLP or Natural Language Processing (NLP).
Text analysis can go beyond simple statistics and numerical values since it examines open-ended and unstructured text data. Text analysis tells you what's going on and reveals why.
Furthermore, text analysis can identify attitudes and subjects in your data as well as extract keywords and other information:
AI Sentiment Analysis
Text may be automatically categorized by the polarity of opinion using NLP or sentiment analysis (positive, negative, and neutral). Text data from any source may be processed to understand the feelings and emotions of the writer.
With open-ended questions and sentiment analysis on customer surveys, you can dig deeper into replies than you could with a yes/no or multiple choice survey. You can automatically analyze customer support tickets and emails to determine the degree of urgency and dissatisfaction and then prioritize the most critical concerns.
AI data analysis solutions that use machine learning enable you to train your own sentiment analyzer to your company's language and criteria in only a few clicks, resulting in unmatched accuracy.
For example, you might use sentiment analysis to track the evolution of consumer sentiment when hundreds of tweets about your brand are sent out shortly after a new product is released.