How to Create Your Own Data Lake: A Practical Guide for Sales and Revenue Leaders

How to Create Your Own Data Lake: A Practical Guide for Sales and Revenue Leaders (with Playbook and Workflow)

As a sales &/or revenue leader, you manage vast data across various accounts, sales pipeline stages, and buyer behaviors. The challenge is that this data often sits in silos—scattered across CRMs, spreadsheets, email threads, or worse, only in your head. To truly harness the value of this data, you need to create a data lake (puddle or pond): a centralized repository where all of this data is stored and structured, allowing for deep analysis and actionable insights.

A data lake is like a giant, accessible pool of data: it starts with small amounts of data (a puddle), grows into something more structured (a pond), and eventually scales into a vast lake where different parts, or “coves,” represent different segments of your data in the sales cycle. For sales, these coves could correspond to various stages of your pipeline, customer behaviors, and other important factors.

In this article, we’ll walk you through building a data lake for your sales organization, how to use it for better analysis, and how AI-driven tools like ChatGPT can take your sales performance to the next level. At the end, we provide a playbook and workflow to ensure this becomes a systematic part of your sales operations.

Why Build a Data Lake for Your Sales Pipeline?

A data lake allows you to:

• Gain a 360-degree view of your pipeline.
• Identify patterns, correlations, and trends across your deals.
• Leverage AI-driven insights for better forecasting.
• Optimize your pipeline and uncover new opportunities.
• Create a repeatable process for analyzing and updating sales data.

The key benefit of a data lake is that it consolidates both structured data (from CRMs) and unstructured knowledge (in your head or informal notes), creating a complete dataset that powers better decision-making.

Steps to Build Your Sales Data Lake

Step 1: Start Small — List Your Current Accounts

Think of this step as creating the “puddle” of your data lake. Start by gathering the data on all the accounts in your pipeline, following a logical sequence:

1. Closed (won) accounts.
2. Near-close accounts that are just steps away from converting.
3. Stalled accounts where there’s been little activity.
4. Recent demo accounts where prospects have been shown your product.
5. Upcoming demo accounts scheduled for presentations.
6. Pre-demo accounts, or leads that are early in the pipeline.

For each account, compile:

• Contact name and details.
• Title, industry, and current pipeline stage.
• All relevant notes, including personal insights or feedback, are in the CRM, and a good bit is saved in your mind that would surface when you told the story/update of the account in a sales team pipeline review meeting or QBR. 

At this point, it’s important to use everything you know—from formal records to memory—about these accounts.

Step 2: Organize Data by Pipeline Stage (Create “Coves”)

As you expand your puddle into a pond, consider each sales pipeline stage a separate cove within your data lake. Create separate documents (or files) for each stage, detailing the accounts in that phase.

For each account, gather:

• All notes on the contact, including what you know from personal recollection, previous emails, calls, or meetings.
• Notes on the company’s challenges, priorities, and anything relevant to the deal.
• Insights could be crucial to closing the deal, even if they aren’t formally recorded.

The idea is to go beyond the CRM to capture everything stored in your memory. Once organized, you’ll see how all this data builds a rich, analyzable picture of your pipeline.

Step 3: Analyze the Data Using ChatGPT

With the data organized, the next step is to analyze it using an AI tool like ChatGPT to uncover patterns and actionable insights.

Step 3.1: Organize the Data
Copy and paste all the accounts and contacts into ChatGPT. Use the following prompt to formalize the structure:

“Please create a detailed account review of the following contacts and accounts.”

This will ensure the data is structured uniformly, making it easier to compare and analyze.

Step 3.2: Uncover Trends and Insights
Next, AI will analyze the data for synergies and trends. Prompt ChatGPT with:

“Please identify correlations, trends, and potential probabilities for closed-won outcomes for each account listed.”

This will give you insight into how accounts are progressing through the pipeline, common factors that lead to closed deals, and potential strategies for accounts at risk.

Step 3.3: Generate an Executive Summary
To make the data more digestible, prompt ChatGPT to create an executive summary:

“Please generate an executive summary of the account review, identifying key trends and five major takeaways.”

This summary will provide a high-level overview of the analysis, which is ideal for sharing with your team or stakeholders.

Step 4: Create a Weekly Process for Ongoing Analysis

Your data lake is not static. The value comes from regularly updating and reviewing it. Here’s how you can maintain and refine the lake over time:

1. Once you have an organized document with all your accounts and analyses, title it with the current date (e.g., “Pipeline Review – October 2024”).
2. Make a copy of the document to use as a template for future reviews. Repeat the process weekly or bi-weekly, ensuring the data is continuously updated.
3. Continue to feed new insights and account updates into the data lake.

Step 5: Combine Reports for a Full Pipeline View

After several weeks of analysis, combine your documents from each pipeline stage into one master document. This master report will give you a comprehensive view of your entire funnel.

Step 6: Repeat for Other Sales Funnel Stages

Repeat this process for every stage of your sales funnel, ensuring you cover all accounts in the pipeline. Eventually, you’ll have a holistic data lake that gives you unprecedented visibility into your sales operations.

Playbook and Workflow for Building Your Sales Data Lake

Now that you understand the process, here’s a playbook to guide you and your team through building and maintaining your data lake.

Playbook for Building a Sales Data Lake

Objective: Create a central repository (data lake) to store and analyze all sales pipeline data, using AI-driven insights to optimize performance.

1. Gather Data: List all accounts across key pipeline stages (Closed, Near-Close, Stalled, Demo, Pre-Demo).
2. Organize by Stage: Create separate files or documents for each pipeline stage (coves), detailing each account’s history, contacts, insights, and status.
3. Process with AI: Use ChatGPT or another AI tool to analyze trends, correlations, and potential outcomes for each account.
4. Generate Executive Summary: Create a high-level summary of your findings for easy reporting.
5. Review Regularly: Update your data lake weekly or bi-weekly to keep your analysis current.
6. Master Report: Consolidate weekly reports into one comprehensive document that covers your entire sales funnel.

Workflow to Execute

1. Preparation:
   • Gather CRM data, personal notes, and insights on each account.
   • Organize the data into stages and ensure completeness.
2. Document Creation:
   • Create individual documents for each pipeline stage (coves).
   • Use dictation or notes to fill in additional details beyond the CRM.
3. ChatGPT Process:
   • Feed the documents into ChatGPT using the provided prompts.
   • Review the AI-generated outputs, refining them where needed.
4. Weekly Updates:
   • Make copies of the data lake document weekly, adjusting it based on new information.
   • Run new analyses through ChatGPT to keep insights fresh.
5. Team Involvement:
   • Bring in the extended sales team to contribute to account information and ensure all data is captured.
   • Use the master report to drive weekly sales meetings.
6. Master Report:
   • After several iterations, create a master report covering the entire sales funnel.
   • Use this report to refine sales strategies, inform leadership, and optimize performance.

Conclusion: Transforming Your Sales Pipeline with a Data Lake

Building a data lake for your sales pipeline can dramatically improve how you manage and analyze your accounts. By consolidating all your data—from formal sources like CRMs and your team’s institutional knowledge—you create a robust foundation for AI-driven insights. This data lake will enable you to optimize your sales strategy, identify high-potential deals, and unlock opportunities that would otherwise remain hidden.

With the playbook and workflow provided, you now have a systematic approach to building, maintaining, and using your data lake. This will accelerate your team’s decision-making and help drive more closed deals.

Now, it’s time to stop thinking and start doing. Build your sales data lake today, and see the impact it can make on your revenue.

If you need help getting started, feel free to contact me—I’m more than happy to guide you through the process!

Tim Savage

Founder

Net New Solutions

https://www.linkedin.com/in/timothywsavage/