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What’s up everyone, today we have the pleasure of sitting down with Erin Foxworthy, Industry Lead, Advertisers & Agencies at Snowflake.

Summary: In this episode, Erin takes us on a ride through the merging worlds of martech, adtech, AI, and privacy, giving a bold glimpse into what’s next for customer data. We cover how you can use 1st party data for seed predictions, why it’s time you move on from APIs and adopt data sharing and what the unified data layer means for marketers. Oh and Erin gives us her take on the uncertainty of Google’s cookie deprecation rollback.

Jump to a Section

• The duality of creativity and measurement in advertising
• The future of automation in creative marketing
• Understanding the convergence of Martech and AdTech
• The uncertainty of Google’s cookie deprecation rollback
• Leveraging first-party data for predictive personalization
• The shift from APIs to data sharing in cloud environments
• The importance of a unified data layer in marketing operations
• The shift towards CMOs engaging with data platforms

About Erin

• Erin is former Category Development Lead at Microsoft Advertising collaborating with product, marketing and sales teams
• She later became Executive VP of Partnerships and Innovation at Horizon Media – the popular NYC-based ad agency –  focused on first-to-market creative and data opportunities for her clients
• She’s also a well traveled speaker and was awarded the Technology Leader at Cynopsis Top Women in Media in 2020
• Today Erin serves as the Industry Principal for media, entertainment and advertising at Snowflake, focusing on advertisers and agencies

The duality of creativity and measurement in advertising

Back in the early days of advertising, when Madison Avenue reigned supreme, media was often an afterthought. Chief marketing officers were obsessed with nailing the perfect TV commercial, crafting flawless direct mail campaigns, or polishing magazine spreads. It was all about the creative, and media played a quiet, supporting role in the background. But as digital platforms exploded and ad units scattered across a thousand channels, the game changed. Creative and media teams, once working hand in hand, started to drift apart. The complexity of new media forced agencies to specialize, dividing the two disciplines.

Erin has seen this separation unfold throughout her career. Starting out at a full-service agency, she was part of a close-knit team where media, creative directors, and copywriters shared ideas freely. But as agencies scaled, media buying became its own beast, spread across hundreds of fragmented platforms, and those cross-disciplinary discussions became rare. Managing the sheer volume of media placements took over, leaving little room for creative collaboration.

The silver lining? Erin believes AI might be the key to bringing that collaboration back. AI advancements are already pushing the industry toward more integrated workflows. With algorithms optimizing ad performance on autopilot, media teams can offload some of the data-crunching and refocus on the bigger creative picture.

She also sees AI democratizing creativity, allowing marketers who aren’t typically involved in the creative process to step up. With AI taking care of the numbers, the door is wide open for creative and media to work together once more, recapturing the synergy that used to drive the best campaigns.

Key takeaway: AI is reshaping the way creative and media teams work together in advertising, bringing them closer again. By automating tasks like data analysis and optimization, AI frees marketers to step into creative roles and focus more on what drives results—both organically and through paid channels.

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The future of automation in creative marketing

We all wonder just how much we can really trust machines to take over core marketing tasks, especially in something as personal as email. AI-driven recommendations are everywhere, but they often get side-eyed skepticism. When Erin was asked about automation’s place in creative marketing, she didn’t hold back.

She’s quick to point out that automation is already shaking things up in marketing ops, especially in the nuts-and-bolts stuff like resizing creatives or serving ads. Those areas are ripe for disruption, and frankly, automation is becoming indispensable just to keep pace with the overwhelming complexity of campaigns. But when it comes to those creative, brand-driven ads—the ones that have to hit consumers right in the feels—Erin’s not convinced AI can replace humans anytime soon.

The crux of it, in her view, is that branding is all about nuance. Creative ads are supposed to forge emotional connections, and that requires empathy, intuition, and a deep understanding of human behavior. Machines just aren’t there yet. Sure, AI can handle the logistics, like deciding where and when to run ads, but translating a brand’s personality into something emotionally resonant? That’s still a job for human hands.

Erin believes AI will continue to grow in marketing operations, streamlining the backend work. But when it comes to brand storytelling, human creativity is still holding the line. As AI keeps evolving, marketers will need to strike a delicate balance—leveraging the speed and efficiency of automation without losing the human touch that makes ads truly connect.

Key takeaway: Automation is set to keep shaking up marketing operations, especially when it comes to streamlining workflows and fine-tuning ad delivery. But when it comes to crafting those creative brand messages that resonate, human creativity is still the secret sauce that AI can’t replicate. Marketers need to lean into AI for its efficiency, but not at the cost of losing that all-important human touch. The trick is finding the balance—using AI as a tool to enhance, not replace, the magic that only people can bring to a brand’s story.

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Understanding the convergence of Martech and AdTech

When asked about the difference between Martech and AdTech, Erin cuts through the usual surface-level explanation. People often throw around the idea that Martech is for marketers and AdTech is for advertisers, but she thinks that’s way too simplistic. The real story is far more complex. Both Martech and AdTech are powered by the same thing—technology. These platforms are designed to serve marketers and advertisers alike. The tricky part isn’t figuring out who owns which tool, it’s about finding the right tech that fits a company’s specific goals and needs.

Erin highlights how this blurring of lines is becoming even more apparent as personalization takes center stage in both marketing and advertising. Martech used to be the go-to for managing owned channels like email and SMS, while AdTech was all about paid media—social ads, programmatic buys, that kind of thing. But now? The two worlds are colliding. Personalization isn’t just for emails anymore—it’s creeping into paid channels, social media, even connected TV (CTV) campaigns. And this merging of tech only works if you’ve got the right data infrastructure in place, something that can handle one-to-one personalization across every channel your customer touches.

The real challenge, Erin says, comes from the fact that marketing has historically been siloed. Marketers were sending out emails, running social ads, and buying media separately, often without much communication between teams. But now, with consumers expecting a seamless, personalized experience, businesses are having to figure out how to connect the dots. Whether it’s an SMS campaign or a CTV ad, every touchpoint needs to work together to craft a cohesive customer journey.

At its core, this shift is about using data across all platforms to deliver personalized, consistent messaging. For Erin, the merging of Martech and AdTech is pushing companies to unify their tools and create a scalable, integrated system. It’s an exciting, yet daunting, trend that’s forcing businesses to rethink how they handle customer data and interactions across the board.

Key takeaway: The traditional divide between Martech and AdTech is becoming outdated. As personalization continues to drive both marketing and advertising, the real challenge lies in unifying customer interactions across all channels on a scalable platform. Businesses must move beyond simple categorizations and focus on seamless integration to stay competitive.

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The uncertainty of Google’s cookie deprecation rollback

When asked about Google’s latest move to delay third-party cookie deprecation, Erin zeroes in on a critical shift in the narrative. Google isn’t ditching the idea altogether—instead, they’re shifting to a model where the power to deprecate lies with the consumer. Much like Apple’s App Tracking Transparency (ATT), Google is now putting the decision in the hands of the user, giving them the choice to opt in or out of data tracking.

At first glance, this might sound like a breather for marketers who thought cookies were on their way out. But Erin is quick to point out that it’s far from a simple reprieve. The real challenge lies in the unknowns—how aggressively will consumers opt out? Will marketers be left scrambling for meaningful data, or will consent be granted at rates that allow business to continue as usual? No one really knows yet, and that’s the unsettling part.

She also touches on the broader ramifications. This isn’t just about cookies anymore; it’s part of a larger movement where platforms like Google (with GA4) are shifting more responsibility onto users. Apple’s already led the charge here, and now Google is following suit, but it leaves a lot of questions hanging. If opt-out rates skyrocket, the data that’s been the lifeblood of personalized ads could dwindle, throwing future strategies into question.

As Erin sees it, this isn’t just a delay in cookie deprecation—it’s a complete reframing of the conversation around privacy and consent. Marketers can’t afford to get comfortable, thinking things are going back to the way they were. Consumer control is becoming the new normal, and brands will have to rethink how they navigate this world of shifting data collection and privacy concerns. While the immediate fear may have calmed, there’s still plenty of uncertainty ahead.

Key takeaway: Google’s delay in deprecating third-party cookies shifts the focus to consumer consent, leaving the future of data collection uncertain. Marketers should not assume a return to normalcy but should prepare for an era where privacy and user control continue to shape how data is accessed and utilized.

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Leveraging first-party data for predictive personalization

We often hear that first-party data can replace third-party cookies, but Erin emphasizes that the real strength lies in how first-party data can act as a “seed” to unlock broader reach. While first-party data alone may not match the scale of third-party cookies in terms of unknown audiences, it has a richness that can be amplified when used strategically with platform partnerships.

Erin points to the value of leveraging first-party data in conjunction with publisher data from major platforms like Disney or NBCU. These companies know their consumers deeply, and their data ecosystems are becoming more unified through tools like Snowflake. When businesses share their first-party data as a seed, it feeds into these platforms’ data models, improving algorithmic accuracy and optimizing the targeting process.

This data loop is a two-way street. First, the first-party seed helps inform external data sets on the platform side, enhancing the ability to reach new audiences. Then, businesses can bring back conversion data to refine the algorithms further, ultimately boosting KPIs such as conversions, engagement, or customer lifetime value. This cyclical relationship between first-party data and platform optimization can drive much higher performance compared to relying solely on massive volumes of third-party data.

The challenge lies in identifying the right seed and ensuring it aligns with business objectives. Erin notes that artificial intelligence is playing a growing role in this space, improving the accuracy of seed selection and predictive modeling. This blend of AI and first-party data is quickly becoming the future of effective personalization at scale.

Key takeaway: First-party data, when used as a seed in collaboration with publisher platforms and AI, can unlock powerful personalization and reach. The key is finding the right seed to feed into these platforms, allowing businesses to amplify their data-driven efforts and optimize their KPIs through smarter targeting and predictive algorithms.

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The shift from APIs to data sharing in cloud environments

As companies continue to refine their data strategies, Erin sheds light on the significant shift from relying on traditional APIs to embracing cloud-driven data sharing. For years, marketers and product teams depended on developers to build and maintain API connections, often leading to bottlenecks, delays, and errors in integrating platforms like Marketo or Salesforce. With the advent of modern cloud infrastructure, these challenges are being resolved through data-sharing capabilities that offer real-time access without the complexity of traditional methods.

Erin explains that platforms like Snowflake allow businesses to expose a view of their data, providing partners or internal teams the ability to query that data without creating multiple copies. She likens this to the concept of a shared Google Doc—both parties can interact with the same data in real time without moving or duplicating it. This real-time, cross-cloud access means that companies can instantly read data into their databases, drastically reducing the time and effort traditionally spent on data integrations.

The benefit of this is most obvious to data engineers, who often find API maintenance a tedious task. Erin points out that data-sharing tools like Snowflake’s zero-copy data sharing remove the need for constant upkeep of APIs. Engineers no longer have to worry about maintaining connections, handling version control, or troubleshooting API breaks. This functionality allows them to focus on higher-value work, rather than getting bogged down in repetitive tasks that don’t drive business outcomes.

This shift isn’t just technical—it’s becoming a business standard. Erin shares an example of a large holding company that now mandates data sharing with partners whenever possible. By leveraging its buying power, the company insists on using Snowflake’s data-sharing features rather than allowing data transfer through more cumbersome means. This approach saves both time and resources while ensuring that the insights coming from platforms like Salesforce or The Trade Desk can be acted upon immediately.

The advantages don’t stop at efficiency gains. Erin notes that this type of real-time data sharing enables companies to tap into deeper, more actionable insights from their Martech and AdTech ecosystems. Whether it’s tracking ad performance or optimizing marketing campaigns, instant access to clean, queryable data provides faster paths to key performance indicators (KPIs) and business outcomes. As more companies adopt this model, we’re likely to see a growing number of use cases that highlight the transformative potential of cloud-based data sharing.

Key takeaway: Cloud-based data sharing not only eliminates the need for API maintenance but also enables companies to access real-time, actionable insights across platforms. This approach saves time, frees up engineering resources, and allows teams to focus on driving more strategic business outcomes. Embracing data sharing can lead to faster decision-making, more efficient workflows, and a competitive edge in leveraging customer and marketing data.

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Composability and zero-copy data in modern CDPs

When asked about the ongoing debate between packaged and composable customer data platforms (CDPs), Erin provides clarity on why composability is gaining traction. For companies that have already invested heavily in building their data architecture on platforms like Snowflake, recreating that work in a packaged CDP—just to bring it closer to marketing—can feel redundant and costly. The concept of zero-copy data becomes central to this conversation, especially when companies are looking to minimize data movement while maximizing accessibility.

Erin emphasizes that zero-copy data sharing, which allows teams to work directly with live data without making duplicative copies, solves two core challenges: cost and security. With the rise of cloud platforms like Snowflake, businesses now have the infrastructure to manage large-scale data without moving it around unnecessarily. The shift toward composable CDPs stems from this realization—why create another copy of your data warehouse when you can bring marketing, analytics, and other applications directly to the existing data?

This composability model not only reduces infrastructure costs but also minimizes security risks, as sensitive data remains in a centralized, well-controlled environment. Erin notes that the conversation has shifted as technology has caught up—platforms like Snowflake now offer the scalability and performance necessary to handle large volumes of data in real time. This opens up opportunities for Martech and AdTech platforms to seamlessly interact with enterprise data without requiring costly and redundant integrations.

Erin also highlights that the ecosystem is evolving to support this approach, with a growing number of warehouse-native applications emerging. These tools, built directly on top of the data warehouse, further streamline the process of accessing, analyzing, and activating data, reinforcing the notion that data sharing—rather than data replication—is the future.

Key takeaway: Composability, powered by zero-copy data sharing, enables businesses to interact with their data in real time without duplicating it. This approach reduces costs, enhances security, and allows companies to bring applications directly to their data, rather than recreating complex infrastructure. Embracing this model simplifies workflows and optimizes performance, making it a preferred option for modern CDP strategies.

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The evolving role of the cloud data warehouse

When asked about the future of cloud data warehouses, Erin offers a thoughtful, forward-looking perspective. One of the biggest challenges marketers face today is wrangling data from multiple platforms, unifying it, and transforming it into actionable insights. This is where platforms like Snowflake excel, by providing a foundation that simplifies the process of integrating and managing data across a wide range of applications. As Erin puts it, this is foundational to what Snowflake and other cloud data platforms aim to do—and it’s only going to become easier over time.

What’s particularly interesting is how Erin envisions the future of data democratization. Right now, working within a cloud data warehouse often requires a deep understanding of data engineering or data science. But as AI continues to evolve, she speculates that business users could take on more of these roles without needing as much technical expertise. In a future where AI-driven platforms can handle complex data tasks, business users may be empowered to wrangle and analyze data themselves, making insights more accessible across an organization.

This shift could have profound implications for Martech and AdTech. Instead of needing specialized technical teams to operate, these platforms could interoperate seamlessly with customer data where it already resides—in the cloud data warehouse. This would lead to a more efficient ecosystem where data doesn’t need to be moved, duplicated, or siloed. Applications would interact directly with the data in real time, creating a robust marketing foundation for businesses to build on.

One of the key advantages of Snowflake, Erin notes, is its neutrality. Unlike many other platforms that come with built-in biases or limitations due to business interests, Snowflake remains agnostic. This neutrality ensures that companies can operate without worrying about their data being locked into proprietary silos or being manipulated for ulterior motives. In an era where many platforms attempt to lock in customers, having a neutral space for data offers significant flexibility and transparency.

Key takeaway: Cloud data warehouses are set to become even more powerful as AI reduces the technical barriers for business users. Platforms like Snowflake offer a neutral, scalable foundation that simplifies data management and application interoperability. As Martech and AdTech platforms increasingly integrate with cloud data, the future will favor ecosystems that prioritize openness and flexibility, enabling organizations to operate without being locked into silos.

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Bridging the gap between data engineers and marketers

When asked whether marketers are ready to leverage AI for managing complex data tasks, Erin points out that there is a significant gap between traditional marketers and data engineering knowledge. Right now, many marketers come from a background where they are far removed from the technical aspects of data wrangling, pipelining, and integration. Erin believes that this is a paradigm shift that needs to happen—and the good news is, it’s starting to.

Erin has observed some forward-thinking organizations where marketers are beginning to engage more deeply with the technology side of the business. However, for this shift to become widespread, companies like Snowflake must play a role in bridging that educational gap. It will become increasingly necessary for marketers to understand, even at a high level, how their data is being collected, cleaned, and transformed. This understanding will be critical in the future, especially as AI evolves to automate many of these processes.

At the same time, Erin sees a convergence happening in the other direction. Data engineers and technologists, who often sit within the marketing organization, are becoming more attuned to the downstream implications of the data they manage. This mutual learning is essential. The industry is already seeing “unicorns” who have mastered both sides—marketers with a solid understanding of data engineering and data engineers who grasp the strategic use cases that marketers are trying to unlock.

What excites Erin is how this convergence will drive new use cases. As marketers become more adept at working with AI-driven data processes, they’ll be able to unlock deeper insights and more real-time opportunities, such as improved transparency in campaign measurement or more accurate customer segmentation. This convergence is the future of marketing, where understanding the technical side will no longer be optional, but essential for unlocking powerful marketing potential.

Key takeaway: Marketers need to close the knowledge gap between traditional marketing and data engineering to fully leverage AI and data automation in the future. By learning more about the technical side of data, marketers can unlock transformative use cases, making their campaigns more data-driven, transparent, and impactful. The future belongs to those who can navigate both worlds.

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The importance of a unified data layer in marketing operations

When asked about the role of a unified data layer within marketing operations, Erin cuts straight to the point—time to value. A properly structured data layer, one that’s organized and standardized across all channels and brands, can reduce the time it takes for marketing teams to see value from their data investments. Rather than spending months or even years setting up complex systems, a unified data layer allows marketers to access actionable insights in a matter of days or weeks. This speed-to-insight is critical for agile teams looking to capitalize on trends and optimize campaigns in real-time.

Beyond time-to-value, Erin highlights another crucial factor: privacy and consent management. With privacy legislation tightening, organizations need to maintain fine-grained controls over customer data. If that data is siloed across different systems, managing consent downstream becomes a logistical nightmare. A unified data layer eliminates these silos, making it far easier to manage privacy requirements at scale.

One of Erin’s key points is that there’s no AI strategy without a solid data strategy. Marketers often get excited about the potential of AI and machine learning, but without organized, accessible data, those AI initiatives won’t get off the ground. Erin stresses that AI needs clean, structured data to build models, generate insights, and create real value. Many organizations have great AI ideas, but their disorganized data environments prevent those ideas from becoming reality.

Looking ahead, Erin sees the integration of a strong data layer as essential not only for present-day marketing efficiency but also for future innovations. With AI and automation becoming central to marketing operations, the need for a unified data foundation is greater than ever. As Scott Brinker and others in the space have emphasized, this unsiloing of data is what will drive the next wave of marketing technology advancements.

Key takeaway: A unified data layer is critical for accelerating time-to-value, ensuring compliance with privacy regulations, and enabling AI strategies. Without clean, organized data, marketing teams can’t fully leverage the power of automation and AI, making the integration of a strong data foundation essential for future success.

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Preparing marketers for the future of AI-driven data

When asked what advice she’d give marketers looking to prepare for the future of AI-driven marketing, Erin acknowledges the challenge of balancing the demands of a busy role with the need to stay ahead of the curve. Marketing is one of the most demanding jobs within an enterprise, and finding time to focus on future developments in data and AI is no easy task. However, she stresses that staying informed is key.

Erin emphasizes the importance of tuning into thought leaders, podcasts, and industry experts who are shaping the future of marketing. These resources allow marketers to gain insight into where the industry is heading without having to carve out large blocks of time for formal education. It’s about staying curious, listening to those on the frontlines, and absorbing as much as possible about the role of AI, data literacy, and the evolving marketing landscape.

A crucial piece of advice Erin offers is the importance of having—or hiring—a marketing operations expert who can serve as a translator between marketing teams and data engineers. These “unicorns,” as she calls them, help bridge the gap between technical and creative teams, ensuring both sides understand how to leverage the power of data to drive marketing success. Finding or cultivating this talent within an organization will be essential for future-proofing marketing teams.

Lastly, Erin highlights the value of working with systems integrators (SIs) or specialized consultancies. While SIs were not traditionally part of media and marketing organizations, they are becoming increasingly important in helping CMOs navigate the complexities of the Martech landscape. Many consultancies are stepping in to advise on data strategies and AI readiness, and Erin recommends marketers take advantage of these resources to gain a competitive edge.

Key takeaway: To stay competitive in the AI-driven future of marketing, marketers need to immerse themselves in the insights of thought leaders, hire experts who can bridge the gap between marketing and data, and leverage specialized consultancies to stay ahead. Staying informed and building the right team will be crucial for navigating the complexities of AI and data-driven marketing.

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The evolving role of the CMO in a fragmented marketing landscape

The new demands placed on CMOs have become increasingly complex, particularly in enterprise organizations where marketing systems are often siloed and disconnected. Erin points out that many CMOs come from specialized backgrounds—whether traditional marketing, PR, or promotions—leading them to lean heavily into the areas they know best. However, today’s challenges require CMOs to navigate a broader spectrum, especially as technology plays a central role in integrating marketing efforts.

Erin highlights that a key struggle for many CMOs is bridging the gap between technology and marketing. With fragmented systems, multiple agencies, and isolated data sources, CMOs must ensure their teams can unify data across channels and platforms. Yet, many CMOs don’t have deep technical backgrounds, making it essential to surround themselves with experts who can help them understand how technology influences everything from media to customer experience.

The tension between the CMO and CTO is another major challenge Erin addresses. Historically, marketing and IT departments have struggled to collaborate effectively, often leading to misaligned priorities. However, for an enterprise to succeed, there must be a close partnership between these two departments. Erin emphasizes that CMOs must build strong relationships with their CTOs to ensure that technology investments are driving real, measurable business outcomes.

Ultimately, Erin believes the modern CMO’s role is to build the right team of experts and foster a collaborative environment across the enterprise. Whether it’s working with the CTO, CFO, or data teams, today’s CMOs need to be flexible and technology-savvy. Those who don’t come from a tech background must actively lean into those areas, hiring and collaborating with the right people to ensure marketing strategies are supported by robust technological frameworks.

Key takeaway: The role of the CMO has expanded beyond traditional marketing expertise. To succeed, CMOs must build strong relationships with technology teams, hire experts in data and martech, and foster collaboration across the enterprise. The ability to unify marketing efforts and demonstrate technology’s impact on the bottom line is critical for driving business success.

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The shift towards CMOs engaging with data platforms

When asked about Snowflake’s shift towards marketing personas, Erin outlines a natural progression in how marketing leaders have evolved to embrace data-driven strategies. Traditionally, platforms like Snowflake were brought into organizations by IT teams, specifically through the CTO. However, as CMOs began to recognize the importance of first-party data and addressable media, their need for a deeper understanding of data platforms like Snowflake became more apparent.

Erin explains that this shift was largely driven by changes in the marketing landscape, particularly with the deprecation of third-party cookies. Marketers had to adapt, focusing on first-party data to drive everything from social to linear campaigns. As they became more advanced in their approach, they realized that they needed robust data infrastructures to support their strategies. This led many CMOs to start asking, “Where does my data live?” and ultimately discovering that platforms like Snowflake were key to enabling their data-driven marketing efforts.

A significant part of this shift has been the rise of data clean rooms. Erin notes that many marketers first encountered Snowflake through its data clean room capabilities, which allowed for secure data collaboration without sacrificing consumer privacy. As more companies like Google, Meta, and Disney adopted similar models, it became clear that data security and collaboration were essential for modern marketing. This industry-wide movement forced CMOs to become more engaged in conversations traditionally reserved for IT teams.

Erin highlights the importance of collaboration between marketing and IT, particularly as more identity providers and data applications begin to natively operate on platforms like Snowflake. This maturation of the ecosystem has brought CMOs into the fold, making them active participants in the data strategy conversation. As marketers continue to rely on data for personalization and campaign optimization, their role in understanding and leveraging platforms like Snowflake will only grow.

Key takeaway: The shift towards data-driven marketing has made it essential for CMOs to engage with platforms like Snowflake. As marketers increasingly rely on first-party data, secure collaboration, and real-time insights, they need to collaborate closely with IT teams and understand how data platforms power their strategies. The future of marketing lies in this intersection of data and technology.

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Promoting diversity and combating bias in Martech leadership

When asked about effective strategies for addressing diversity and unconscious bias in the workplace, Erin shares a passionate perspective, particularly from her experience in the technology sector. In her earlier career within the media industry, Erin notes that she often found supportive female colleagues and role models. However, as she transitioned into the tech world, the lack of women, especially in leadership roles, became more evident—a reality many industries still face today.

One of the most effective approaches Erin highlights is mentorship, whether it involves women supporting other women or men mentoring women as they advance through the organization. She has seen mentorship programs have a meaningful impact, not only helping women grow into leadership positions but also providing the necessary support networks within companies. For Erin, creating these mentoring opportunities is a key strategy for fostering a more inclusive environment where women can thrive.

Erin also points to the growing push for more women in leadership roles, particularly at the board level. She references California’s legislative efforts to increase the presence of women on corporate boards as an important step forward. Having women at the highest levels of decision-making is crucial, especially as companies grapple with the far-reaching impact of AI. Erin emphasizes that the development of AI platforms, if not approached with diverse perspectives, could unintentionally reinforce biases—an issue of increasing concern as AI becomes more integrated into consumer-facing products.

For Erin, ensuring that women have a seat at the table during major decisions—both in technical and managerial roles—is vital to shaping a more equitable future. It’s about diversity not only in numbers but in the thought processes that inform the technology and decisions that affect millions of people.

Key takeaway: Mentorship programs and promoting women into leadership roles are essential for combating unconscious bias and ensuring diversity in decision-making. As AI continues to shape the future, it’s critical that diverse voices are involved in its development to prevent bias and create a fairer, more inclusive environment in tech.

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Finding balance as a leader, mother, and creative

When asked how she balances her career as a senior leader at Snowflake with her personal life, Erin reflects on the importance of creating space for non-work-related passions. As both a mother of three and a dedicated gardener and quilter, she finds that these activities help her stay grounded. Erin humorously refers to this phase of her life as her “grandma era” — a time when hobbies like quilting and gardening provide her with much-needed mental clarity. For her, these creative endeavors allow her brain to switch off from work, giving her a sense of calm and rejuvenation that ultimately feeds her happiness.

Being a mother, Erin explains, naturally helps her transition from work to family mode. Whether she’s stepping off a stage or returning from travel, once she walks through the door, she’s simply “mom.” This role, she says, demands her complete attention and forces her to be present in a way that work doesn’t. Erin believes this switch between roles is essential to maintaining happiness, as it requires her to focus on different aspects of life.

Erin stresses the importance of keeping a strict separation between her professional responsibilities and her role as a mother. She’s always mom first, and having that ethos has allowed her to maintain a healthier work-life balance. By feeding both her creative and family sides, Erin believes she’s able to show up as her best self in her career. In a world that often glorifies hustle and constant availability, she attributes her success to being intentional about stepping away from work and focusing on the things that truly matter outside of the office.

For Erin, balance isn’t about perfection, but about ensuring that different parts of her life—creative projects, family, and work—receive the attention they deserve. This holistic approach to managing her time helps her stay fulfilled both personally and professionally.

Key takeaway: Finding balance as a leader involves stepping away from work to focus on creative passions and family. By intentionally separating professional life from personal life, you can feed different aspects of yourself, which in turn allows you to show up fully in both areas. Balance comes from nurturing all parts of who you are.

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Episode Recap

In this episode, Erin takes us on a ride through the merging worlds of martech, adtech, AI, and privacy, giving a bold glimpse into what’s next for customer data. We cover how you can use 1st party data for seed predictions, why it’s time you move on from APIs and adopt data sharing and what the unified data layer means for marketers. 

We kick things off by reshaping how we think about AI—it’s no longer just about automating the boring stuff. It’s clearing the way for marketers to zero in on what actually drives impact. AI may be the engine, but creativity is still the fuel. Without that human spark, no amount of automation can produce real, game-changing growth.

Then we smash through the old silos of Martech and AdTech. These once-distinct fields are now fusing together into something much more powerful. Erin stresses that it’s not about picking a side anymore—it’s about weaving them into a unified strategy that drives personalized experiences at scale. If your tools aren’t synced up, you’re leaving massive gaps in your customer experience, and those gaps are costing you.

Next, Erin zooms in on first-party data. As the cookie era crumbles, internal data becomes your most valuable currency. The challenge? Knowing how to harness it. AI can supercharge your ability to leverage this data, but the magic happens when you feed the right inputs into the system. Get that wrong, and you’re just spinning your wheels. Get it right, and your marketing is suddenly predictive, precise, and incredibly personal.

Erin wraps up by diving into cloud-based data sharing, where the real revolution is happening. Imagine breaking free from the grind of API maintenance—data flows like it should, fast and unobstructed, giving teams the real-time insights they need to stay nimble. The result? Faster decisions, more strategic moves, and a workflow that’s no longer bogged down by technical hurdles.

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Intro music by Wowa via Unminus
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Future-proofing the humans behind the tech

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