For a local craft brewery with 100 dedicated employees, the decision to expand the product line or distribution area is often a mix of gut feeling, market buzz, and the brewer’s intuition. But in today’s competitive landscape, data is the most potent secret ingredient. By adopting a modern data strategy, even a medium-sized brewery can move from guessing to knowing.
This blog will use the PMI’s Cognitive Project Management for AI (CPMAI) framework—a structured approach for data-driven projects—to outline how your brewery can use powerful data tools like Databricks and its competitor, Snowflake, to make smarter expansion decisions.
️ The Data Tools: Databricks vs. Snowflake
Both Databricks and Snowflake are leading cloud-based data platforms that help businesses collect, store, process, and analyze massive amounts of data in a unified way. They are ideal for a brewery looking to scale its analytics capabilities.
- Databricks: Built on the foundation of Apache Spark, it excels as a unified data and AI platform (often called a “Lakehouse”). It’s fantastic for data engineering, real-time analytics, and is particularly powerful for complex tasks like building Machine Learning (ML) models—for example, predicting which new beer styles will be most popular.
- Snowflake: Known as a powerful, high-performance cloud data warehouse, it focuses on speed, simplicity, and concurrency for SQL-based analytics (standard database querying). It’s incredibly easy for business users to quickly run reports and analyze structured sales, inventory, and customer data.
For a craft brewery, Databricks would be the long-term choice for advanced predictive modeling (like demand forecasting), while Snowflake offers a simpler, faster route to essential business reporting and analytics. We will use the Databricks/Snowflake comparison to highlight the flexibility of a modern data strategy.
The Framework: CPMAI for Business Expansion
The CPMAI framework provides six structured phases to manage data and AI projects, ensuring they are aligned with clear business objectives. Here is how your craft brewery can apply it to the critical question of expansion: Should we launch a new sour beer and distribute it to three new cities?
Phase 1: Business Understanding
Goal: Clearly define the business problem and the criteria for success.
- Define the Problem: The core question is: Which new product (a new Sour IPA) and which new markets (City A, B, or C) will provide the highest return on investment (ROI) within the next 18 months?
- Success Metrics (KPIs): Define what success looks like. This could include a 15% increase in total revenue, a 10% increase in market share in the new cities, or a 5% reduction in production waste.
- Initial Data Scope: Identify the business areas that hold the data needed to answer the question: sales, inventory, production, and customer feedback.
Phase 2: Data Understanding
Goal: Locate, assess, and characterize the quality and relevance of the data.
- Source Inventory: Where is your data currently stored?
- Brewing Logs: Temperature, gravity, pH readings (often in spreadsheets or a small production management system like Beer30).
- Sales Data: POS systems, distributor reports, e-commerce platform.
- Customer Feedback: Social media, taproom comments, online reviews.
- External Data: Competitor pricing, regional demographics, weather data (for demand correlation).
- Data Health Check: You’ll likely find data silos (data is stored in separate, incompatible places) and inconsistent formats. This phase confirms the raw ingredients you have to work with.
Phase 3: Data Preparation 識
Goal: Clean, transform, and structure the data into a usable format for analysis.
This is where Databricks and/or Snowflake become essential. Your brewery will build a Data Lakehouse—a single, centralized repository for all your data.
- Ingestion: You use the platform’s tools to automatically ingest all the raw data (spreadsheets, logs, POS exports, etc.) into the data lakehouse.
- Cleaning & Transformation: This is the most time-consuming step. You use Databricks’ Spark engine (or Snowflake’s powerful SQL capabilities) to standardize data.
- Example: Ensuring all beer names are consistent (e.g., “Pale Ale” vs. “PALE ALE”) and that all sales data links correctly to inventory data.
- Feature Engineering: You create new, powerful metrics from the raw data.
- Example: Calculating “Days of Inventory on Hand” by distributor or “Average Customer Rating per Beer Style.”
Phase 4: Model Development
Goal: Use the prepared data to build analytical models that provide the crucial expansion insights.
- Analysis & Visualization (Snowflake/Databricks SQL):
- Run queries on the structured sales data to identify your top 5 existing beer styles by revenue and gross margin.
- Analyze customer review text (using basic Natural Language Processing, easily run in Databricks) to see if local customers are frequently asking for sour beers.
- Create dashboards (using tools like Tableau or Power BI connected to Snowflake/Databricks) showing current sales performance in potential new markets (City A, B, C) based on current, limited distribution.
- Demand Forecasting (Databricks/ML):
- Leverage Databricks’ integrated Machine Learning (ML) tools to build a sophisticated predictive model.
- This model uses historical sales, seasonality (holidays, summer months), local events, and even weather data to forecast the predicted demand for the new Sour IPA in each of the three new cities. This is the game-changer insight.
Phase 5: Model Evaluation ⚖️
Goal: Validate the analysis and models against business reality before making a commitment.
- Model Accuracy: For the demand forecast (the ML model from Databricks), the data team tests the model on past data to ensure its predictions are accurate and reliable. A bad forecast leads to expensive mistakes (e.g., over-brewing and waste, or under-brewing and lost sales).
- Business Review: The leadership team reviews the visualizations and forecasts.
- Finding: The analysis shows sour beers are only a top-requested item in City A, but the predicted high-volume demand in City C still makes City C the most profitable expansion target. This challenges the initial gut feeling but is backed by data.
- Scenario Testing: Use the data platform to quickly model different scenarios: What if the cost of grain increases by 5%? What if the new beer only gets a 3.5-star rating? This helps build confidence in the decision.
Phase 6: Model Operationalization and Monitoring
Goal: Deploy the solution to drive business value and continuously monitor its performance.
- Deployment: The brewery decides to expand distribution of the new Sour IPA into City C.
- Actionable Insights: The model’s forecast is integrated directly into the production schedule to ensure the right amount of beer is brewed—no waste, no stock-outs.
- Continuous Monitoring: The same Databricks/Snowflake platform is now used to continuously track the actual sales in City C against the initial forecasted sales.
- If sales are low: The system can automatically flag this, prompting the sales team to investigate a distributor issue.
- If sales are higher: The system alerts the production team to increase the batch size immediately, maximizing revenue.
This end-to-end data process, structured by the CPMAI framework and powered by tools like Databricks and Snowflake, transforms a risky business decision into a calculated, measurable expansion strategy. For the craft brewery, data isn’t just about reporting what happened; it’s about predicting the future and brewing success.
CA Project Intelligence 