GovAI is capable to consuming, analyzing, and interpreting datasets in multiple different formats. This capability can be a real time saver when it comes to any dataset, large or small.
How does it work?
When you provide GovAI with a dataset and a prompt, GovAI first examines the dataset by reading the column headings and values to understand its contents. It then analyzes the question you’ve asked and generates the necessary code to perform the required analysis. In essence, GovAI translates your question into a format that a computer can understand, allowing it to run the analysis and deliver the insights you need.
What makes using GovAI different than using other tools?
One of GovAI's key strengths is its ability to write complex formulas for you, extracting exactly the information you need without the manual effort. In Excel, achieving the same results might require numerous data manipulation steps, intermediate calculations, and intricate formulas.
Another strength of GovAI is its ability to understand the context of the data. Based on this understanding, it can provide you with unique insights and exploratory analysis that go beyond standard tools.
Finally, perhaps the greatest advantage of GovAI is its ease of use. You simply ask questions about the data in natural language, and GovAI responds with the analysis, saving you time and effort.
What are some examples of how GovAI can be used for data analysis?
General exploration of the data: "What are some key insights or patterns that can be discovered from this dataset"
Check for data cleanliness: "Given this dataset are there any anomalies to be aware of"
Numeric analysis: "How many incidents were recorded within x month"
Pattern analysis: "Is there between daytime temperatures and public pool attendance"
Correlation analysis: "Is there any correlation between the number of speeding ticket issued and time of day?"
Plotting graphs "Plot the number of parking tickets across each hour of the day, and overlay the amount of revenue generated on top. Use different scales in the vertical axis as needed"
See it in action
In the video below, we go through an example of a large data set of parking tickets extracting insights, discovering pattern, and arriving at potential explanations of these patterns.