AI for Data Analysis: How Small Businesses Can Make Smarter Decisions
Chapter 10 of AI for Small Business opens with Pallen admitting something refreshing. He says he’s not a data person. He doesn’t love spreadsheets or calculations. But thanks to AI, that’s perfectly fine. You don’t need a degree in data science to understand your business data anymore.
That’s a message a lot of small business owners need to hear.
Data Without the Data Scientist
The chapter centers on a real client story. Dana Benjamin runs Back of the Napkin Consulting, a firm that helps mission-driven organizations use data for positive impact. Her main clients are Boys & Girls Clubs across America.
Dana’s work involves tracking everything: where money comes from, member demographics, program participation rates, donor retention, volunteer hours, fundraising patterns, and more. Before AI, her team would spend months just organizing and collecting data. By the time they finished, they were too exhausted to do deep analysis.
With AI, three critical steps get handed off to the machines: organization, collection, and interpretation. Dana’s team can skip straight to what actually matters: making strategic decisions.
This is the pattern Pallen keeps coming back to throughout the book. AI handles the grunt work. Humans handle the thinking.
Platforms for Analysis
Pallen recommends several tools for data work:
Sisense combines data visualization and predictive analytics. Companies like Skullcandy and Nasdaq use it. Looker integrates with Google Cloud and offers customized dashboards. People.ai focuses on revenue intelligence for sales and marketing teams. Qualtrics XM measures customer and employee satisfaction while forecasting behaviors. And Salesforce Nonprofit Cloud is built specifically for managing fundraising, grants, and volunteer data.
Each tool serves a slightly different purpose, but they all share one thing in common: they make complex data accessible to people who aren’t data experts.
Big Data Is Not Just for Big Companies
Pallen addresses something important here. Small businesses generate way more data than they realize. Customer transactions, social media analytics, website traffic, email conversations, sensor data, geolocation data. It adds up fast.
The problem isn’t collecting data. It’s doing something useful with it. Most small businesses sit on mountains of information and never touch it because it feels too complicated. AI removes that barrier.
He suggests using ChatGPT or similar tools to brainstorm what big data your business actually collects. The chapter includes an example of a social worker who uses this approach to map out all the data her therapy practice generates, from patient demographics to session notes to feedback forms. It’s a simple exercise, but it can reveal opportunities you didn’t know you had.
Cleaning Up Your Data
One section that doesn’t get enough attention in most AI books: data quality. Pallen points out that having data is worthless if the data is wrong. In healthcare, a single data error in a patient record could lead to misdiagnosis. In research, bad data can invalidate entire studies.
Tools like Alteryx Designer Cloud automate the process of finding and fixing errors in datasets. It works across industries: retail, finance, manufacturing, government, telecom. The point is that you need clean data before AI analysis can be trusted.
He also covers data merging with Talend. Most businesses keep data in separate silos: online sales in one place, in-store purchases in another, social media in a third. Talend connects these sources into a unified view. Without it, you’re looking at fragments. With it, you see the full picture.
Seeing the Data
My favorite part of this chapter is the data visualization section. Pallen uses another client example. Mark Maynard is a business consultant who was hired to figure out why a farm-to-table restaurant chain had wildly fluctuating profit margins despite high sales.
Using Microsoft Power BI, Mark created visualizations showing food cost fluctuations, menu item profitability, overstaffing during slow periods, and utility costs outside peak hours. When the CEO could actually see the data in charts and graphs, the decisions practically made themselves: cut unprofitable dishes, adjust prices quarterly, renegotiate with suppliers, fix the staffing model.
That’s the power of visualization. Raw numbers in a spreadsheet can be paralyzing. A clear chart showing where you’re losing money makes the next step obvious.
Even Your Spreadsheets Have AI Now
If you’re not ready to jump to dedicated AI platforms, Pallen reassures you that Excel and Google Sheets already have built-in AI features. Excel’s “Analyze Data” feature can spot trends and run predictive analytics on your existing spreadsheets. Google Sheets has auto-fill features and data cleanup tools. For bigger datasets, Google’s Connected Sheets feature lets you tap into BigQuery’s machine learning without writing SQL.
This is practical advice. Not everyone is ready to adopt a whole new platform. Sometimes the best first step is using what you already have.
My Take
Chapter 10 is essentially Pallen saying: you’re sitting on a goldmine of data and you probably don’t even know it. AI makes that data accessible, understandable, and actionable.
The closing line of the chapter stuck with me. Pallen says we’re in the early stages of AI as a decision-making tool. The sooner you adopt, the sooner you can adapt when the technology evolves. That’s not hype. It’s just practical thinking.
If you’ve been making business decisions based on gut feelings or incomplete information, this chapter gives you a clear path to doing it better. The tools exist. The data exists. AI just connects the dots.
Book Details:
- Title: AI for Small Business
- Author: Phil Pallen
- ISBN: 978-1-5072-2291-1
- Publisher: Adams Media (Simon & Schuster)
- Published: January 2025
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