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What’s up everyone, today we have the pleasure of sitting down with Kevin Hu, Co-founder and CEO at Metaplane.
We’re exploring Youtube this year if this is your jam, watch the full episode below 👇
Summary: Dr. Kevin Hu gives us a masterclass on everything data. Data analysis, data storytelling, data quality, data observability and data anomaly detection. We unpack the power of inquisitive data analysis and a hypothesis-driven approach, emphasizing the importance of balancing data perfection with actually doing the work of activating that data. He highlights data observability and anomaly detection as a key to preempting errors, ensuring data integrity for a seamless user experience. Amid the rise of AI in martech, he champions marketing ops’ role in safeguarding data quality, making clear that success hinges on our ability to manage data with precision, creativity, and proactive vigilance.
Jump to a Section
- How to Ask the Right Questions in Data Analysis
- Balancing Data Accuracy with Rapid Growth
- Prioritizing Data Points in a Sea of Information
- Harnessing Anomaly Detection to Enhance Marketing Operations
- Future-Proofing Marketing Data with Anomaly Prevention
- Refining Anomaly Detection to Foster Trust in Data
- Elevating Martech by Unearthing the Unknown Unknowns of Data Monitoring
- Addressing Data Issues Before They Impact Your Users
About Kevin
- Kevin did his undergrad in Physics at MIT
- He later collaborated with his biologist sister, assisting in analyzing five years of fish behavior data. This experience inspired him to further his research and earn a master’s degree in Data Visualization and Machine Learning
- He also completed a PhD in Philosophy at MIT where he led research on automated data visualization and semantic type detection
- His research was published at several conferences like CHI (pronounced Kai) (human-computer interaction), SIGMOD (database) and KDD (data mining) and featured in the Economist, NYT and Wired
- In 2019, Kevin teamed up with former Hubspot and Appcues engineers to launch Metaplane, initially set out to be a product focused on customer success, designed to analyze company data for churn prevention
- But after going through Y Combinator, the company pivoted slightly to build data analytics-focused tools
- Today Metaplane is a data observability platform powered by ML-based anomaly detection that helps teams prevent and detect data issues — before the CEO pings them about weird revenue numbers.
How to Ask the Right Questions in Data Analysis
When Kevin shared the profound impact César Hidalgo, his mentor at MIT, had on his journey into the data world, it wasn’t just about learning to analyze data; it was about asking the right questions. César put together one of our favorite TED talks ever – Why we should automate politicians with AI agents – this was back in 2018, long before ChatGPT was popular.
Hidalgo, recognized not only for AI and ML applications but also developing innovative methods to visualize complex data and making it understandable to a broader audience, was the most important teacher in Kevin’s life. He helped Kevin understand that the bottleneck in data analysis wasn’t necessarily a lack of coding skills but a gap in understanding what to ask of the data. This revelation came at a pivotal moment as Kevin navigated his path through grad school, influenced by his sister’s work in animal behavior and his own struggles with coding tools like R and MATLAB.
Under Hidalgo’s guidance, Kevin was introduced to a broader perspective on data analysis, akin to an astronaut floating in space, untethered from the conventional constraints of earth-bound thinking. This journey wasn’t merely about running numbers through a program; it was about enriching those numbers with context and meaning, illuminating the vast cosmos of data with the light of inquiry.
Hidalgo’s mentorship, characterized by personalized attention and encouragement to delve into complex ideas like those presented in Steven Pinker’s “The Blank Slate,” propelled Kevin into a new universe of thought. In this expansive space, the questions one asked became as critical as the data one analyzed, guiding Kevin through the stars and beyond in his quest for understanding.
This mentorship experience highlights the importance of curiosity and critical thinking in the field of data science. Kevin’s reflection on his journey reveals a key insight: mastering coding languages is only one piece of the puzzle. The ability to question, to seek out the stories data tells, and to understand the broader implications of those stories is equally, if not more, important.
Kevin’s gratitude towards Hidalgo for his investment in students’ growth serves as a reminder of the value of mentorship. It’s a testament to the idea that the best mentors don’t just teach you how to execute tasks; they inspire you to see beyond the immediate horizon. They challenge you to think deeply about your work and its impact on the world.
Key takeaway: For marketers delving into data-informed strategies, Kevin’s story is a powerful reminder that beyond the technical skills, the ability to ask compelling, insightful questions of your data can dramatically amplify its value. Focus on nurturing a deep, inquisitive approach to understanding consumer behavior and market trends.
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Bridging Academic Rigor with Startup Agility
During his career in academia working alongside Olympian-caliber scientists and researchers, Kevin garnered insights that have since influenced his approach to running a startup. The parallels between academia and startups are striking, with both realms embodying a journey of perseverance and unpredictability. This analogy provides a foundational mindset for entrepreneurs who must navigate the uncertain waters of business development with resilience and adaptability.
At the heart of Kevin’s philosophy is the adoption of a hypothesis-driven approach. This methodology, borrowed from academic research, emphasizes the importance of formulating hypotheses for various aspects of business operations, particularly in marketing strategies. Identifying the ideal customer profile (ICP), crafting compelling messaging, and selecting the optimal channels are seen not as static decisions but as theories to be rigorously tested and iterated upon. This empirical approach allows for a methodical exploration of what resonates best with the target audience, acknowledging that today’s successful strategy may need reevaluation tomorrow.
Another vital lesson from academia that Kevin emphasizes is the respect for past endeavors. In a startup ecosystem often obsessed with innovation, there’s a tendency to overlook the lessons learned from previous attempts in similar ventures. By acknowledging and building upon the efforts of predecessors, Kevin advocates for a more informed and grounded approach to innovation. This perspective encourages entrepreneurs to consider the historical context of their ideas and strategies, potentially saving time and resources by learning from past mistakes rather than repeating them.
Key takeaway: Embracing a hypothesis-driven mindset should be familiar grounds for marketers. Challenge your team to identify and test hypotheses around underexplored or seemingly less significant customer segments. This could involve hypothesizing the effectiveness of personalized content for a niche within your broader audience that has been overlooked, measuring engagement against broader campaigns.
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Balancing Data Accuracy with Rapid Growth
For startups grappling with survival, the luxury of perfect data is often out of reach, and they need to balance the time they spend on data quality with the time they spend actually growing the company. Just like sharpening a pencil to a fine point before starting to write, startups must find the right moment to transition from perfecting their data to leveraging it for growth.
Kevin points out that data quality should be tailored to the specific needs of the business. For instance, data utilized for quarterly board meetings does not necessitate the same level of freshness as data driving daily customer interactions. This pragmatic approach underscores the importance of defining data quality standards based on the frequency and criticality of business decisions, ensuring that startups can navigate the delicate balance between preparation and action in their journey towards growth.
At the heart of Kevin’s argument is the concept that as businesses scale, the stakes of data accuracy and timeliness escalate. He highlights scenarios where real-time data becomes crucial, such as B2B SaaS companies engaging with potential leads or e-commerce platforms optimizing their customer journey. In these cases, even slight inaccuracies or delays can result in missed revenue opportunities or diminished customer trust.
This discourse on data quality transcends the binary choice between perfect data and rapid action. Instead, Kevin advocates for a dynamic strategy that evaluates data quality through the lens of its application. By prioritizing data accuracy and freshness in areas that directly impact growth and customer experience, businesses can make informed decisions without being paralyzed by the pursuit of perfection.
Key takeaway: Marketers must adopt a strategic approach to data quality, focusing on the criticality of data-driven decisions rather than striving for unattainable perfection. Understanding that the value of data is context-dependent enables marketers to allocate resources effectively, ensuring that data quality supports key business outcomes without hindering growth.
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Prioritizing Data Points in a Sea of Information
Marketers are often like Neo in the Matrix, inundated with a deluge of data, making the task of sifting through this information to find actionable insights daunting. Kevin’ approach to prioritizing what data points to focus on is a great example of human judgment and experience. He admits that it’s hard to know what you don’t know.
Kevin’s first answer to figuring out where to shine the spotlight in a dark basement of data to collect is relying on experts. Relying on subject matter experts who have a proven track record of success in similar scenarios is a great starting point and provides a compass for identifying valuable data points. For instance, when introducing a new demand generation campaign, an expert can pinpoint essential metrics to monitor and implement key steps like UTM parameters that will be essential to understanding the impact of certain types of content.
Aside from use case drive examples of figuring out where to start collecting data, Kevin also highlights the evolutionary nature of data management within organizations. Initially, those tasked with assembling a data stack might not have specialized in data roles but possess the necessary skills to meet immediate needs. As the organization grows and the demands on data become more sophisticated, the introduction of a dedicated data team becomes imperative. This team’s responsibility is to develop a comprehensive data model that caters to the diverse needs of the business, from marketing to revenue operations.
This strategic approach to data prioritization and management reveals the symbiotic relationship between expertise and data infrastructure. By starting with targeted data collection based on expert recommendations and gradually building a more structured data framework, companies can navigate the data overload more effectively. This methodology not only streamlines the decision-making process but also enhances the overall impact of data-driven strategies.
Key takeaway: The journey through the dark basement of data begins with expert guidance and evolves into a structured, use case-driven framework. Marketers can navigate the complexities of data overload by focusing on the most impactful data points and gradually working with their data teams to build a comprehensive data model. This approach ensures that data collection and analysis are always aligned with the strategic goals of the business, offering a pathway to informed decision-making and sustained growth.
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Harnessing Anomaly Detection to Enhance Marketing Operations
Kevin delves into the intricate world of anomaly detection, explaining how this critical tool aids marketers in navigating the vast ocean of data. By identifying discrepancies that could skew analytical outcomes, anomaly detection serves as a guardian, ensuring that marketing strategies are built on solid, reliable data.
The story Kevin shares, involving a B2B SaaS customer experiencing significant data skew due to a minor code alteration by a product engineer, illustrates the nuanced challenges faced by marketing teams. This example underscores the importance of vigilance and sophisticated data monitoring systems to catch such anomalies before they wreak havoc on marketing campaigns and decision-making processes. It’s a testament to the fact that, in the digital age, looking at charts sporadically is no longer sufficient. The dynamic nature of data demands constant, automated surveillance to maintain its integrity.
Kevin’s shout-out to tools like Census and Airbyte, alongside the acknowledgment of marketers’ balancing act between qualitative insights and quantitative data, highlights the multifaceted role data plays in shaping marketing strategies. From decision-making to automating routine tasks, the utility of data spans a broad spectrum. Yet, this utility is contingent upon the data’s accuracy and timeliness, making data quality not just a concern but a foundational aspect of effective marketing operations.
The concept of reverse engineering from the desired business outcomes to define necessary dashboards, tools, and data quality standards illustrates a strategic approach to data management. By setting clear objectives for data usage, businesses can better define what constitutes quality data, moving beyond the whack-a-mole strategy to a more targeted, efficient method of anomaly detection.
Key takeaway: Anomaly detection is not just about safeguarding data quality; it’s an essential component of a proactive marketing strategy. By implementing robust anomaly detection mechanisms, marketers can ensure that their decisions are informed by accurate, reliable data, thereby enhancing the effectiveness of their campaigns and automating processes with confidence.
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Future-Proofing Marketing Data with Anomaly Prevention
Kevin touches on the quintessential challenge of navigating through data transformations, exemplified by the shift from Universal Analytics to Google Analytics 4. This scenario revealed the fragile nature of data setups that had gone unchecked for years, leading to a pivotal realization about the importance of data integrity and the need for anomaly prevention.
The crux of future-proofing data lies in creating robust feedback loops between those who manage data production and those who rely on it for strategic decisions. This concept mirrors the analogy of doctors and pharmacists, where the creators of data, much like doctors, may not always foresee the implications of their actions downstream. Kevin emphasizes the significance of establishing connections within organizations to ensure that the impact of data changes is fully understood and accounted for, fostering a culture of continuous improvement and vigilance.
This strategy goes beyond merely rectifying errors; it’s about preempting potential issues and ensuring that data systems are not just resilient but are designed to evolve without compromising their core functionality. For marketing operations, this means being actively involved in the conversation about data quality, understanding the technical underpinnings of marketing technologies, and advocating for systems that are adaptable and robust.
Marketers, as the frontline users of data, are uniquely positioned to spearhead these discussions, acting as bridges between the technical and strategic realms. By cultivating a deep understanding of data workflows and championing the importance of data integrity, marketers can help steer their organizations towards more sustainable and effective data practices.
Key takeaway: The transition from anomaly detection to prevention requires feedback loops between data creators. Marketers play a critical role in this system, leveraging their insights to advocate for resilient data infrastructures that can adapt to the future without losing their relevance or accuracy.
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Enhancing Data Storytelling Through Simplification and Engagement
Kevin delves into the essence of data storytelling, drawing parallels to traditional narrative techniques and emphasizing the paramount importance of understanding one’s audience. His insights shed light on the challenges and strategies of making data not just accessible but compelling to those who engage with it.
At the core of effective data storytelling lies the principle of simplification. Kevin references Edward Tufte’s concept of the data-ink ratio, advocating for a minimalist approach to data visualization. This approach prioritizes clarity and focuses on conveying information with as little “ink” as possible, stripping away any unnecessary elements that may obfuscate the message. Such simplicity ensures that viewers are not overwhelmed by the complexity of data, making the insights more digestible and engaging.
Further, Kevin introduces the concept of Schneiderman’s Mantra, advocating for a structured approach to presenting data: start with a broad overview, allow for exploration through zooming and filtering, and offer detailed information upon request. This methodology mirrors the user’s natural process of inquiry, fostering a more intuitive and interactive experience with data visualizations. It’s akin to navigating a new city with a map; one starts with an overview to gain orientation, then delves into specific areas of interest for finer details.
This nuanced approach to data storytelling—balancing simplicity with depth, and structuring the narrative to match the audience’s curiosity—transforms data visualization from a mere presentation of facts into a compelling story that engages and informs. By applying these principles, data becomes not just a tool for insight but a canvas for storytelling, enabling viewers to see beyond numbers and recognize the broader narrative at play.
Key takeaway: Effective data storytelling hinges on simplicity, engagement, and a deep understanding of the audience. By minimizing complexity and structuring data presentations to guide the viewer through a journey of discovery, marketers can transform intimidating data sets into compelling narratives that drive action.
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Refining Anomaly Detection to Foster Trust in Data
Data observability presents a unique set of challenges, especially when it comes to minimizing false positives in anomaly detection. Overloading stakeholders with alerts can lead to the very tools designed to aid decision-making being ignored or silenced. The question we asked Kevin was: how to make anomaly detection both sensitive and specific enough to be genuinely useful without becoming a source of distraction.
At the heart of Metaplane’s approach to this challenge is the recognition that the traditional, off-the-shelf anomaly detection models often fall short in the nuanced realm of business data. Kevin outlines the unique dynamics of business data, such as non-real-time data loads and the potential for sudden, significant changes in data patterns due to events like company acquisitions, bot traffic or campaign launches. These peculiarities necessitate a departure from conventional models, which may not adjust quickly enough to the new realities of a dataset.
Metaplane’s strategy hinges on developing bespoke models tailored to the specific needs and rhythms of business data. By crafting these models from the ground up, Metaplane aims to achieve a delicate balance: capturing genuine anomalies while filtering out noise. This effort underscores a commitment to creating tools that not only identify potential issues but also adapt dynamically to the evolving landscape of business operations.
This tailored approach allows for a more nuanced understanding of what constitutes an anomaly within the context of a particular business’s data flows. By adjusting baselines instantly in response to significant changes, Metaplane’s models strive to avoid the trap of crying wolf, ensuring that alerts maintain their relevance and urgency.
Key takeaway: In the pursuit of effective data observability, the key is not merely detecting anomalies but doing so in a way that respects the unique characteristics of business data. By developing specialized models that account for the specific challenges of this domain, companies can enhance trust in their data processes. For marketers and data professionals alike, embracing solutions that offer this level of specificity and adaptability is crucial in navigating data systems without being overwhelmed by false alarms.
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Elevating Martech by Unearthing the Unknown Unknowns of Data Monitoring
Distinguishing between known unknowns and unknown unknowns is critical for marketing operations teams. Kevin articulates this distinction with clarity, shedding light on the necessity for tools that adapt to the intricacies of business data. Known unknowns are uncertainties we’re aware of and can plan for, while unknown unknowns are surprises that catch us completely off guard, challenging our preparedness. Kevin’s explains that basic data quality checks are known unknowns, such as ensuring unique customer IDs, but the nuanced challenge of identifying and addressing anomalies that don’t fit neatly into predefined rules are the unknown unknowns. The latter is the focus.
dbt Tests and libraries like Great Expectations serve as powerful allies in the quest for data integrity, allowing teams to set clear parameters around known data conditions. However, Kevin underscores the limitation of these tools when faced with the unpredictable nature of business data—where variables like traffic and campaign click-through rates can fluctuate based on myriad factors, making hard-and-fast rules both impractical and insufficient.
Enter Metaplane, a platform designed to transcend the capabilities of standard data observability tools by focusing on the unknown unknowns. By leveraging historical data, Metaplane’s models can detect deviations that signal underlying issues, offering a more dynamic and context-sensitive approach to data monitoring. This adaptability is crucial, especially as businesses introduce new data sources or transformations that could introduce fresh complexities into the data ecosystem.
The key advantage of Metaplane lies in its ability to maintain a high signal-to-noise ratio, ensuring that alerts are both relevant and actionable. This aspect is particularly vital in a marketing context, where the performance of campaigns across different geographies and demographics can provide critical insights into consumer behavior and campaign effectiveness.
Key takeaway: By focusing on the dynamic nature of business data and the challenges of unknown unknowns, teams can ensure their data observability practices are as comprehensive and adaptive as possible. This approach not only safeguards data integrity but also enhances the ability to glean actionable insights from marketing campaigns, ultimately driving better business outcomes.
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Data Quality Takes a Center Stage Role in AI-Driven Marketing
Kevin brings to light an essential truth that echoes through the realms of marketing operations—clean and reliable data is the linchpin of effective AI applications. This recognition not only vindicates those who have long championed data hygiene but also underscores the critical interplay between data quality and AI performance.
In the landscape of AI development, it’s not the sophistication of the models that delineates success but the uniqueness and integrity of the data fueling these systems. Kevin points out that while the prospect of crafting cutting-edge AI models may be reserved for a select few with substantial resources, the real differentiator lies in the data each business possesses. This data, reflective of specific customer interactions, preferences, and behaviors, becomes the bedrock upon which AI can generate meaningful, actionable insights.
However, this reliance on data brings its own set of challenges, particularly given the nature of AI to sometimes “hallucinate”—drawing conclusions or making predictions based on flawed or incomplete data inputs. The human tendency to take AI outputs at face value without rigorous scrutiny only amplifies the risks, potentially leading to decisions that could misalign with customer expectations or business objectives.
Thus, the conversation pivots to data observability—an essential practice that ensures data quality across the board. Kevin highlights the decades-long evolution of data governance practices and their relevance in the contemporary AI-driven marketing landscape. By drawing on these established methodologies, businesses can fortify their data infrastructures, thereby enhancing the reliability and efficacy of AI applications.
Key takeaway: The advent of AI in marketing heralds new opportunities and challenges, with data quality taking center stage as a critical success factor. Marketers and data professionals alike must prioritize data observability and governance to harness the full potential of AI, ensuring that inputs are clean and reliable.
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Addressing Data Issues Before They Impact Your Users
As the martech landscape continues to blow up into a hot mess of AI-driven apps, the role of marketing operations teams in stewarding data quality has never been more pivotal. Kevin emphasized the crucial part MOPs teams play in ensuring the integrity of data that powers AI systems but also the speed in which you can detect data issued.
Let’s face it, there will always be data issues. The question, Kevin says, is do you want to know about it before your users do? And do you want to get better at it over time? The critical differentiator for any martech or data team is the ability to identify and address these issues proactively—before they impact the end-user experience. This proactive stance is not just about maintaining the status quo but about continuously enhancing data quality to stay ahead in a rapidly evolving digital ecosystem.
Kevin gives a nice shoutout to all the marketers out there. He argues that the essence of exceptional marketing cannot be distilled into the mere generation of content by AI, such as ChatGPT. Instead, it’s the nuanced, holistic understanding of how to convey a value proposition effectively—a skill that AI, in its current form, cannot replicate. This perspective reaffirms the enduring importance of human creativity and strategic thinking in marketing, even as AI tools become increasingly prevalent.
Key takeaway: As marketing technology evolves, the intersection of AI advancements and data quality emerges as a critical focal point. Marketing ops teams stand at the forefront of this convergence, tasked with ensuring that data issues are identified and corrected before they reach your audience. This is crucial for maintaining trust and delivering a seamless user experience. Proactive data management practices are essential for staying ahead of potential problems.
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Balancing Life and Learning for Success and Happiness
In an era where the hustle of startup culture and the demands of professional growth often overshadow personal well-being, Kevin shares a refreshingly dualistic approach to maintaining happiness and success. He navigates through the complexities of his roles—juggling responsibilities as a co-founder, CEO, academic, cycling enthusiast, language learner, and a connoisseur of Chinese cuisine—with a philosophy grounded in self-perception and continuous learning.
Kevin describes oscillating between viewing himself as a simple organism with basic needs (“Kevin the lizard”) and recognizing the depth of his emotional and intellectual being. This analogy speaks volumes about the importance of catering to both our fundamental and sophisticated selves. Ensuring physical well-being through adequate sleep, hydration, and exercise forms the bedrock of productivity and happiness. Yet, it’s the engagement with our complex nature—our passions, intellectual pursuits, and relationships—that enriches our lives profoundly.
At the heart of Kevin’s happiness lies an insatiable desire to learn. Regardless of the unpredictability of outcomes in the professional realm, he finds joy in the journey of acquiring new knowledge. By setting personal projects and diving deep into subjects unfamiliar to him, such as the intricacies of designing marketing campaigns, Kevin transforms potential stressors into opportunities for growth and satisfaction.
Key takeaway: By attending to both our basic needs and our deeper intellectual and emotional aspirations, and by framing challenges as opportunities to learn, we can find fulfillment and joy amidst the chaos. Embracing a mindset that values learning as an end itself fosters resilience and adaptability—qualities essential for thriving in today’s dynamic environment.
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Episode Recap
Dr. Kevin Hu gives us a masterclass on everything data. Data analysis, data storytelling, data quality and data anomaly detection. It’s not just about having tons of data; it’s about asking the smart questions that really dig deep. This idea is gold, especially when we’re all like JNeo in the Matrix facing a sea of data. It’s about being that detective, piecing together the clues hidden within the data to reveal the bigger picture of consumer behavior and market trends.
A common theme that Kevin’s brought over from his time in academia is the importance of being hypothesis-driven. It’s a powerful tool we should all be familiar with and have in our martech toolkit. It’s like being in a lab, but instead of test tubes and Bunsen burners, we’ve got data pipelines and martech. This approach encourages us to explore those less obvious paths and find unique ways to connect with our audience.
Kevin hits the nail on the head when he talks about finding that sweet spot between spending time perfecting data quality and actually doing the work and pushing forward to grow. It’s like being a chef in a busy kitchen; you’ve got to keep your ingredients top-notch but still making sure those plates keep coming. It means working closely with your data team to sift through the noise and focus on what really matters to your business goals.
Something that might be unfamiliar to marketers is anomaly detection and Kevin walks us through where it’s a game-changer. Think of it as having a super-smart watchdog for your data. It’s not just about catching the mistakes; it’s about being proactive and preventing them from messing up your user experience. Kevin’s thoughts on evolving from detection to prevention and creating those feedback loops within our teams? That’s the future right there. It’s about making sure our data is not just accurate but also resilient and ready for whatever comes next.
Lastly, simplicity in data storytelling is where it’s at. We’re reminded that the essence of good storytelling isn’t lost in the world of data. It’s about making those complex datasets approachable and engaging. And as we edge closer to a future where AI plays a bigger role in martech, the spotlight on clean, quality data has never been brighter. It’s clear that marketing ops teams are the unsung heroes here, ensuring that as we harness AI’s potential, we’re building on a foundation of solid, reliable data.
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Intro music by Wowa via Unminus
Cover art created with Midjourney (check out how)
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