What are some AI-powered tools for data analysis?

My go-to platforms shift depending on the project. For general-purpose exploration and visualization, I lean towards powerhouses like Microsoft Power BI and Tableau. Their AI-powered insights save me hours of sifting through data, highlighting patterns and correlations I might have missed. But here's the thing: the perfect tool is only as good as the mind behind it. My journey with AI-powered data analysis has been one of continuous learning and adaptation.

Tools like Trifacta or OpenRefine use AI to identify patterns and anomalies in large datasets, making it easier to detect and correct errors.

I have only used named entity extraction before which is a pretty basic NLP thing. These days it seems like large language models are in a great spot to disrupt this. You can make some amazing tools that take raw text and output - summaries - named entities - key phrases At this point you are basically limited only by how creatively you can leverage these tools. Of course if you don’t need anything more than simple regex matching you probably don’t need AI but I predict more and more people will be using agents to process and extract structured data from unstructured text

Platforms such as Tableau or Power BI employ AI capabilities to create more dynamic and interactive visualizations, aiding in identifying trends and patterns more intuitively.

The best way to visualize data in my experience is JavaScript for dynamic web pages. For quick reports and analysis you can leverage matplotlib, dataframe tools in notebooks, or even excel can be good for quick graphics. Once you know it’s something you will consistently want, then it should become a web page in my opinion with interactive components for grouping and filtering With AI I would guess the interface might be more drop in the data and ask questions for it to visualize things. Haven’t found great tools for this but I’m sure they exist or are coming soon

Tools like Splunk use AI to analyze large volumes of unstructured data and find relationships, enabling the discovery of valuable insights in complex datasets.

LLMs are good with semantic (contextual) understanding and generation of unstrructured data but when it comes to mathematical reasoning and logic calculation on tabular data - the current LLMs as stand alone are still not above human level intelligence. This is an active research area for industry and is a growing area. One methodology which is being tested is to make LLM learn how to invoke APIs of external tools say Python or Snowflake to auto generate SQL, other methodology is use Chain of Code Reasoning (akin to Chain of Thought - prompt engineering) where LLMs will generate code and explain with "reasoning" multiple steps for data exploration and data analysis.

I have seen some interesting copilot and agent setups that will read raw data into python scripts that are written by the AI to do feature extraction and analysis. I’m interesting how this area develops as models get better at interpreting data. So far it seems more reliant on good prompts to get meaningful results but I predict soon the tools will be much faster and better than humans at exploration

Tools like RapidMiner or DataRobot leverage AI to automate the construction and optimization of predictive models, streamlining the data analysis process and enhancing result accuracy.

Platforms like IBM Watson or Google Cloud Platform offer AI services that enable predicting future trends or outcomes based on historical data analysis.

One use case of data once it is cleaned is targeted marketing. Machine learning is used to accurately segment cleaned data so that marketers can implement targeted advertising quickly and efficiently. Databricks is a platform that enables businesses to implement customer segmentation for targeted advertising.

AI-powered tools for data analysis include IBM Watson Studio, Google AutoML, and Microsoft Azure Machine Learning. These tools automate pattern recognition, predictive modeling, and data insights, enhancing efficiency in complex data processing tasks.