Data is an incredibly valuable resource. The more data scientists and analysts can gather and analyze, the more they can unlock insights that drive business growth. Unfortunately, as data becomes increasingly available in various formats, it’s becoming increasingly difficult to manage. That’s where artificial intelligence (AI) comes in: AI is a part of business intelligence trends in 2022 that can help you sort through this wealth of information by automatically organizing it into usable human analytics datasets.
Here are ways that AI is transforming data analytics.
Predictive analytics
Predictive analytics is a subset of data analytics that uses data to make predictions. For example, it can predict which customers are likely to churn from your company to help you retain them. This can help companies take actionable steps (such as sending an offer) before they leave, rather than doing so once they have left.
Predictive analytics involves collecting and organizing relevant information, applying algorithms and statistics to it, and then interpreting and presenting the results in a way that helps people make sense of them.
Automated data analysis
For a long time, analytics tools have been somewhat limited. They are typically either manual or automated, but rarely both. Automated data analysis involves taking the results of predictive analytics and applying them to the system to make further predictions.
Automated data analysis has many benefits over other methods of analyzing data:
- It’s faster than manual analysis because it doesn’t require human input.
- It can be performed on larger datasets than what is feasible for humans alone to handle.
- Its results are more accurate than manual analyses since there’s no room for human error in the calculations (and we all know how easy it is for humans to make mistakes).
Data extraction
Data extraction is the process of pulling specific data from an entire dataset. For example, this can pull out information like time stamps or images and videos.
There are two major types of data extraction: manual and automated. Manual data extraction involves a person manually looking at each piece of information in a dataset. At the same time, automated data extraction (also known as machine learning) uses algorithms to do this work for us, often behind the scenes when we don’t realize it’s happening until after it has been done.
Automated methods are becoming increasingly popular because they save companies both time and money compared to manual methods, making them an attractive option for businesses looking for ways to cut costs while increasing efficiency at scale.
Natural language processing
Natural language processing (NLP) is the ability to analyze and understand human language. It can be used for a variety of purposes, including:
- Chatbots and voice assistants: Apple’s Siri, Amazon’s Alexa, Google Assistant
- Email classification: More than 80% of emails are spam. NLP can help determine which emails are legitimate or not by determining whether they appear to come from known contacts and whether the content matches what you would expect from them (e.g., customer service inquiries).
- Document classification: This is another way that NLP helps analyze data in your organization. You might have thousands of documents on different topics in different formats; NLP could help organize those documents into categories, making it easier to search through and quickly find what you need.
Manual analysis
Manual analysis is a process of reviewing large amounts of data and manually deriving conclusions from it. This type of analysis is not only time-consuming but prone to human error because there’s no way to ensure accuracy when you’re looking at a huge amount of data. But unfortunately, it’s also prone to human error because there’s no way to ensure accuracy.
Conclusion
Artificial Intelligence is an exciting new technology that can dramatically improve the way data science is done. It’s still early days for this technology, but it’s clear that we are moving towards a future where AI can help us gain insights from large datasets faster than ever.