Understanding AI Data Analysis

Data is big business, and big data may in time prove to be bigger still. 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 in various industries and sectors use artificial intelligence (AI) tools to streamline established data analysis tasks. Traditionally, this required human data analysts to aggregate and analyze data manually. However, challenges with skill gaps and time constraints can prevent organizations from optimizing their use of data, a stumbling block that AI can help professionals overcome. AI helps speed up the data analysis process because it can handle enormous stores of data and parse them in seconds, much faster than a human data analyst. 

That doesn’t mean anyone is trying to replace all human data analysts with AI. In fact, data analysts utilize AI to automate and streamline manual 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. 

And that’s just the beginning. Explore deeper into what AI data analysis can do.

How is AI used in data analysis?

In addition to the above tasks, data analysts use AI in a variety of ways, ranging from automated data cleaning to utilizing AI’s predictive capabilities to forecast future business trends. These and other tasks are possible because AI automates and speeds up data processing, cleaning, and analysis using several methods, including:

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

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

• Natural language processing (NLP): By means of sophisticated technology, AI can perform tasks like 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, which can then be 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 can create visual representations of data in the form 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 finance. 

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?

Data analysts don’t perform the same tasks in every role. Some examples of specializations include:

• Descriptive analytics: Spotting trends based on past patterns

• Prescriptive analytics: Endorsing certain specific business methodologies 

• Predictive analytics: Determining future trends and acting proactively in response 

• Diagnostic analytics: Analyzing why discrete business outcomes occurred

Each of these tasks demands a set of tools not necessarily suited to the rest. In other words, determining the best AI tool for data analysis depends on which functionality you need. Pros and cons exist with each program, of course. Popular AI tools for data analysis include: 

Microsoft Power BI 

This interface allows you to collate data from a variety of sources and create interactive visualizations from the results. Microsoft Power BI is highly customizable, and you can integrate it with programs such as Excel, SQL, and various cloud-based platforms. Power BI can handle more data than Excel itself [1].

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 is an application programming interface (API) that can help you sync data and AI capabilities with your applications. Polymer requires no coding knowledge to operate. Its dashboards are highly collaborative, and it’s highly adaptable with use cases in fields such as marketing, e-commerce, and enterprise [2]. 

Polymer may be cost-prohibitive for some: Its plans start at $500 a month. That said, they do offer a free seven-day trial. Polymer’s customizability is limited and not appropriate for every business use case. It can have a difficult learning curve and works best when connected to the internet.

Tableau

Tableau is Salesforce’s business analytics software. It can help you understand and visually represent data. 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. In fact, you need to be conversant in SQL to get the most out of Tableau’s functionality profile. 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. At scale, it can be very costly [3].

Can I use ChatGPT to analyze data?

Rather than purchase an expensive software program stocked with functionality you don’t plan on using, you can use the free, open-source AI interface ChatGPT as a data analysis tool: With a premium account, the latest iteration of ChatGPT, GPT-4, grants you several data analysis capabilities via its Advanced Data Analysis feature [4]. 

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 using ChatGPT for data analysis can be, 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. The AI industry is predicted to be worth $1.3 trillion by 2032 [5]. 

Increased AI use comes with an increase in AI use regulations designed to protect user privacy from the potential harm that could come from unchecked companies and tools. 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)

Due to the fact that programmers train AI models on massive stores of data, said data will sometimes include sensitive consumer information or protected intellectual property. The above regulations, among others, exist to help you make sure that customer data is kept confidential and that you obtain it with user consent. The thrust of AI data regulation is always to help you observe best practices while you concentrate on your business goals [5].

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 to immerse yourself in the basics of data analytics, understand key data functions, and gain experience with popular business analytics tools. Further expand your knowledge with IBM’s Data Analytics with Excel and R Professional Certificate. This course can help you learn how to apply up-to-date data analysis techniques to Excel. Both of these courses are available on Coursera.