Boost Your Data Analysis with Assistance From Google's Gemini

Editor's Note: Google announced on February 8, 2024 that Duet AI and Bard will be moved under the Gemini product umbrella. This blog has been updated to reflect the new branding. 

Modern data analysis is often bogged down in repetitive manual tasks, limiting the time they can devote to deriving impactful insights from their work. Gemini for Google Cloud and Gemini for Google Workspace (formerly Duet AI) introduce a new data analysis solution by automating these responsibilities through natural language conversations.

Whether it's aggregating data, generating SQL queries, or even producing presentation decks, Gemini liberates data teams to focus purely on high-value analysis. With Gemini-assisted data analysis, processes become exponentially more efficient, intuitive, and rewarding.

Understanding Gemini & Its Benefits for Data Analysis

Gemini is a tool that blends conversational and generative AI capabilities to streamline workflows in both Google Workspace and the Google Cloud Platform.

Gemini for Google Cloud is designed to empower businesses by delivering a new cloud experience that’s personalized and intent-driven. It offers natural language interaction as well as coding assistance and automation for tasks like SQL coding, visualization, data prep, and collaboration.

Gemini for Google Workspace has the same end goal: to augment the way teams collaborate and perform tasks. However, the way it goes about it is slightly different. Gemini for Google Workspace focuses on injecting generative artificial intelligence or "gen AI" into Google Docs, Meet, Gmail, and other Workspace applications. It provides smart writing suggestions, image-to-text visualization, and video enhancements that empower users to produce their best work.

While both versions of the tool have many use cases, one of the most popular is to analyze data and get insights through natural language conversations. With Google Gemini-assisted operations, data analysts can get quick insights and visualize data trends through simple dialogue. This simplicity makes data democratization across teams and roles throughout business organizations possible without the need for expert code or data analytics knowledge.

Gemini for Google Cloud, in particular, features impactful enhancements to accelerate core workflows like data preparation, exploration, visualization, and sharing. It integrates directly into services like BigQuery, Looker, and Data Studio to assist in contextually relevant ways.

Google Gemini nimbly parses natural language queries and instructions to handle tedious data tasks automatically. This allows analysts to focus their energy on critical thinking and distilling insights from their work.

Decoding Google Gemini's Features for Data Analysis

Some of Gemini for Google Cloud's and Gemini for Google Workspace's key capabilities for supercharging analysis include:

• SQL Auto-Completion: Gemini provides intelligent code suggestions as you type SQL queries to save time and reduce errors.
• SQL Generation: You can generate full SQL queries just by describing the data relationships you want to analyze in plain English.
• SQL Summarization: For complex queries, you can prompt Gemini to explain them in simple terms to help you better understand the intent and logic.
• Data Prep Automation: Take care of repetitive ETL tasks like joining, filtering, aggregating, and cleaning datasets through conversational instructions.
• Interactive Visualization: Quickly create customizable charts and images visualizing your data by describing what you want to see.
• Presentation Generation: You can have Gemini for Google Workspace auto-build presentation slide decks highlighting key data insights, visuals, and takeaways from your analysis.
• Collaboration: Gemini for Google Workspace also facilitates better teamwork through capabilities like annotated queries and integration into Docs, Sheets, and Slides.

Why GeminiStands Out Among Other Tools

While other AI models offer attractive capabilities like chat assistance and generating creative content, Google's Gemini solutions represents a major leap forward in augmented analytics for a few reasons:

• It fluently understands natural language requests to automate rote data tasks.
• Gemini integrates directly into Google Workspace applications and Google Cloud data services instead of being a separate tool.
• It focuses specifically on boosting productivity for data teams and is designed for that workflow.
• Gemini continually evolves with the latest AI advancements from Google research.
• As a fully managed Google service, Gemini provides enterprise scale, reliability, and support.

Gemini enables a paradigm shift from just performing analysis to optimizing the time spent deriving insights. Google Gemini-assisted data science promises to fundamentally transform the role of data teams.

Unlocking Gemini's Potential with Google Entities

Google Entities inject contextual awareness into Gemini for Google Cloud and Google Workspace, allowing it to provide more tailored and relevant recommendations aligned with your organizational knowledge. By connecting these entities to Gemini, its understanding goes beyond just public data to in-depth specifics.

This advanced understanding can be seen in several enhanced Gemini capabilities.

Auto-Completion of Company Domain Terms

As analysts type SQL queries or ask natural language questions, Gemini for Google Cloud can automatically suggest relevant entities for faster coding with fewer errors. For example, it could auto-complete product names, regional office identifiers, and customer-type classifications based on connected entities.

Contextual Explanations

Gemini for Google Cloud and Google Workspace can provide definitions and explanations for company-specific entities referenced in queries, analyses, and documents to keep stakeholders aligned. This feature helps onboard new employees faster.

Relevant Data Recommendations

Based on connected entities and activity context, Gemini can proactively recommend useful datasets, metrics, and joins for analysis. This accelerates exploration and ideation.

Tailored Generated Outputs

Any code, analysis, and documents auto-generated by Gemini for Google Cloud can include company entities where relevant to make the outputs more customized and actionable. For example, a generated sales report can reference specific product lines and geographies.

Best Practices for Integrating Entities with Google Gemini

Follow these best practices to maximize the value of Entities for enhanced Gemini intelligence:

• Ingest as many relevant entities as possible across people, places, products, terminologies, etc. to build Gemini's domain understanding. Maintain this over time.
• Structure entities to align with how your teams reference information for easy associative discovery. For example, match employee entity terminology to human resource information systems (HRIS).
• Connect entities from across all enterprise systems — HRIS, CRM, ERP, KM, etc. — into a unified graph.
• Assign permissions thoughtfully so certain Entities are visible only to certain Gemini users and data teams depending on sensitivity.
• Fully leverage entity APIs and automation potential, including keeping Entities updated programmatically.
• Analyze entity usage data in Gemini to focus ingestion on high-value knowledge areas over time.
• Build a process to rapidly incorporate new Entities spawned from organizational changes and growth.

Steps to Get Started With Gemini & Google Entities

Follow these best practices to effectively integrate Google Entities with Gemini for enhanced analytics:

1. Catalog Your Key Entities: Thoroughly audit and catalog the key entities across your organization's knowledge graph. These encompass elements like employees, products, customers, regions, domain terminologies, codes, and more. Prioritize high-value entities that can boost analytics.
2. Connect Entities to Analytics Services: Link your compiled entity data to the Google Cloud services where Gemini is enabled, such as BigQuery. The connections allow Gemini to ingest entities as contextual knowledge. Manage access permissions if certain entities contain sensitive information.
3. Leverage Bulk Upload and APIs for Ongoing Updates: Bulk upload entity information and leverage Gemini's API integration capabilities for seamless ingestion at scale rather than manual entry. Build processes to keep entities current as information changes.
4. Observe Contextual Improvement: Interact with Gemini organically in analytics workflows and observe how entity awareness improves auto-completion, recommendations, explanations, and generated outputs. Gain hands-on visibility into value.
5. Expand Entities and Usage Iteratively: Analyze initial entity usage data to identify high-ROI knowledge areas. Rapidly iterate on expanding cataloged entities, connections, and Gemini conversational experiences to maximize intelligence gain.
6. Operationalize Entity Updates: Build pipelines to continually update entities with new and modified information from enterprise systems like HR, CRM, and ERP. Fresh Entity data ensures Gemini has the latest contextual knowledge.
7. Extend Beyond Gemini: Leverage Entity APIs and integrations for workflows beyond Gemini, like centralizing cross-system knowledge management, data mapping, reporting, and more. Maximize their automation potential.

Anticipating & Overcoming Challenges with Google Gemini

While immensely powerful, introducing Gemini for Google Cloud and Gemini for Google Workspace to your business can come with some common transitional challenges. The good news is that once you're familiar with these hurdles, it will be easier to overcome them.

Learning Capabilities

Given the breadth of Gemini's capabilities, data teams should invest time upfront to thoroughly understand the solution's full scope rather than assuming limitations.

Useful strategies include reading documentation extensively, attending training webinars, and experimenting with open-ended conversations to reveal possibilities.

Overcoming Legacy Process Inertia

Transitioning to new tools like Gemini can encounter resistance from teams entrenched in legacy workflows. To drive adoption, clearly communicate quantified productivity gains and business insights enabled by Gemini through metrics analysis.

Implement monetary incentives connected to Gemini usage goals. Continuously highlight exponential value realized at scale by other groups who have adopted Gemini more extensively.

Hesitance to Trust AI

Some data analysts may hesitate to trust AI-generated insights from Gemini for Google Cloud or Gemini for Google Workspace. Build confidence by enabling GeminiI's scoring of output confidence levels, explanations of reasoning, and overall result transparency.

Rigorously validate Gemini's initial outputs through feedback from internal teams. Gradually increase the level of influence and agency granted to Gemini on more critical analytical tasks as trust develops.

Managing Organizational Change

Introducing new AI tools like Gemini requires organizational change management. Roll out Gemini in a phased, pilot-first manner rather than full-scale implementation all at once.

Provide extensive training, coaching, and support resources to smooth adoption. Regularly gather user feedback through surveys and interviews to uncover integration friction points and continuously improve workflows.

Securing Buy-In

Gaining executive buy-in and sponsorship can accelerate Gemini's adoption. Quantify Gemini, and increased analytical throughput. Have leadership explicitly mandate usage goals to drive change.

Best Practices for Gemini Data Analysis Success

Once your organization has adopted Gemini for Google Cloud and Google Workspace, you can follow these best practices to gain proficiency and maximize productivity:

• Frame prompts conversationally using natural language, not just keywords. Provide plenty of context for each query.
• Start with simple, bounded requests and expand complexity as your team's AI skills improve.
• Review Gemini's explanations of its reasoning where available to build understanding.
• Rapidly iterate in conversations instead of serving Gemini's repeated one-off requests.
• Provide explicit positive and negative feedback to improve Gemini's effectiveness.
• Analyze Gemini conversation analytics to optimize experiences.
• Proactively identify and resolve friction points through surveys and interviews within your organization.

Google Solutions from Promevo

Get the most out of transformative Google Cloud and Google Workspace innovations like Gemini by partnering with Promevo.

Our certified Google experts work as an extension of your team to successfully adopt breakthrough innovations like Gemini through assessment, migration, integration, training, and ongoing optimization. Promevo accelerates your analytics productivity by combining our technical experience with knowledge of your data stack and business goals.

Our end-to-end services unlock Gemini's full potential so you can focus resources on scaling data-driven insights. With Promevo, you get enterprise-grade solutions tailored specifically to realize value from Google tools like Gemini.

If you're interested in trying Gemini for Google Cloud or Google Workspace, contact us today.

FAQs: Gemini-Assisted Data Analysis

What is Google Gemini?

Gemini for Google Cloud is designed to empower businesses by delivering a new cloud experience that’s personalized and intent-driven. It offers natural language interaction as well as coding assistance and automation for tasks like SQL coding, visualization, data prep, and collaboration.

Gemini for Google Workspace has the same end goal: to augment the way teams collaborate and perform tasks. However, the way it goes about it is slightly different.

Gemini for Google Workspace focuses on injecting generative artificial intelligence or "gen AI" into Google Docs, Meet, Gmail, and other Workspace applications. It provides smart writing suggestions, image-to-text visualization, and video enhancements that empower users to produce their best work.

How can Google Gemini improve data analysis?

Gemini for Google Cloud streamlines repetitive and manual processes, allowing analysts to focus on higher-value work like modeling, monitoring, and application of insights derived from data.

How much does Gemini cost?

As of 2023, Gemini for Google Cloud is only available in preview. Gemini for Google Workspace, on the other hand, is generally available. Contact Promevo to schedule a demo.