“You can't scale on bad data. If marketers don't learn to be data stewards and systems thinkers, no amount of AI or automation will save them.” That's a quote from Kim Tran and a sneak peek at today's episode.

Welcome to Revenue Boost, A Marketing Podcast—the show where growth-minded business leaders learn how to turn marketing into a measurable revenue engine.

I'm your host, Kerry Curran—revenue growth–obsessed, go-to-market expert, and industry analyst. Each week, I sit down with the brightest minds in marketing, sales, and customer success to unpack the real-world strategies that drive sustainable growth. If you like what you hear, please be sure to follow, rate, and review the podcast on your favorite platform. It helps us reach more leaders like you.

In today's episode, Turning Data Chaos Into Clarity: How Smart Marketers Turn Metrics Into Go-to-Market Momentum, I’m joined by Kim Tran, Head of Marketing and Business Development. Together, we take a deep dive into one of the most critical and overlooked challenges in modern B2B marketing: dirty, disjointed, and disconnected data.

Kim shares her firsthand experience navigating data chaos, aligning stakeholders, and building the systems and skills needed to transform flawed inputs into strategic growth. Stay tuned until the end of today’s episode to hear how Kim recommends building your data literacy and AI readiness—one tech stack at a time. Let’s go.

Kerry Curran, RBMA (00:01.560) So, welcome, Kim. Please introduce yourself and share your background and expertise.

Kim Tran (00:07.276) Hi, Kerry. Thank you for having me. Hi, everyone. My name is Kim Tran. I am currently the Head of Marketing and Business Development.

Kerry Curran, RBMA (00:18.866) Excellent. Kim, we’re excited to have you back—this is your second time on the show. Last time, you did a great episode on change management and why it’s so important for marketers. We’ll include that episode in the show notes; I learned a lot from it. Today we’re talking about data and data governance. Marketers have access to so much data now that it can be overwhelming. We all want to use it to get smarter and invest more strategically, but how do we know which data to use and how to use it? Can you share what you’re seeing?

Kim Tran (01:14.946) Yes. A bit of background: I’ve spent the past decade in tech software, particularly in highly regulated industries—most recently ed-tech and financial services. In those sectors, we dealt with an abundance of sensitive data. A major challenge is that data is often siloed across different technologies and teams, and it’s not always clear who is responsible for it or who can access it. Whether you’re in a small company, where maybe only one person owns the data, or in a Fortune 500 firm with many data “cooks,” marketers need to become good data stewards and stay curious. The days when marketers could focus solely on brand or creative work are gone—especially in the age of AI. We’ve reached a critical inflection point: we now deal with synthetic, AI-generated data in addition to human-created data. Another challenge is systems thinking. Marketers must understand how data connects across teams; too often, we see a single metric and make knee-jerk decisions without context.

Kerry Curran, RBMA (04:13.888) Absolutely. For marketing leaders who are just beginning to think strategically about data, what first steps can they take to establish a solid foundation?

Kim Tran (04:37.838) Great question. We tend to jump into execution—looking for quick wins and feeling pressure from all sides—but data strategy is a long-term play. Your automation workflows, personalization efforts, and data privacy compliance will all depend on that foundation. First, learn how to learn. Many marketers come from creative backgrounds, and diving into technical tools can feel scary. I encourage hands-on learning: log into your marketing-automation platform or Salesforce and explore the data yourself. You can’t scale on bad data. Second, remember that marketers must be discerning. AI can ingest data faster than humans ever will, but if that data is flawed, the output will be flawed. Finally, create a culture of learning together. My team works closely with IT to clean our data, focusing on quality over quantity. These skills are increasingly non-negotiable as AI and synthetic data become the norm.

Kerry Curran, RBMA (08:04.142) Some marketers come from more creative roles and might feel intimidated by data. What misconceptions do you see, and how do you address them?

Kim Tran (08:34.636) One misconception stems from fear of becoming obsolete. I started in B2B tech 11 years ago, just as marketing automation platforms were taking off. The same anxieties we now see with AI existed then. If your workflows rely on outdated data, AI will simply generate inaccurate outputs faster. Another misconception is that marketers can remain purely creative. In reality, you must reverse-engineer from your goals. Whether you need more revenue, cost savings, or compliance, work backward to build the right workflows. AI can be an “over-confident intern.” It may sound authoritative but can hallucinate. Critical thinking and strong data foundations are essential.

Kerry Curran, RBMA (11:36.680) You mentioned aligning data metrics with business goals. How do you ensure stakeholders speak the same language?

Kim Tran (11:45.516) I always ask stakeholders what matters most: pipeline? Lead quality? At a large enterprise, my only KPI used to be the number of leads. In a smaller company, I can’t rely on volume alone. Two years ago, we cleaned our Salesforce pipeline—eliminating legacy data from acquisitions. Now, while we still track MQL and SAL rates, we focus on pipeline health and conversion rates. Aligning metrics to leadership’s priorities—revenue and quality—keeps everyone on the same page. Avoid blanket statements like “SEO is dead” or “events don’t work.” Often, that’s survivorship bias—you’re only looking at the data you have, ignoring missing context. Ask broader questions. For instance, when acquisition costs tripled, I asked why retention was also triple. We discovered users were gaming limited-time offers. That insight shifted our strategy entirely.

Kerry Curran, RBMA (26:10.264) For listeners who want to improve their data literacy, what foundational skills or resources do you recommend?

Kim Tran (26:31.852) Start by mastering the tech stack you already have. If you inherited Salesforce, dive in—pull reports, explore the data, and learn its quirks. Seek out resources like DataCamp and General Assembly. On LinkedIn, communities such as Women in AI share practical content. Finally, remember the value of learning together.

My young daughter is learning Mandarin, and her teachers reward participation—even mistakes. Adults need that same mindset: it’s okay to be beginners again. As you build confidence with your data, you’ll be better equipped to tackle larger AI initiatives and communicate insights to leadership.

Kerry Curran, RBMA (30:10.505) Thank you, Kim. How can listeners connect with you?

Kim Tran (30:19.340) If you’re interested in data and information-governance solutions, visit gimel.com. Personally, I’m sharing my data-literacy journey on LinkedIn—find me at Kim Tran, I’m always open to virtual coffee chats, especially in the DC metro area.

Kerry Curran, RBMA (30:54.798) Fantastic. Thanks again, Kim. Great to have you back, and I look forward to having you on again soon.

Kim Tran (31:03.096) Thank you, Kerry

Thanks for listening to Revenue Boost: A Marketing Podcast. If this episode gave you new ways to think about data alignment or building smarter marketing systems, share it with a teammate who needs to hear it. Don’t forget to subscribe and leave us a review on your favorite podcast app—it helps other revenue leaders like you find us. And if you're looking to build a better go-to-market strategy rooted in real metrics and real results, visit revenuebasedmarketing.com. Until next time: align your data, learn together, and scale with purpose.

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