Principles of Building Data Profitable Products

WebinarMay 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, Srujan Akula, the CEO at the Modern Data Company, teaches you about common types of data product that can create value for your organization, what you need to do to set up the people and processes and infrastructure to create data products, and how you can get a return on investment from your data product initiatives.

Key Takeaways:

Learn about the types of data product you can create, and how they can help your company.
Understand how to set your team up for success developing their first data product.
Learn about best practices and things to avoid when developing data products.

Resources

Summary

In the rapidly evolving digital environment, the lines between data and software development are increasingly becoming unclear, leading to the emergence of data products. Here, data takes precedence, and is designed to aid organizations in leveraging data to drive business outcomes. Srujan Akula, CEO of The Modern Data Company, underlines the necessity of crafting data products that are both refined and reusable, ensuring swift value delivery. Data products should align with product thinking, focusing on business intent and user-centric outlook. They should be easily accessible, reliable, and secure, with standard integration options to simplify data consumption. Effectively deploying data products can markedly cut down time-to-value, enhance governance and data quality, and uncover competitive advantages by enabling organizations to confidently employ data for multiple use cases.

Key Takeaways:

Data products blur the line between data and software development, focusing on delivering data-driven business outcomes.
Effective data products are refined, reusable, and built with product thinking, emphasizing business intent and user-centricity.
Reliability, security, and accessibility are key traits in a successful data product.
Deploying data products can reduce time-to-value, improve data quality, and enhance competitive advantage.
Standard interfaces and integration options are vital for smooth data consumption and application building.

Deep Dives

The Importance of Product Thinking in Data Products

Data products should align with product thinking, a strategy that gives priority to und ...
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erstanding the specific purpose and business intent behind the data. Srujan Akula points out, "You start with what is the business intent, what is the use case that you're trying to execute." This approach ensures that the data is not simply an aggregation of raw inputs but is refined, enriched, and pre-processed to deliver value directly aligned with business goals. By embedding product thinking, organizations can eliminate the inefficiencies of being 'data rich and insights poor,' where vast amounts of data fail to translate into actionable insights. Instead, data products become independently valuable units, offering reusable data that drives quicker time-to-value and enhances decision-making processes.

Reliability and Security in Data Products

Building trust in data products is vital, as it affects how data is utilized across an organization. Akula emphasizes the need for setting up the right Service Level Objectives (SLOs) to assure data consumers of the quality and compliance aspects of the data. He states, "The SLOs and the data contracts implicitly will guarantee the trust in data with full audit compliance logging." This trust is key in making data products the single source of truth, allowing data to be shared confidently within and outside the organization. Security is non-negotiable; data products must be secure from a privacy and compliance perspective, often necessitating flexible governance to protect sensitive information while enabling smooth data sharing.

Data Product Accessibility and Reusability

For data products to be effective, they must be easily accessible and reusable. Accessibility is facilitated through a centralized data catalog that allows users to find and understand data products efficiently. Akula stresses, "Having a very simple discoverability, so that based on the use case, you know where to find the data product." Reusability is achieved by designing data products with standard integration options, such as APIs and connectors, which allow them to be used across various applications and business processes. This approach not only enhances productivity but also ensures that data products can serve multiple purposes without necessitating redundant data copies, thereby optimizing resource use and maximizing data's value.

Integration and Compatibility

Integration and compatibility are vital for the successful deployment of data products. Data products should be designed with ready-to-use integration options that allow them to connect smoothly with existing systems, whether it's for powering analytics dashboards or machine learning applications. Akula notes, "These ready-to-use integration options allow you to quickly deliver the data in the right formats at the right cadence." This capability ensures that data products can operate across different cloud environments and data infrastructures, providing a consistent consumption-ready layer that decouples data usage from data management. As businesses increasingly rely on data-driven insights, having a flexible and compatible data product layer is essential for maintaining agility and scalability.