Google Gemini 2.0 Flash brings the power of Python to business analysts

Anyone who’s ever had a job that requires a lot of analysis will tell you that any speed gain you can find is like taking back an extra 30, 60, or 90 minutes of your day.

Automation tools in general, and artificial intelligence 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, “An AI-focused strategy leads to increasing returns”, states that the most advanced companies depend on AI to increase the accuracy, speed and scale of analytical work to drive three core objectives (business growth, customer success and cost efficiency), with competitive intelligence being fundamental to each one of them.

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

Google claims that Gemini 2.0 Flash builds on the success of 1.5 Flashits most adopted model so far by developers.

Gemini 2.0 Flash outperforms 1.5 Pro in key benchmarks and offers twice the speed, according to Google. Flash 2.0 also supports multimodal inputs, including images, video, and audio, as well as multimodal outputs, including natively generated images mixed with text and multilingual, steerable text-to-speech (TTS) audio. You 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 script requests to test its speed, accuracy, and precision in addressing the nuances of the cybersecurity market.

Wearing Google AI Study To access the model, VentureBeat started with simple script requests and moved on to more complex ones focused on the cybersecurity market.

What’s immediately noticeable about Python scripting with Gemini 2.0 Flash is how fast (almost instant, in fact) they are at providing Python scripts and 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 asked to do: create a matrix comparing a number of vendors and analyze how AI is used in each company’s products.

Analysts often have to create tables quickly in response to sales, marketing, or strategic planning requests, and they typically need to include unique advantages or insights about each company. Doing this manually can take hours or even days, depending on the analyst’s experience and knowledge.

VentureBeat wanted to make the immediate request realistic by having the script encompass an analysis of 13 XDR vendors and also provide insights into how AI helps the listed vendors handle telemetry data. As is the case with many requests analysts receive, VentureBeat asked Python to generate an Excel file with the results.

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

Write a Python script to analyze the following cybersecurity vendors that have AI integrated into their XDR platform and create a table showing how they differ from each other in implementing AI. Make the first column the name of the company, the second column the company’s products that have AI built in, the third column what makes them unique, and the fourth column how the AI ​​helps manage the company’s telemetry data. their XDR platforms in detail with an example. . Don’t do web scraping. Generate an Excel file of the result and format the text in the Excel file so that it does not contain square brackets ({}), quotes (‘), or 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

Using Google AI Studio, VentureBeat created the following AI-powered XDR vendor comparison Python scripting request, with Python code produced in seconds:

VentureBeat then saved the code and uploaded it to Google Co.. The goal in doing this was to see how bug-free the Python code was outside of Google AI Studio and also measure its compilation speed. The code ran perfectly without errors and produced the Microsoft Excel file Gemini_2_flash_test.xlsx.

The results speak for themselves.

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

VentureBeat downloaded the Excel file and found it was 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 create this table was less than four minutes, from sending the message, getting the Python script, running it in Colab, downloading the Excel file, and doing a quick format.

A compelling argument for unleashing AI on monotonous tasks

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

Analysts, by nature, have a high degree of intellectual curiosity. Unlocking AI into the most mundane, repetitive parts of their jobs and equipping them to create the comparisons and matrices they are often asked to quickly develop is a powerful boost to the productivity of an entire team.

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 will give analysts the opportunity to do what they enjoy and do best, which is use their intuition, intelligence, and knowledge to generate exceptionally valuable insights.

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