Developing Data & AI Products

WebinarAugust 2024

To increase the impact of data, you need people beyond the data team - whether other employees or customers - to make use of it. That means moving from data analyses to data products. These can include product search engines, recommendation systems, fraud detection tools, and supply chain management tools.

In this session, Sagar, SVP of Enterprise Sales and Solutions at The Modern Data Company, and Logan, Associate Director at Moody's Analytics teach you why, when, and how to create and manage data and AI products. You'll learn how to get started with high-impact projects, how to align the product strategy with business strategy, and how to architect your product.

Key Takeaways:

Learn what constitutes a good data or AI product.
Learn how to create a data product strategy that aligns with your business strategy.
Understand technical and cultural best practices for achieving product success.

Resources

Summary

The discussion focused on the evolving definitions and significance of data and AI products within the business sector. Data products are viewed as integrated, self-contained combinations of data that are ready for use, reliable, and consistently updated. AI products, conversely, are defined as software that employs AI to enhance decision-making, especially in areas such as financial risk assessment and fraud detection. The dialogue also underlined the necessity for a team approach in developing these products, involving both technical and business expertise. The need for business-driven leadership in supervising the creation and deployment of these products was stressed, as it ensures their relevance and alignment with business objectives. Also, the speakers discussed the challenges of balancing simplicity and value when starting to create data and AI products, along with the need to avoid the AI hype trap by focusing on realistic, beneficial applications.

Key Takeaways:

Data products are not simply datasets; they must be ready for use and trusted by users.
AI products should use data to inform business decisions and improve efficiency.
Successful data and AI product creation requires teamwork between technical and business teams.
Business leaders should supervise data product initiatives to ensure alignment with business objectives.
Be cautious of AI hype; concentrate on realistic and valuable applications.

Deep Dives

Understanding Data and AI Products

Data products are not just datasets; they include a combination of data, metadata, semantics, and templates, all designed to be rea ...
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dy for use and trusted by users. These products need to be constantly updated and governed to ensure their reliability and usefulness for data sharing, monetization, and analytics. AI products, in contrast, use large amounts of data to produce insights and predictions that inform business decisions. AI technologies like large language models have revolutionized the creation of AI products by enabling complex data interactions and responses to human prompts. As Logan Clark from Moody's Analytics explains, AI products assist businesses in automating processes, such as credit risk assessments, by transforming data into valuable insights.

Role of Teamwork in Product Development

Effective creation of data and AI products requires a team approach involving technical experts and business stakeholders. This collaboration ensures that the products developed are not only technically sound but also aligned with business needs. As Sagar Paul from the Modern Data Company points out, the creation of data products involves data engineers, developers, data product owners, and business users working together to ensure the product serves multiple use cases. This teamwork is essential in managing the complexities inherent in integrating data and AI technologies into business processes, where each participant's expertise contributes to a successful outcome.

Leadership and Management of Data Products

Leadership in data product development is essential for ensuring that these initiatives align with business strategies and deliver expected outcomes. While it might seem natural to assign this role to chief data officers or AI officers, both Sagar and Logan emphasize the need for business leaders to take charge. These leaders ensure that the products are relevant and impactful. Business leaders, especially those with a strong understanding of technology, can close the gap between technical capabilities and business objectives, driving the successful adoption and integration of data and AI products within their organizations.

Avoiding AI Hype and Focusing on Real Value

The AI sector is filled with hype, particularly around technologies like generative AI. However, it is essential for organizations to focus on practical applications that deliver real value. As Logan notes, it's easy to get caught up in the excitement but critical to remain grounded by identifying achievable and valuable use cases. The key lies in understanding the limitations of AI and ensuring that any product developed addresses genuine business needs. By starting with simple, high-impact projects, businesses can build trust and demonstrate value, setting the stage for more ambitious projects in the future.