INBOX INSIGHTS, October 1, 2025: AI Data Quality, Dealing With Information Overload

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INBOX INSIGHTS: AI Data Quality, Dealing With Information Overload (2025-10-01)

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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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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how user data quality fundamentally controls social media algorithms, including the new LinkedIn LLM system.

You will learn how modern social algorithms use language models to predict your engagement, shifting the control back to you. You will discover why your likes, comments, and shares act as critical input data that constantly trains the underlying system. You will understand how to implement a personal data stewardship strategy to ensure you see the content you value most. You will stop feeding the negative engagement cycle by recognizing how algorithms treat all interactions equally. Watch the full episode now to take control of your social media feed!

Watch/listen to this episode of In-Ear Insights here »

Last time on So What? The Marketing Analytics and Insights Livestream, we took a surprising tour through the complex world of Microsoft Copilot. Catch the episode replay here!

This week on So What?, we’ll be investigating whether your data is AI-Ready or not. Are you following our YouTube channel? If not, click/tap here to follow us!

Here’s some of our content from recent days that you might have missed. If you read something and enjoy it, please share it with a friend or colleague!

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In this week’s Data Diaries, let’s talk about dealing with content overwhelm. With the advent of generative AI, more people and more companies are publishing more content than ever before.

How do we decide what content to consume? How do we keep up?

I’ll share the methodology that works for me. Your mileage may vary. Long ago, Google had a set of three measures by which they judged and routed search queries – QDR, QDD, and QDF. These obscure acronyms stood for query deserves relevance, query deserves diversity, and query deserves freshness.

Google used this tripartite algorithm to judge what results to return back in the day, more than a decade ago. It was supplanted by EAT (expertise, authority, and trust) and then by AI not too long after. But this old rubric can serve us well even today, sort of a mini search algorithm in our brains.

Given any piece of content that someone wants us to pay attention to, is it:

Relevant? If it doesn’t help us move the ball down the field, then no matter how good it is, it should be lower priority.
Diverse? In Google’s algorithm, diversity referred specifically to format. If the content isn’t available in our preferred format – text, image, audio, video, interactive – then it should be lower priority.
Fresh? Stale data is like stale coffee beans. If you have nothing else, it will get you by, but fresh is definitely better. Stale content should be lower priority and really stale content should just get composted.

This first pass can winnow down the tsunami of content into a river. But if you’re trying to drink a river, it still might be a frustrating and overwhelming experience.

That’s where AI comes in. You can tackle this in simple ways, like taking all your favorite data sources and putting them into a service like NotebookLM so you can ask super relevant, hyper specific questions of your sources that precisely answer your most burning questions.

You could use Deep Research tools with a prompt like this to programmatically collect the data you want and assemble a briefing daily, weekly, or whenever you want it:

Put together a news briefing for me of the latest news in (topic). The only allowed sources are (your list of trusted sources). No other sources are permitted. The timeframe is content published for the first time after (date). Do not use republished content from before that date. Do not use content published before that date. Rank order the content from most relevant to me to least relevant to me by (your criteria and needs). Return a minimum of 10 results and a maximum of 50 results. Present your results in Markdown format following this outline:

Article Title

Summary: (1 sentence summary of the article’s most useful insight)

Publication Date: (yyyy-mm-dd format)

Relevance Explanation: (1 sentence explanation of why the article is relevant to me)

Relevance Score: (integer 0-10, where 10 is most relevant to my needs)

Source: (full article URL)

You could even go so far as to building your own AI agent with AI that programmatically reads and scores all the content in your inboxes and highlights which you should pay attention to first. That’s what we did with our automated weekly AI newsletter, which uses basically the same prompt as above but programmatically, processing thousands of articles a week.

Keeping up with change can be hard, but using today’s tools and technologies can make it more manageable. The bottom line is that you have no shortage of tools and capabilities that can distill the flood of news on any topic down to what you need, in the way that works best for you.

Almost every AI course is the same, conceptually. They show you how to prompt, how to set things up – the cooking equivalents of how to use a blender or how to cook a dish. These are foundation skills, and while they’re good and important, you know what’s missing from all of them? How to run a restaurant successfully. That’s the big miss. We’re so focused on the how that we completely lose sight of the why and the what.

This is why our new course, the AI-Ready Strategist, is different. It’s not a collection of prompting techniques or a set of recipes; it’s about why we do things with AI. AI strategy has nothing to do with prompting or the shiny object of the day — it has everything to do with extracting value from AI and avoiding preventable disasters. This course is for everyone in a decision-making capacity because it answers the questions almost every AI hype artist ignores: Why are you even considering AI in the first place? What will you do with it? If your AI strategy is the equivalent of obsessing over blenders while your steakhouse goes out of business, this is the course to get you back on course.

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Where can you find Trust Insights face-to-face?

SMPS, Denver, October 2025
MAICON, Cleveland, October 2025
Generative AI for B2B Marketers, London, October 2025
MarketingProfs B2B Forum, Boston, November 2025
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Imagine a world where your marketing strategies are supercharged by the most cutting-edge technology available – Generative AI. Generative AI has the potential to save you incredible amounts of time and money, and you have the opportunity to be at the forefront. Get up to speed on using generative AI in your business in a thoughtful way with our workshop offering, Generative AI for Marketers.

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