This post was originally featured in the October 1st, 2025 newsletter found here: INBOX INSIGHTS, October 1, 2025: AI Data Quality, Dealing With Information Overload
Why Your Annual Plans Are Only as Good as Your Data
I’ll never forget the planning meeting where everything fell apart. (Ok, so not just one meeting, but stay with me.)
We were three hours into our annual planning session—you know the kind, where everyone’s crammed into a conference room with laptops, spreadsheets, and increasingly cold coffee—when someone asked what should have been a simple question: “What was our actual conversion rate last quarter?”
Silence.
Then, chaos. The marketing team pulled up one number. Sales had a different number. Product had yet another number. And here’s the kicker: we were all looking at data that was supposedly from the same source.
That meeting ended with no decisions made, a lot of frustration, and a very expensive realization: we had been making plans based on data we couldn’t trust. And if we couldn’t trust our historical data, how on earth were we supposed to make confident predictions about the future?
The Uncomfortable Truth About Data Quality
Here’s what I’ve learned after years of working with organizations of all sizes: most companies are making critical business decisions based on fundamentally flawed data, and they don’t even know it.
It’s not that people are careless. It’s that data quality issues are sneaky. They accumulate over time, like dust bunnies under the couch. One missing field here, one inconsistent naming convention there, a few duplicates that no one bothered to clean up. Before you know it, you’re sitting in that planning meeting with three different versions of the truth and no way to know which one is actually true.
And when it comes to AI? That’s where things get really interesting (and by interesting, I mean potentially disastrous).
What Actually Happens When Your Data Is a Mess
Let me get specific here, because “bad data quality” sounds abstract until it hits you in the face with real consequences:
In Annual Planning:
- Your forecasts are based on incomplete or incorrect historical data, so you’re essentially guessing
- Different departments can’t align on shared goals because they’re working from different “truths”
- You waste hours (or days) in meetings trying to reconcile conflicting numbers instead of actually planning
- Leadership loses confidence in the team’s ability to execute because the foundation is shaky
In AI Proof of Concepts:
- Your AI model learns from flawed data and produces flawed results (garbage in, garbage out is still very much a thing)
- You can’t tell if the AI isn’t working because the model is bad or because the data is bad
- You waste budget on AI tools that never had a chance to succeed
- Your team gets demoralized when the “exciting new AI project” fails, even though it was doomed from the start
In Day-to-Day Business:
- Your team wastes time manually cleaning and reconciling data instead of doing their actual jobs
- You can’t answer basic questions about your business with confidence
- Customer experiences suffer because your data about them is incomplete or wrong
- You miss opportunities because you can’t trust the signals you’re seeing
The really painful part? These issues compound. Bad data leads to bad decisions, which lead to more bad data, which lead to worse decisions. It’s a vicious cycle.
Why “We’ll Fix It Later” Doesn’t Work
I get it. Data quality work isn’t sexy. It’s not the exciting new AI tool or the flashy dashboard that everyone wants to talk about in the leadership meeting. It’s the digital equivalent of cleaning out the garage—necessary but not exactly thrilling.
But here’s the thing: you can’t build on a broken foundation.
Think about it this way. If you were building a house, would you:
- Start with a solid foundation and then build up, or
- Build the whole house and then try to fix the foundation later?
Obviously, you’d start with the foundation. Yet in business, we do the equivalent of option 2 all the time. We invest in expensive tools, hire specialized talent, and launch ambitious initiatives—all while ignoring the fact that our foundational data is crumbling beneath us.
The Case for Starting with an Assessment
So what’s the answer? Do you need to stop everything and spend six months cleaning data before you can do anything else?
No. (Thank goodness, because no one has six months to spare.)
What you need is to understand where you actually stand. And that’s where a proper data quality assessment comes in.
Think of it like going to the doctor for a check-up. You don’t need to cure every ailment before you walk in the door. You need a diagnosis. You need someone to tell you what’s working, what’s not, and what needs attention first.
A good data quality audit will:
- Give you an honest assessment of your current state (not a sugar-coated “everything’s fine” report)
- Identify the specific issues that are causing the most pain or risk
- Prioritize what needs to be fixed first (because you can’t fix everything at once)
- Provide a clear, actionable roadmap for improvement
- Give you the ammunition you need to get buy-in and resources for the necessary work
What Makes a Data Quality Assessment Actually Useful
Not all assessments are created equal. I’ve seen plenty of reports that are essentially 100 pages of charts that tell you “your data has some issues” without actually helping you understand what to do about it.
A useful assessment needs to:
Be comprehensive. It should look at your data from multiple angles—not just “Is it clean?” but also “Is it complete? Is it actually measuring what you think it is measuring? Is it in a format you can use?”
Be specific. Instead of vague statements like “data quality could be improved,” you need concrete examples: “The ‘lead source’ field is blank in 37% of records” or “There are 14 different ways that ‘Trust Insights’ is spelled in your CRM.”
Be actionable. You should walk away knowing exactly what needs to happen next, not just that “something should probably be done.”
Be realistic. Not every data quality issue needs to be fixed immediately. A good assessment helps you understand which issues are blockers and which ones you can live with (at least for now).
Here’s the Real Talk
Your annual plans are only as reliable as the data they’re based on. Your AI initiatives are only as effective as the data they learn from. You don’t need perfect data to move forward—but you do need to know where you stand and have a plan to get better.
So before you dive into planning season or kick off that exciting AI project, check the foundation. Start with an honest assessment, be realistic about what you find, and prioritize fixing what actually matters for your goals. Get help if you need it (this is literally what we do at Trust Insights with our AI-Ready Data Quality Audit).
You’ll save yourself a lot of headaches and a lot of awkward meetings where no one can agree on the numbers.
Trust me on this one. I’ve been in that conference room with the cold coffee and the conflicting spreadsheets. It’s not where you want to be.
Ready to find out where your data actually stands? Our AI-Ready Data Quality Audit gives you a clear, honest assessment of your data health and a prioritized roadmap for improvement. Learn more at trustinsights.ai/expertise/services/ai-ready-data-quality-audit/ or shoot me a message—I’m happy to talk through whether this is the right fit for what you need.
How confident are you in your data quality? Reply to this email or join our free Slack group, Analytics for Marketers.
– Katie Robbert, CEO
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Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.
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