Why Develop Data Products?
For most companies, there are three major motivations for developing data products:
Improve Customer Value: Retain current customers and improve the customer value proposition. More value = more lifetime earnings and happy customers.
Improve Company Valuation: Data products contain proprietary business logic that leverage the unique data that the company has collected, creating internal and customer-facing differentation.
New Business Development: Enable growth into new markets and deliver value for new customers.
Let’s look at how data product R&D enables opportunities to create new value. We will also share design considerations that one should make during prototyping to make future development more sustainable.
How Data Product R&D Increases Customer Value
A hallmark of R&D is an increase in customer-focused thinking. Specifically, the product design process challenges the company to think about the gaps that their products and services leave their customers with. There are always opportunities to create gains or resolve pains for customers. Once a company has customers, they have a direct channel to offering new solutions. The key is to embrace the human-centered design process that asks questions like How might we help our customers do XYZ? instead of the standard operating procedure mentality that says We should optimize and scale what we are good at.
All customers of a company experience gaps created by the company's services. And if the company's services are indeed airtight, then those real human beings have needs that are complementary to but outside of the scope of the company's services.
By reducing the amount of work that customers have to do to meet various combinations of needs, companies create positive value for their customers. We can also reduce activities that increase costs and generate negative value. For companies considering data product R&D, a major area of negative value is the high effort of understanding and getting insight from a customer's own history. Humans are visual creatures that deeply want to understand their own history and lives to make decisions about the future.
Effort in data product R&D creates assets that have value to humans, such as databases, dashboards, visualizations, and new models and language to describe the world.
The files of data that are created daily by standard company operations need to be organized into databases, labeled as information, and aggregated into visualizations for humans to extract insight. Improving automation and skill around these activities creates internal value by increasing speed and clarity of decision making. This can result in speedier services for customers. And certain visual assets can be repurposed for customer-facing applications, creating positive value by creating insight and a sense of history with the company's services.
Improving Company Valuation By Developing Proprietary Data Assets
When is a company ready to do this type of R&D? Any company-wide belief that "we don't have all the data we need'' puts us at odds with the customer, stymies development, and creates decision-making paralysis. Clearly, the stakes and emotions are high when it comes to data self-sufficiency.
The process of developing proprietary data assets involves creating new ways to label, aggregate, and visualize existing data into business-friendly metrics. The key is combining the company's unique business logic with the contents of its databases and implementing that logic into in-house algorithms. By doing more data processing and structured data analysis, we can create new applications and insight presentations.
Processing Data Through the Data Stack Creates Value
A resourceful attitude toward design and diverse approach to inputs invites us to reuse common features to serve different customers with shared needs. This resourcefulness is a form of alchemy that creates gold from ordinary materials that have been overlooked. This can result in a more valuable company just by processing and applying data and knowledge it already possesses.
Architecting Data Products to Sustain New Businesses
If the product-facing goal of prototyping is to validate what creates value, then the technology-facing goal of data R&D is to explore reusable and lean options to deliver value. Whatever effort it took to create the initial business, data products should help create new business lines that have much better cost/benefit profiles.
To create options for sustainable development, our technical design decisions are focused on the following:
Design modular code, databases, and APIs to reduce incremental costs to deliver a new service or feature.
Create on-demand access to core features to serve common needs.
Custom needs can be offered at a premium tier or service. Lessons learned can inform the next wave of feature development.
Enable seamless new feature releases that serve more diverse or niche needs.
