Modern enterprises are practically “drowning” in data. By the end of this year, the global data volume is expected to reach nearly 200 zettabytes. Be it customer information or sales figures, this sheer volume of data makes it extremely challenging to extract any actionable insights. Traditional data analytics solutions often fall short in terms of speed, complexity, and the skills required to analyze the massive data explosion. This can lead to business failures in the form of missed opportunities or delayed decisions.

The question is whether the business world can use Generative AI for data analysis and convert raw data into actionable insights in a few seconds. Can GenAI be the “game changer” for data analysis? In this “how-to” guide, let’s explore how enterprises can combine Google Gemini and Agentspace technologies to extract “smart” decisions from data analysis.

The “what” and “why” of Generative AI for data analysis

In the traditional mode, data analytics tools essentially deliver insights and reports based on past events. Generative AI (or GenAI) tools elevate data analysis to the next level with capabilities like:

• Generating new insights.
• Hypothesizing business solutions.
• Predicting future trends.

Here’s “why” data-driven enterprises must use GenAI for data analysis:

• Speed – Delivers insights or answers to questions in minutes – not days or weeks.
• Scalability – Analyzes low to massive datasets with ease.
• Data patterns – Discovers hidden data patterns or anomalies (not visible to human analysts).
• Natural language responses – Answer complex questions in human language.
• Data democratization – Democratizes business data and makes it accessible to non-technical users.

How can Google AgentSpace and Gemini combine to deliver intelligent data-driven decisions?

1. Google Gemini acts as the “brain” or the large language model (LLM) powerhouse. It provides the core analytical capabilities of the GenAI system, including:
   • Understanding the contextual element.
   • Simplifying complex data relationships.
   • Generating a human-like response and insight.
2. Google AgentSpace serves as both the workspace and the orchestrator. As the operational platform, here are some of the capabilities of AgentSpace:
   • Ingesting the data.
   • Defining their analytical tasks.
   • Deploying the Gemini-powered intelligent agents.
   • Automating and managing the entire process workflow.

The following sections delve into each of these tasks in more detail. First, let’s look at some pre-analysis checklist items that need to be completed.

Setting the stage: Pre-analysis checklist

Here’s what enterprises must complete as part of their pre-analysis checklist: 

1. Define the objective.
   The first step is for enterprises to define the objective of using an AI-powered data analytics solution – for example, to reduce customer churn or identify future market trends.
2. Collect and prepare data from the right sources.
   The adage, “Garbage in, garbage out” is also applicable to GenAI-powered analytics. Identify the right data sources – for example, structured databases, spreadsheets, APIs, or cloud storage – that are relevant to the defined objective. GenAI models perform best with clean, structured data. While AI tools can enable data cleansing, the right data source can accelerate the entire process.
3. Grant the right access and permissions.
   The final preparatory step is to grant data access and permissions to both Google Gemini and Agentspace. This is crucial for secure data access and privacy.

Transforming data into decisions using GenAI analysis

Here’s a 5-step process for enterprises to transform raw data into decisions using Google Gemini and Agentspace:

1. Ingest the data using Google Agentspace.
2. Define the AI agent for analytical purposes by integrating Google Gemini.
3. Orchestrate the analysis workflow using Google Agentspace.
4. Review and validate the insights delivered by Google Gemini.
5. Translate data-driven insights into meaningful decisions.

Let’s dive into each of these steps with more depth.

Step 1 

Ingest data using Google Agentspace.

The first step is to ingest data into the analytical environment. Google Agentspace simplifies this process by extracting data by:

• Connecting to SQL and NoSQL databases.
• Connecting to cloud storage solutions like Google Cloud Storage or AWS S3.
• Integrating with third-party tools using APIs.
• Uploading files in CSV, JSON, or Excel format.

With its pre-built connectors, Google Agentspace can simplify the data onboarding process, thus minimizing both setup time and effort. Among the best UI/UX practices, user-friendly tools (or interfaces) with clear prompts can be used to configure data sources, along with a visual indication of the connection status.

Step 2 

Define the AI agent for analytical purposes by integrating Google Gemini.

Next, enterprises need to define a smart AI agent that can perform the data and analytical functions. With Google Gemini, they can now infuse AI into data analysis and create an intelligent AI agent in the Agentspace domain.

This process involves prompt engineering for drafting instructions (in natural human language) to query Google Gemini – for instance, “Analyze sales data from the last quarter to identify the top three performing regions and suggest reasons for their success.” Additionally, Google Gemini can be integrated as a core component within Google Agentspace to provide context and direct access to the ingested data

Based on their desired AI outcome, enterprises can also set parameters for:

• Scope of data analysis
• Output formats (for example, text, bulleted list, or table)
• Any specific constraints

Besides, with its prompt builders and UI templates, Google Agentspace provides configuration options for processing data using Google Gemini.

Step 3

Orchestrate the analysis workflow using Google Agentspace.

In the real world, data analysis is not a “one-step” process, but a comprehensive workflow. Using Google Agentspace, enterprises can now design a complete sequence of workflow operations, which also includes the analysis from Google Gemini.

Here’s a multi-step workflow that incorporates Google Gemini insights with other data processing actions:

1. Analyzing customer feedback using Google Gemini.
2. Triggering a sentiment analysis tool with Google Agentspace.
3. Visualizing the results automatically.

Enterprises can implement a conditional logic (for instance, “If Google Gemini identifies a high churn risk, then generate personalized outreach suggestions”). They can also schedule and automate this analysis workflow to run at regular intervals (for example, daily, weekly, or monthly). This ensures that the system is always generating the latest insights.

With an intuitive visual builder tool (with drag-and-drop functionality), enterprises can monitor the agent execution and send notification alerts for workflow completion or crucial findings.

Step 4

Review and validate the insights delivered by Google Gemini.

The next step involves reviewing and validating the insights provided by Google Gemini. This step is crucial to ensure the accuracy and relevance of the generated insights. A generated insight can be anything from a detailed text summary or numerical analysis to a complex chart or hypothesis. 

Here are some essential practices to validate the generated insights:

• Cross-check the insights against the existing knowledge or external data points.
• Perform iterative refinement to adjust the prompts (or workflows) to generate the desired output.

Google Agentspace provides intuitive dashboards that display the results and explain the logical reasoning behind the Google Gemini reports. Using the export feature, relevant insights can also be shared with (or reviewed by) other stakeholders.

Step 5

Translate data-driven insights into meaningful decisions.

The final step in this process is to translate the data-driven insights (delivered by Google Gemini) into meaningful actions or business decisions. Enterprises can also generate detailed reports summarizing these insights with other stakeholders. Besides, Google Gemini also provides actionable recommendations based on the analysis results.  

By integrating with third-party systems, Google Agentspace can also trigger the right action in systems like CRM, project management, and marketing. Here are a few examples of AI prompts:

• Add all high-risk customers identified by Gemini to a re-engagement marketing campaign.
• Create a new task in Jira for the product team based on user feedback analysis.

Besides, enterprises can also improve user interactions with customizable report templates and one-click sharing options.

Best practices in GenAI analysis

While enterprises can benefit from using LLM for data analysis, they must also focus on the following best practices to get the best of GenAI analysis:

1. Data security and privacy
   Every GenAI implementation must prioritize data security and privacy. This requires enterprises to ensure that Google Agentspace and other integrated services comply with their data privacy regulations and policies.
2. Ethical AI
   With Generative AI for data analysis, enterprises must still be mindful of avoiding any “bias” in their data feed and outputs. Ensure that any AI-powered analysis is driven by fairness and transparency.
3. Frequent iterations
   Instead of addressing every business problem in a single move, enterprises must learn to start small with a manageable use case, iterate by refining AI prompts and workflows, and then expand to other GenAI capabilities.
4. Continuous learning
   As the GenAI ecosystem continues to evolve, companies must strive to continuously refine their GenAI prompts and skills and stay updated on Google Gemini’s new features.
5. Human in the loop
   While GenAI is a powerful tool for data analysis solutions, it cannot replace human traits like critical thinking, domain expertise, and rational judgment.

Conclusion

Without AI data analysis tools, enterprises can find the “data-to-decision” journey complex and challenging. This can now be transformed by integrating Google Gemini’s analytical capabilities with Google Agentspace’s orchestration. This integrated approach also empowers enterprises to adopt a proactive and intelligent approach instead of a reactive approach.

As Google’s AI technology partner, Onix has successfully delivered customized solutions in data analytics for the benefit of its global customers. We believe that the future of data analysis will be intelligent, accessible, and automated.

Are you ready to embrace the GenAI-powered business transformation? Contact us today.