Anyone who has had a job that required intensive amounts of analysis will tell you that any speed gain they can find is like getting an extra 30, 60, or 90 minutes back out of their day.
Automation tools in general, and AI tools specifically, can assist business analysts who need to crunch massive amounts of data and succinctly communicate it.
In fact, a recent Gartner analysis, “An AI-First Strategy Leads to Increasing Returns,” states that the most advanced enterprises rely on AI to increase the accuracy, speed, and scale of analytical work to fuel three core objectives — business growth, customer success, and cost efficiency — with competitive intelligence being core to each.
Google’s newly released Gemini 2.0 Flash provides business analysts with greater speed and flexibility in defining Python scripts for complex analysis, giving analysts more precise control over the results they generate.
Google claims that Gemini 2.0 Flash builds on the success of 1.5 Flash, its most adopted model yet for developers.
Gemini 2.0 Flash outperforms 1.5 Pro on key benchmarks, delivering twice the speed, according to Google. 2.0 Flash also supports multimodal inputs, including images, video, and audio, as well as multimodal output, including natively generated images mixed with text and steerable text-to-speech (TTS) multilingual audio. It can also natively call tools like Google Search, code execution, and third-party user-defined functions.
Taking Gemini 2.0 Flash for a test drive
VentureBeat gave Gemini 2.0 Flash a series of increasingly complex Python scripting requests to test its speed, accuracy, and precision in dealing with the nuances of the cybersecurity market.
Using Google AI Studio to access the model, VentureBeat started with simple scripting requests, working up to more complex ones centered on the cybersecurity market.
What’s immediately noticeable about Python scripting with Gemini 2.0 Flash is how fast it is — nearly instantaneous, in fact — at providing Python scripts, generating them in seconds. It’s noticeably faster than 1.5 Pro, Claude, and ChatGPT when handling increasingly complex prompts.
VentureBeat asked Gemini 2.0 Flash to perform a typical task that a business or market analyst would be requested to do: Create a matrix comparing a series of vendors and analyze how AI is used across each company’s products.
Analysts often have to create tables quickly in response to sales, marketing, or strategic planning requests, and they usually need to include unique advantages or insights into each company. This can take hours and even days to get done manually, depending on an analyst’s experience and knowledge.
VentureBeat wanted to make the prompt request realistic by having the script encompass an analysis of 13 XDR vendors, also providing insights into how AI helps the listed vendors handle telemetry data. As is the case with many requests analysts receive, VentureBeat asked Python to produce an Excel file of the results.
Here is the prompt we gave Gemini 2.0 Flash to execute:
Write a Python script to analyze the following cybersecurity vendors who have AI integrated into their XDR platform and build a table showing how they differ from each other in implementing AI. Have the first column be the company name, the second column the company’s products that have AI integrated into them, the third column being what makes them unique and the fourth column being how AI helps handle their XDR platforms’ telemetry data in detail with an example. Don’t web scrape. Produce an Excel file of the result and format the text in the Excel file so it is clear of any brackets ({}), quote marks (‘) and any HTML code to improve readability. Name the Excel file. Gemini 2 flash test.
Cato Networks, Cisco, CrowdStrike, Elastic Security XDR, Fortinet, Google Cloud (Mandiant Advantage XDR), Microsoft (Microsoft 365 Defender XDR), Palo Alto Networks, SentinelOne, Sophos, Symantec, Trellix, VMware Carbon Black Cloud XDR
Using Google AI Studio, VentureBeat created the following AI-powered XDR Vendor Comparison Python scripting request, with Python code produced in seconds:
Next, VentureBeat saved the code and loaded it into Google Colab. The goal in doing this was to see how bug-free the Python code was outside of Google AI Studio and also measure its speed of being compiled. The code ran flawlessly with no errors and produced the Microsoft Excel file Gemini_2_flash_test.xlsx.
The results speak for themselves
Within seconds, the script ran, and Colab signaled no errors. It also provided a message at the end of the script that the Excel file was done.
VentureBeat downloaded the Excel file and found it had been finished in less than two seconds. The following is a formatted view of the Excel table where the Python script was delivered.
The total time needed to get this table done was less than four minutes, from submitting the prompt, getting the Python script, running it in Colab, downloading the Excel file, and doing some quick formatting.