Google Gemini 2.0 Flash brings Python power to business analysts

Anyone who has ever had a job that required intensive analysis will tell you that every speed gain they can find is the equivalent of getting an extra 30, 60 or 90 minutes out of their day.

Automation tools in general, and AI tools in particular, can help business analysts who need to process massive amounts of data and communicate it succinctly.

In fact, a recent Gartner analysis shows: “An AI-First strategy leads to higher returns”, states that the most advanced enterprises depend on AI to increase the accuracy, speed and scale of analytical work to achieve three core goals – business growth, customer success and cost efficiency – with competitive intelligence at the heart of each goal.

Google’s recently released Gemini 2.0 Flash provides business analysts with greater speed and flexibility in defining Python scripts for complex analyses, giving analysts more precise control over the results they generate.

Google claims that Gemini 2.0 Flash builds on the success of 1.5 Flashthe most accepted model yet for developers.

Gemini 2.0 Flash outperforms 1.5 Pro on key benchmarks and delivers twice the speed, according to Google. 2.0 Flash also supports multimodal input, including images, video, and audio, as well as multimodal output, including natively generated images mixed with text and controllable multilingual text-to-speech (TTS) audio. It can also call native tools such as Google Search, code execution, and third-party user-defined functions.

Take Gemini 2.0 Flash for a test drive

VentureBeat gave Gemini 2.0 Flash a series of increasingly complex Python script requests to test its speed, accuracy, and precision in dealing with the nuances of the cybersecurity market.

Usage Google AI Studio To access the model, VentureBeat started with simple script requests and worked up to more complex requests aimed at the cybersecurity market.

What’s immediately noticeable about Python scripting with Gemini 2.0 Flash is how fast it is (nearly instantaneous, in fact) to deliver Python scripts and generate them in seconds. It is noticeably faster than 1.5 Pro, Claude and ChatGPT when processing increasingly complex prompts.

VentureBeat asked Gemini 2.0 Flash to perform a typical task a business or market analyst would have to do: create a matrix comparing a set of vendors and analyze how AI is used in each company’s products.

Analysts often need to quickly create tables in response to sales, marketing, or strategic planning requests, and they usually need to include unique benefits or insights into each business. This can take hours or even days to do manually, depending on an analyst’s experience and knowledge.

VentureBeat wanted to make the prompt request realistic by having the script include an analysis of 13 XDR vendors, as well as provide insight into how AI helps said vendors process telemetry data. As is the case with many requests analysts receive, VentureBeat asked Python to create an Excel file with the results.

Here is the prompt we gave Gemini 2.0 Flash to run:

Write a Python script to analyze the following cybersecurity vendors that have integrated AI into their XDR platform and create a table showing how they differ from each other in implementing AI. Let the first column be the company name, the second column be the company’s products that integrate AI, the third column be what makes them unique and the fourth column be how AI helps process the telemetry data from their XDR platforms in detail with an example . No web scraping. Create an Excel file of the result and format the text in the Excel file so that it is free of brackets ({}), quotation marks (‘), and HTML code to improve readability. Give the Excel file a name. Gemini 2 flash test.
Cato Networks, Cisco, CrowdStrike, Elastic Security XDR, Fortinet, Google Cloud (Mandiant Advantage XDR), Microsoft (Microsoft 365 Defender

Using Google AI Studio, VentureBeat created the following AI-powered XDR Vendor Comparison Python script request, producing Python code in seconds:

VentureBeat then saved and entered the code Google Colab. The purpose of this was to see how bug-free the Python code was outside of Google AI Studio and also measure compile speed. The code worked flawlessly and without errors and produced the Microsoft Excel file Gemini_2_flash_test.xlsx.

The results speak for themselves

Within seconds the script was executed and Colab reported no errors. It also provided a message at the end of the script that the Excel file was ready.

VentureBeat downloaded the Excel file and found it was done in less than two seconds. The following is a formatted view of the Excel table in which the Python script is provided.

The total time it took to get this table ready was less than four minutes, from submitting the prompt, getting the Python script, running it in Colab, downloading the Excel file, and quick formatting .

A convincing argument for unleashing AI on monotonous tasks

For the many professionals who have worked in a variety of business, competitive and market analyst roles throughout their careers, AI is the force multiplier they are looking for to save hours on repetitive, monotonous tasks.

Analysts naturally have a high degree of intellectual curiosity. Unleashing AI in the most mundane and repetitive parts of their work and equipping them to create the equations and matrices they often need to develop quickly is a powerful boost to an entire team’s productivity.

Managers and leaders of business, competitive analysis and marketing teams should consider how rapid advances in models, including Google’s Gemini 2.0 Flash, can help their teams manage growing workloads. Helping to alleviate that burden gives analysts the opportunity to do what they love and do best: use their intuition, intelligence and insight to deliver exceptionally valuable ideas.