“If all you have is a hammer, everything looks like a nail.”

This old adage perfectly describes how many data teams approach their work today – with dashboards as their hammer, every business problem becomes a dashboard-shaped nail.

I personally experienced how this approach can hold back both data teams and the businesses they serve.

That's why this episode shows you how to become indispensable to your organization and protect your data team during downsizing events.

The Cost of Complacency

The harsh truth? Many data teams are stuck in a self-imposed BI prison, limiting their potential impact and relegating themselves to reactive service desks.

While building data warehouses and dashboards is crucial foundational work, stopping there means missing out on the most valuable opportunities to drive business outcomes.

Let’s explore how data teams can break free from this pattern and evolve their analytical capabilities to create genuine business impact.

The Analytics Evolution Framework

The journey of analytical maturity can be mapped on two axis: effort and impact. Understanding where your team stands – and where it needs to go – is crucial for maximizing your value to the business.

Stage 1: What Happened? (Descriptive Analytics)

→ Low effort, low impact

→ Primary tool: Dashboards

→ Purpose: Surface patterns and raise questions

🚨 Warning: Many teams get stuck here, treating dashboards as the end goal rather than the starting point

Stage 2: Why Did It Happen? (Diagnostic Analytics)

→ Medium effort, medium impact

→ A common mistake here is to use dashboards as a means to answer business questions. The purpose of a dashboard is to raise questions and not to answer them!

→ Better approach: My personal favorite approach to cover this step in the analytical evolution is to enable business decision makers (e.g. marketing managers, product managers, finance managers) to access a BigQuery Data Warehouse via the native Google Sheets connection and answer business questions with tooling they are familiar with and they enjoy using.

Stage 3: What Will Happen? (Predictive Analytics)

→ Higher effort, higher impact

→ Tools:

• Google Sheets/Excel for simple modeling (often underutilized!)
• Statistical models/ML Models
• Pro tip: Start with simple heuristics before jumping to complex ML models
• Remember: An SQL query or spreadsheet model often gets you 80% of the way there

Stage 4: What Should We Do? (Prescriptive Analytics)

→ Highest effort, highest impact

→ This is where true transformation happens and where data teams have the biggest lever to create business value.

→ Three key approaches to driving action:

Large Language Models (LLMs)

Examples:

• Automating customer service responses
• Scaling content creation for marketing campaigns

Automated Decision Systems

Examples:

• Triggering personalized email campaigns by sending churn prediction scores to the email tool
• Optimizing ad spend by sending predicted customer lifetime values to Google Ads

Experimentation

• This is a highly underutilized "hack" how data teams can create business value that is very direct and easy to measure. So many data teams don't want anything to do with this topic. This is a huge wasted opportunity!

Real-world example: In a recent conversation with Tris Burns , former Head of Data at Pizza Hut Digital Ventures, he shared a compelling case study. Their team identified a friction point in the traditional pizza ordering flow: customers were forced through a customization process, even when they didn’t want to modify their pizza. The data team ran an experiment with a streamlined checkout that allowed customers to skip customization. The results were remarkable – the simplified flow generated millions in incremental revenue. This perfectly illustrates how data teams can create tangible business value through smart experimentation.

Where to do what by whom

The chart below shows how different stages of analytics require different skills and occur in different layers of your data infrastructure.