The Analytics Power Hour - Episode #262: 2025 Will Be the Year of... with Bar Moses

Release Date: January 7, 2025

In the latest episode of The Analytics Power Hour, hosts Michael Helbling, Tim Wilson, and Mo Kiss engage in a thought-provoking conversation with returning guest Bar Moses, co-founder and CEO of Monte Carlo. Titled "2025 Will Be the Year of...", this episode delves deep into the evolving landscape of data analytics, the challenges of the modern data stack, the rise of generative AI, and the critical importance of data reliability and governance.

1. Welcome and Introduction

The episode kicks off with the hosts humorously contemplating what the year 2025 will be defined by, setting a lighthearted tone for the in-depth discussions to follow.

Michael Helbling [00:13]:
"I didn't say that very clearly, but we do want to define the future. So what will 2025 bring?"

2. Guest Introduction: Bar Moses and Monte Carlo

Bar Moses is introduced as a seasoned expert in data reliability, leading Monte Carlo in ensuring data accuracy and trustworthiness across organizations leveraging AI.

Michael Helbling [02:04]:
"Bar Moses is the co-founder and CEO of Monte Carlo, the data reliability company... Welcome back, Bar."

3. Evolution of Data Quality and Data Observability

Bar reflects on the journey since her last appearance in 2021, highlighting how data quality has risen in importance as the complexity and usage of data have surged.

Bar Moses [02:30]:
"Monte Carlo was founded to solve the problem of what we call data downtime... periods of time when data is wrong or inaccurate."

Mo Kiss [03:33]:
"I think it maybe wasn't as complex and so like, you know, as complexity has grown... it's even harder to troubleshoot."

Bar elaborates on the inception of data observability, emphasizing its role in maintaining trusted data pipelines essential for accurate reporting and AI applications.

Bar Moses [04:15]:
"Data observability is basically allowing people creating data products... to make sure that they are actually using trusted, reliable data."

4. Modern Data Stack and Its Challenges

The discussion transitions to the modern data stack, with Tim expressing skepticism about its initial promises and Bar agreeing on the necessity to revisit fundamental data principles amidst the influx of new trends.

Tim Wilson [07:36]:
"The modern data stack is a phrase that has slid into the trough of disillusionment."

Bar Moses [10:07]:
"There are core truths like data being a competitive advantage, reliable data matters, and innovation is essential."

5. Generative AI and Data as a Competitive Advantage

Bar introduces the concept of data serving as the moat in the generative AI landscape. She shares insights from a survey indicating nearly universal adoption of generative AI among data leaders but a significant lack of confidence in data reliability.

Bar Moses [10:42]:
"The only thing that differentiates different generative AI products is the data that's powering them."

Michael Helbling [12:25]:
"Only one out of three trust and two out of three don't trust the data."

6. Data Governance and Organizational Structures

The conversation delves into the complexities of data governance within organizations, debating centralized versus decentralized data management models. Bar emphasizes the importance of federated models where centralized standards guide decentralized teams.

Bar Moses [33:23]:
"Centralized data platform defines what excellence looks like... all teams adhere to the same requirements."

Tim Wilson [26:19]:
"Is it our unique data, we must use it. There's a missed step to say, like, really, what's our minimum viable product?"

7. Unstructured Data and Its Growing Importance

Bar discusses the anticipated surge in unstructured data, predicted to account for 90% of data growth, and the challenges it poses for data reliability and AI training.

Bar Moses [48:34]:
"Unstructured data is becoming more and more important... I think it's the year of messiness."

8. Reliability in AI Applications

The episode highlights the critical need for robust data reliability to prevent costly errors in AI applications. Bar recounts an example where Monte Carlo helped an insurance company ensure the accuracy of AI-generated sentiment scores from customer service interactions.

Bar Moses [51:09]:
"A conversation scored a 12... we allow them to observe the output of the LLM to make sure that the structured data is within the bounds."

9. Predictions for 2025: The Year of Highly Reliable Data and AI

As the conversation wraps up, the hosts and Bar share their predictions for 2025. Bar confidently asserts that the year will be defined by highly reliable data and AI, underscoring the industry's shift towards prioritizing data trustworthiness.

Bar Moses [65:28]:
"2025 would be the year of highly reliable data and AI."

10. Final Thoughts and Last Call

The episode concludes with the hosts sharing personal reflections and recommendations. Bar uses the metaphor of "watching the avocado" to illustrate the importance of timing in adopting new technologies like generative AI.

Bar Moses [58:56]:
"As data leaders, how do we keep watching the avocado? We gotta hit the avocado before it's too ripe."

Mo Kiss [60:05]:
"I'm trying to enjoy the journey more... not focusing so much on the end state."

Key Takeaways

• Data Reliability is Paramount: As organizations increasingly adopt generative AI, the reliability and trustworthiness of their data become critical competitive differentiators.

• Generative AI Demands High-Quality Data: With the widespread use of generative AI, ensuring data accuracy is essential to prevent costly errors and maintain brand integrity.

• Evolving Data Governance: Organizations are moving towards federated data governance models, balancing centralized standards with decentralized data management to handle the complexity of modern data usage.

• Unstructured Data's Rising Role: The bulk of data growth is expected to come from unstructured sources like text and images, presenting new challenges for data observability and reliability.

• Organizational Adaptation: The roles within data teams are blurring, necessitating a mix of engineering and data expertise to build effective generative AI and data products.

Notable Quotes

• Michael Helbling [00:55]:
  "2025 probably be the year of Tim Wilson still being frustrated with people calling stuff the year of."

• Bar Moses [10:42]:
  "What's the advantage? I can create a product just like you can create a product. The only difference is the data."

• Bar Moses [24:13]:
  "We all have access to the latest, greatest models, but the only thing that differentiates different generative AI products is the data that's powering them."

• Bar Moses [58:56]:
  "As data leaders, how do we keep watching the avocado? We gotta hit the avocado before it's too ripe."

• Bar Moses [65:28]:
  "2025 would be the year of highly reliable data and AI."

Conclusion

Episode #262 of The Analytics Power Hour offers a comprehensive exploration of the challenges and opportunities facing the data analytics industry as it navigates the complexities of a rapidly evolving technological landscape. With insightful commentary from Bar Moses and the insightful moderation of the hosts, listeners gain a nuanced understanding of why data reliability and governance are set to be defining themes in 2025.

For those eager to stay ahead in the data analytics field, this episode underscores the importance of building robust data infrastructures, embracing generative AI responsibly, and fostering organizational structures that prioritize data trustworthiness and innovation.

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