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

Anyone who has a job that requires a lot of analysis will tell you that any speed boost they can find is like squeezing an extra 30, 60, or 90 minutes out of their day.

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

In fact, a recent analysis by Gartner, “An AI-first strategy increases returnsstates that the most advanced organizations are relying on AI to increase the accuracy, speed, and volume of analytical work in support of three core goals—business growth, customer success, and cost efficiency—with competitive intelligence being core to each.

Google Gemini 2.0 Flash is newly released It provides business analysts with greater speed and flexibility in specifying Python scripts for complex analysis, giving analysts more precise control over the results they generate.

Google claims Gemini 2.0 Flash It builds on the success of Flash 1.5the most adopted model to date for developers.

The Gemini 2.0 Flash outperforms the 1.5 Pro in key benchmarks, delivering twice the speed, according to Google. Flash 2.0 also supports multimedia input, including images, video, and audio, as well as multimedia output, including locally generated images mixed with text and multilingual text-to-speech (TTS) audio. It can also call tools such as Google search and execute third-party user-defined code and functions.

He took the Gemini 2.0 Flash for a test drive

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

Use Google Artificial Intelligence Studio To arrive at the model, VentureBeat started with simple scripting requests, working up to more complex requests centered around the cybersecurity market.

What’s immediately noticeable about Python programming using Gemini 2.0 Flash is how quickly — almost instantaneously, in fact — Python scripts can be made available, generating them in seconds. It’s noticeably faster than 1.5 Pro, Claude, and ChatGPT when handling increasingly complex claims.

VentureBeat asked Gemini 2.0 Flash to perform a typical task a business or market analyst is asked 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 features or insights that are unique to each company. This can take hours and even days to accomplish manually, depending on the analyst’s experience and knowledge.

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

Here’s the claim we made for Gemini 2.0 Flash to implement:

Write a Python script to analyze the following cybersecurity vendors that have AI built into their XDR system and build 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 AI is integrated into, the third column be what makes it unique, and the fourth column be how AI helps handle the telemetry data of their XDR platforms in detail with an example . Don’t throw away the web. Create an Excel file of the result and format the text in the Excel file so that it is free of any parentheses ({}), quotes (‘) and any HTML code to improve readability. Name the Excel file. Gemini flash test 2.
Cato Networks, Cisco, CrowdStrike, Resilience Security XDR, Fortinet, Google Cloud (Mandant Advantage XDR), Microsoft (Microsoft 365 Defender

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

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

The results speak for themselves

Within seconds, the script was running, and Kolab reported 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 that it was completed in less than two seconds. Below is a formatted view of the Excel table where the Python script was delivered.

The total time needed to complete this table was less than four minutes, from submitting the claim, getting the Python script up and running in Colab, downloading the Excel file, and doing some quick formatting.

A compelling case for unleashing artificial intelligence on monotonous tasks

For 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 shave hours off monotonous, repetitive tasks.

Analysts naturally have a high degree of intellectual curiosity. Unleashing AI into the more mundane and repetitive parts of their jobs and equipping them to quickly create the comparisons and matrices they are often asked to develop is a huge boost to the productivity of the entire team.

Managers, leaders of business, competitive analysis, and marketing teams need to consider how rapid advancements in models, including Google Gemini 2.0 Flash, can help their teams keep increasing workloads under control. Helping lift this burden will give analysts the opportunity to do what they enjoy and do best: use their intuition, intelligence, and insight to deliver insights of exceptional value.