How PayPal, Etsy & Unbounce Leaders Use Big Data for Innovation

Data is one of the most valuable assets a business can possess today. The ability to effectively leverage big data can provide companies with a competitive edge, driving innovation and uncovering key business insights. However, utilizing the full potential of data is not without its challenges. According to a recent report by IDC, the amount of data generated globally is expected to reach a staggering 175 zettabytes by 2025. This explosive growth presents both opportunities and hurdles for organizations looking to use data strategically.

In one of our sessions at CTO-Con, industry leaders featuring Jay Sen, Director of Data Engineering Services at PayPal; Mike Fisher, Advisor at Etsy; and Sabah El-Hilo, VP of Engineering at Unbounce, shared their insights on overcoming these challenges and practical strategies to use big data for business innovation and growth. Here's what they had to say.

Understanding Big Data Challenges: Insights from PayPal

Jay Sen from PayPal opened the discussion by highlighting several challenges organizations face when dealing with big data. According to Jay, one of the primary issues is the organizational structure and budget allocation for the data stack. Companies often struggle with adopting the right technologies and aligning their data strategies with their business goals. A significant challenge he noted is building trust in the data being collected—ensuring that the data is not only accurate but also effectively leveraged for business decisions.

Jay emphasized that many organizations face a misalignment between data producers (those who collect the data) and data consumers (analysts and data scientists). This misalignment can lead to ineffective data strategies, where teams operate with varying levels of efficiency, often resulting in missed opportunities for leveraging data effectively. Jay suggested that addressing these challenges requires clear communication and alignment between all stakeholders involved in the data lifecycle.

Organizing for Success: Mike Fisher's Perspective from Etsy

Mike Fisher, an advisor at Etsy, expanded on Jay’s points by focusing on organizational and cost-related challenges. He pointed out that one of the first hurdles for companies is determining data ownership—who owns the data and who is responsible for using it. He also highlighted the challenge of managing costs associated with data, particularly in transitioning from batch processing to real-time data streaming.

Mike also introduced the concept of a "data hierarchy," which begins with data collection and extends to sophisticated use cases like machine learning. He noted that many companies try to leap directly to advanced analytics without first mastering the basics of data management, which can lead to inefficiencies. Properly understanding and following the data hierarchy ensures that organizations build a solid foundation before moving on to more complex applications.

Aligning Data Strategy with Business Goals: Sabah El-Hilo's Approach from Unbounce

Sabah El-Hilo from Unbounce brought a different perspective, focusing on the strategic alignment between a company’s data strategy and its overall product strategy. She stressed that leveraging data effectively involves more than just data operations—it requires understanding how data can create value for customers. This alignment is crucial for developing data strategies that are not only operationally efficient but also aligned with business objectives.

Sabah also addressed the importance of compliance, including considerations around GDPR and terms of service. These regulatory challenges add an additional layer of complexity to data management, requiring leaders to be both strategic and compliant. She advocated for a balanced approach that aligns data collection and usage with both business goals and regulatory requirements.

Prioritizing Data Collection and Utilization

In the latter part of the discussion, the conversation shifted to prioritizing data initiatives. Jay Sen emphasized the importance of starting with a clear understanding of why data is being collected. He mentioned that organizations should work backward from their business objectives, identifying specific goals like increasing sales or optimizing marketing efforts, and then determine the necessary data to support those objectives. This approach helps avoid the pitfall of collecting excessive data without a clear purpose.

Mike Fisher supported this viewpoint, adding that while it’s crucial to collect as much data as possible due to its potential future value, the real challenge lies in effectively managing and using the data. He warned against the risks of data duplication and the associated costs, stressing the importance of efficient data storage and usage practices.

Fostering a Data-Driven Culture

Finally, the discussion turned to the importance of cultivating a data-driven culture within organizations. Jay highlighted that businesses need to integrate data usage into their everyday operations, ensuring that decision-making processes are consistently informed by relevant data insights. This cultural shift often starts with demonstrating the value of data through small, tangible wins that can build momentum and support for broader data initiatives.

Conclusion

The session concluded with a consensus that leveraging big data effectively requires a combination of strategic alignment, efficient data management, and a culture that values data-driven decision-making. By understanding and addressing the various challenges associated with big data—from organizational structure to compliance and cost—leaders can position their companies to harness the full potential of their data.

This insightful discussion provided a roadmap for organizations looking to maximize the power of their data. As our panelists from PayPal, Etsy, and Unbounce demonstrated, the journey to becoming a data-driven organization is complex but ultimately rewarding, with the potential to drive significant business insights and innovation.

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