Despite the name, We Dig Data isn’t about data. Because the real goal of data is to answer questions, make better decisions, and build momentum on the things that matter most.
So we’re doing something a little different this week.
With so many new readers (welcome and thank you!), we wanted to share some thoughts on what We Dig Data is about and make it easier to find the information that will help with what you’re working on right now.
Think of this as your quick guide to We Dig Data.
What do you want to do with that data?
We Dig Data readers are people doing real work with limited time, money, and resources. Whether they are:
lean teams or individuals in businesses/non-profits or
data professionals wanting to bridge a gap with their partners
Our readers believe data will help them do their work better, but they have questions about how to practically (and efficiently) accomplish that.
Here, you can expect to find real-world lessons, frameworks, tools, and examples to help you:
#1. Answer questions. See what’s really happening.
You’re looking at a dashboard, forecast, AI-generated answer, or analysis and wondering: Is this the right data?
Make sense of the numbers in front of you. Ask better questions, figure out what information you can trust, spot patterns and problems, and avoid conclusions the data doesn’t actually support.
Is That the Right Information for the Job? — practical steps to help you decide what information to trust and what to skip.
One Hour. One Dashboard. Still Spinning. — this method teaches you how to approach a dashboard for productive analysis (and not just clicking around).
How We Unlocked Our Substack Performance — the dashboard wasn’t enough so we pulled the data and built our own system. Here’s what we did.
#2. Making better decisions, especially when it feels too subjective.
Everyone has an opinion. You need a way to understand what’s really happening and make a more informed call before committing scarce time, money, or people.
Use the information you have to compare options, test assumptions, and make a more informed call, while recognizing when more analysis won’t make things clearer.
Numbers Don’t Speak for Themselves — questions for the people explaining the numbers and the people making decisions with them.
How to Use Data to Make Decisions — how you turn interesting information into meaningful action.
Metrics That Seem Obvious (But Aren’t) — the conversations every team should have about their numbers.
#3. Advance priorities more quickly.
You are trying to build momentum in a key area, but simply doing MORE isn’t sustainable. You need to know what’s working so you can go further, faster.
Figure out what to measure, track whether you’re making progress, adjust, experiment, and focus your time and resources where they’re having the greatest impact.
Build a Data Feedback Loop to Accelerate Growth — choose what to measure and use that data to grow better, faster than peers and competitors.
Why Winning Teams Focus on a Few Key Metrics — the scoreboard your team is missing and how to build one.
#4. Make a compelling proposal or business case.
Strong data can shift the conversation from who has the loudest opinion to what the evidence is telling us.
Use credible data and information to explain what you’re seeing and why it matters, whether you are proposing a change, recommending an investment, or trying to get support for an idea.
The Numbers Behind Good Business Plans — credible data make or break investment decisions. How to better estimate opportunity and financials.
The Opinions Behind Every Forecast — what a hundred financial forecasts taught me about numbers, trust, and the room where they get decided.
#5. Decide when to use AI or other technology for data work.
AI. Automation. Dashboards. New platforms. All can be useful. But none automatically fixes a fuzzy problem, messy information, or broken process.
Make better choices when applying technology, AI, and automation to your data, especially when you don’t have a giant team or budget.
Before Investing in Automation, Focus on Your Process - don’t automate confusion. Fix your process first.
AI at Work: When to Trust, Adapt, or Toss AI Outputs — add this step when adding AI to your data workflow.
We share the lessons we’ve learned from the bumps and bruises collected along the way, so you don’t have to make the same ones. We’ve trusted the wrong information. Misread things. Built things that weren’t useful. Overcomplicated problems. And watched technology change what was possible again and again.
That’s part of the reality of learning to use data and information well.
For us, the real measure of our success isn’t whether you learned something interesting about data. It’s whether something you care about got better because of what you learned here.
About us
Hi - we’re Taylor and Rachel!
We started We Dig Data to help individuals and lean teams connect the business question, the information, and the technology — without making the solution bigger or more complicated than it needs to be.
Taylor has 20+ years of building products, businesses, teams, and growth initiatives — using information to decide where to focus, make the case for investment, and move work forward.
Rachel has 20+ years working across data, technology, products and operations, bridging technical and business worlds. She brings a systems approach on how people, processes, information, and technology can fit together.
Together, our complementary skills and experience enables us to bring together:
the business question +
information that can help answer it +
technology (where it’s needed) +
the simplest, practical path forward
Some things we’ve learned
In working with data, technology, businesses, products, and the people trying to make all of those things work together, we’ve developed a few strong opinions over the years.
Data is a means to an end. We don’t use data because there is something inherently thrilling about a spreadsheet. We use it because it can help us understand what’s happening, make better decisions and figure out what to do next.
You don’t have to be a ‘numbers person.’ Knowing what questions to ask, what to challenge and how to work with partners can matter more than knowing every formula and tool yourself.
More data isn’t always the answer. Sometimes you already have enough information to make the decision. More analysis can create the illusion of certainty without actually making the choice any clearer.
Technology can’t fix the wrong problem. AI, automation, dashboards, and new tools can all be useful. But first you need to understand what you’re trying to accomplish and what’s getting in the way.
Useful beats impressive. The right solution might be a sophisticated model. It might also be a spreadsheet, a better process, or one number everyone finally agrees how to calculate.
That’s why we look at the whole problem: the question you’re trying to answer, the information that can help, the people and processes involved, the technology that may — or may not — be useful, and what you actually do next.
Because for us, the real measure of whether We Dig Data is successful isn’t whether you learned something interesting about data. It’s that something you care about got better because of what you learned here.
That’s why we dig data.
Work with us
Sometimes an article is enough. Sometimes you need another set of experienced eyes on the problem.
We work directly with teams who want help figuring out what to measure, what the information is telling them, where technology can help, and how to turn what they learn into action - with a focus on practical solutions that move important work forward.