Understanding AI Data Analysis

Data analysts in various industries and sectors use artificial intelligence (AI) tools to streamline specific established data analysis tasks. 

AI data analysis tools facilitate the process of categorizing and analyzing larger amounts of data than human data analysts have traditionally been able to handle manually. AI can also collect and interpret data more efficiently than humans, allowing for improved data-driven decision-making based on highly sophisticated, less error-prone, broadly sourced data sets. 

What is AI data analysis?

Data analysts aggregate and analyze data to improve a business's data-driven decision-making process. However, challenges regarding skills and time can prevent organizations from optimizing their use of data, a stumbling block that AI can help professionals overcome. Additionally, AI helps speed this process up: AI can handle enormous stores of data and parse them in seconds—much faster than any human data analyst can alone. 

AI data analysis automates and streamlines general data analysis tasks such as: 

• Data collection: AI is capable of taking in and sorting data from multiple input sources at once. 

• Data preparation: AI organizes and cleans data—that is, it fixes factual, technical, grammatical, and formatting errors. 

• Data interpretation: AI discovers patterns in data and analyzes what they mean. This allows you to make sense of large stores of data and make decisions based on accurate data. 

How is AI used in data analysis?

Data analysts use AI in a variety of ways, ranging from cleaning data to using it to make predictions about the future. AI automates and speeds up data processing, cleansing, and analysis via several methods: 

• Data preprocessing: AI addresses inconsistencies in raw data and cleanses it for analysis. 

• Data transformation: Your AI interface structures data to work with a various tools. 

• Natural language processing (NLP): By means of sophisticated language-learning technology, AI can perform sentiment analysis, language translation, and data summarization.

• Speech recognition: An AI platform can learn to recognize human voices and transcribe what they say while determining patterns in data it can then input into virtual assistants and voice-controlled technologies.

• Audio and visual processing: Modern AI utilizes a multimodal approach to processing, extracting, and sorting information from audio and visual inputs. 

• Data visualization: AI creates visual representations of data in the forms of charts, graphs, and interactive dashboards. 

• Time-series analysis: This allows AI to predict future outcomes based on the patterns it discovers in past input data. Predictive analysis is key in a variety of fields, including meteorology and marketing. 

Performing the above with speed and accuracy allows you to winnow enormous amounts of data into its most salient points to contribute to high-level decision-making.

Which AI tool is best for data analysis?

Determining the best AI tool for data analysis depends on the functionality you need. Pros and cons exist with each program. Popular AI tools for data analysis include: 

Microsoft Power BI 

This interface automates data exploration and predictive analytics and presents the results in interactive dashboards. Microsoft Power BI is reasonably affordable and highly customizable. You can also easily integrate it with programs such as Excel, SQL, and various cloud-based platforms. 

Some people report a significant learning curve when it comes to learning Power BI. It’s reliant on the Microsoft ecosystem, and is only available while online, which may pose a problem for sharing information with others who have spotty internet connectivity. 

Polymer

Polymer transforms static, spreadsheet-based data into interactive, searchable visual databases. It offers real-time, easily intelligible insights via conversational AI. It can easily sync data from multiple sources, its dashboards are highly customizable, and it’s highly adaptable, possessing use cases in fields such as marketing, e-commerce, and enterprise sectors. 

Polymer’s customizability isn’t endless nor appropriate for every business use case. It initially has a difficult learning curve and works best when connected to the internet.

Tableau

Tableau helps you understand and visually represent data from multiple sources via shareable, interactive visual dashboards. Its drag-and-drop functionality is far simpler than other code-based analytics tools, making it preferable for those lacking a coding background. Its many applications include data management and governance features. 

Tableau’s broad applicability profile can make it difficult for beginners to use. Certain functionalities are somewhat limited, and the program’s overall complexity results in slower loading speeds. It is also somewhat price-prohibitive compared to other AI data analytics tools.

Can I use ChatGPT to analyze data?

If you have a premium account, the latest iteration of ChatGPT, GPT-4, allows for data analysis capabilities via its Advanced Data Analysis feature. 

The Advanced Data Analysis feature allows for multimodal inputs. This means it can parse a variety of media, including: 

• Text

• PDFs and other large documents

• Videos

• Images

• Audio files

The program can read these inputs and then describe and cleanse them. However, you should still check the accuracy of its output regularly. You can do this manually or by prompting the program to explain or describe the work it’s doing so it can self-correct along the way. 

As convenient as this program is, it has its drawbacks. You can’t connect ChatGPT’s Advanced Data Analysis tool to other programs, meaning you must upload data rather painstakingly, one file at a time. It has trouble with unstructured, large, or particularly complex data sets and doesn’t automatically understand domain-specific use cases. And because it’s a large language model (LLM) program by nature, you have to figure out how to prompt the interface in more or less proper English to get output relevant to your business needs. 

The future of AI in data analysis

A majority of businesses continue to invest in AI for data analysis and other purposes. According to a Bloomberg report, the AI industry will be worth $1.3 trillion by 2032 [1]. 

As more and more businesses integrate AI into their data analysis workflows, they will continue to come up against an ever-changing battery of regulations and requirements, non-compliance, which can result in both reputational and legal consequences. Currently, businesses using AI for data analysis purposes must comply with laws such as: 

• The California Consumer Privacy Act (CCPA)

• The General Data Protection Regulation (GDPR)

• The Health Insurance Portability and Accountability Act (HIPAA)

Any AI data analysis tool you use is only as good as the data put into it. You need to make sure data is kept confidential and that you obtain it with user consent. Because programmers train AI models on massive stores of data, said data will sometimes include sensitive consumer data, protected intellectual property, or biased information. 

Learn about AI data analysis on Coursera

AI is changing the field of data analysis, offering value in terms of efficiency, productivity, and speed.

Continue learning about AI data analysis and build in-demand skills. For example, consider beginning with the Google Data Analytics Professional Certificate. Further expand your knowledge with IBM’s Data Analytics with Excel and R Professional Certificate, both of which are available on Coursera.