INBOX INSIGHTS, February 2, 2025: Finding Clarity, Dealing with Missing Data - Trust Insights Marketing Analytics Consulting

INBOX INSIGHTS: Finding Clarity, Dealing with Missing Data (2025-02-05) :: View in browser

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Finding Clarity When Everything Feels Overwhelming

I’ve been waking up with a sense of dread every day for a few weeks. I know I’m not alone. A lot of us are struggling to find purpose and put one foot in front of the other.

I’ve written about finding your way through hard times before. But right now, as we’re all trying to balance well-being with business growth, it feels like the right time to revisit how we can use structure to find clarity. I’ve said it before and I’ll say it again. New tech doesn’t solve old problems. And being human is the oldest problem of them all. It feels like a good time to remind you (and me) what we can do when everything feels like wading through molasses.

Finding Clarity When Everything Feels Overwhelming

42.5 million Americans have an anxiety disorder. The data shows something important: we’re not alone in feeling stretched thin. Americans are experiencing more mentally unhealthy days than ever before, and our businesses are feeling the impact. As a leader, I’ve learned that the key to moving forward isn’t pretending everything is fine—it’s being honest with yourself and your team. That’s what makes you strong. Not trying to do it all alone.

If you want to find some control, some structure, try the 5P Framework to help you make sense of the chaos. (Yes, I can feel your eyes rolling from here. But hear me out).

The Power of the 5Ps

When everything feels like too much, I return to the 5Ps framework. Not just because I like frameworks (though I do), but because it helps me see the whole picture while breaking things down into manageable pieces:

Purpose:

Start with your “why.” Yesterday, I caught myself spinning on tasks until I stopped and asked: “What actually matters right now?” Sometimes your purpose is as simple as keeping the lights on. Sometimes it’s about growth. But you need to know which it is.

People:

Look at who’s involved – your team, your clients, your stakeholders. I had a breakthrough moment last week when I realized I was trying to solve a problem without talking to the people most affected by it. Your people hold crucial insights about what’s working and what isn’t.

Process:

This is your “how.” When I’m overwhelmed, I start documenting everything. Not because I love documentation (I do), but because writing things down helps me see where we’re making things harder than they need to be. Plus, documented processes make it easier to ask for help.

Platform:

These are your tools and data. Are they helping or hindering? Last month, we realized we were collecting data we never used while missing crucial information. Your platforms should make your life easier, not create more work.

Performance:

This is where you measure what matters. But here’s the key—measure what actually matters right now. In tough times, maintaining stability might be a win. In growth phases, you might need different metrics.

Putting It All Together

Here’s how this plays out in practice. When I felt overwhelmed this morning, I:

Checked our purpose: Are we still aligned with what matters most?
Connected with my people: What do they need to succeed right now?
Reviewed our processes: Where are we creating unnecessary work?
Assessed our platforms: Are we getting the right data?
Looked at performance: Are we measuring what truly matters?

The result? I found clarity about what needed my attention today (and what didn’t).

Making It Work For You

Start small. Pick one area where you’re feeling stuck and run it through the 5Ps:

What’s the real purpose behind this work?
Who needs to be involved?
What process would make this easier?
Do you have the right tools and data?
How will you know if it’s working?

Remember: The goal isn’t perfection. It’s about finding enough clarity to take the next step forward.

Your Next Step

Look at your biggest challenge right now. Which P feels most out of alignment? Start there. Maybe you’ve lost sight of your purpose. Maybe your processes are creating more problems than they solve. Or maybe you’re measuring the wrong things.

I’d love to know— which P resonates most with where you’re struggling right now? Understanding this helps us all learn and grow together.

Because at the end of the day, that’s what gets me out of bed (even on the tough mornings) – knowing that by sharing our challenges and solutions, we all get better at navigating whatever comes next.

How are you focusing and moving forward? Reply to this email to tell me, or come join the conversation in 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 reasoning models, a new type of AI, can revolutionize your scenario planning. You’ll discover how these advanced AI models can help you anticipate unforeseen challenges and opportunities for your business. Learn to move beyond reactive panic planning and create robust strategies for any future scenario. You will explore how to prepare your business knowledge for AI and scale your scenario planning efforts effectively across your organization. Tune in to learn how to leverage reasoning models to build a resilient and future-proof business today!

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

Last time on So What? The Marketing Analytics and Insights Livestream, we covered a marketer’s introduction to NotebookLM. Catch the episode replay here!

This week we’re going to dig into using generative AI reasoning models to do scenario planning and disaster planning. Are you following our YouTube channel? If not, click/tap here to follow us!

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In this week’s Data Diaries, we’re going to get adjacent to the third rail (politics), but it’s timely and important. Recent changes by the US government have taken thousands of useful datasets offline, especially from research organizations like the Centers for Disease Control and Prevention (CDC) and the National Institutes for Health (NIH). Other datasets are being modified in place, with data being changed retroactively.

To someone like me who is obsessed with clean, complete data, this is obviously reprehensible. But it raises the bigger picture question: what do you do when data you rely on goes missing?

This is not new; we’ve seen in the last decade marketing data sources that were exceptionally valuable just vanish, such as Meta’s Crowdtangle, Twitter’s API, and even Google Analytics (Universal Analytics, we miss you). Those folks who didn’t back up their Universal Analytics data lost years, even decades of historical data.

The loss of historical data is bad, but the loss of current data is worse, because even with predictive analytics and AI, you can only forecast so much. Like all prediction, the further you get away from a source of truth, the more a prediction degrades.

So what do we do? We work with the best data still available to us. We use AI to help us construct an understanding of proxy indicators from data we do have so that we understand the data we don’t have.

Here’s an example. In 2022, reporting for COVID data changed, as did the amount of testing reported to the CDC. That lack of testing made things seem safer than they actually were (which continues to this day). However, wastewater data was an almost perfect match with the high quality test data we developed in 2021 – so good that it was almost a perfect correlation.

As a result, even though official test data showed one number, inference from wastewater data (which is a more reliable data source) showed a very different number. For folks who wanted the most accurate data, we had a terrific proxy number.

So what’s the first step towards doing this? If you don’t have access to statistical software, you do have access to generative AI. You could, with one of today’s reasoning models (which are excellent coders, like OpenAI o1, Google Gemini 2 Flash Thinking, Deepseek R1, etc.) ask it to build you Python or R code to do that statistical analysis for you. While you never want generative AI doing the math, you absolutely do want it writing code to do the math.

You provide examples of the data you have, ask it to recommend a statistical method of correlation best suited for the data (such as Pearson, Spearman, or Kendall-Tau), build out the requirements for the software, and then have it generate the code. You’ll have working software you can reuse over and over again.

As Katie said in the opening, when things get overwhelming, when situations out of our control are happening – especially to our data – we focus on what we CAN do, what’s within our reach. When data goes missing, it’s okay to react negatively about it. Take a moment, feel the feels, and then start building your plan for what you’ll do to get around it. Missing or corrupted data is just damage, and our goal is always to route around damage so that we get to where we want to go.

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