Five data product distribution models for artificial intelligence

If we get to the core of the difference in distribution logic between data products and software or consumer goods, we will touch on the intrinsic nature of data and how data integrates into human processes and business activities.

Data products are not sold or consumed but integrated into decision-making and the operation of systems. In the software field, distribution refers to channels; in the data field, it refers to dissemination and how the truth can flow within an organization without being distorted .

5 Data Product Distribution Models to Promote Enterprise Data Adoption

The data product distribution framework is the foundation for large-scale data applications, especially for enterprises whose core expertise is not in technology, data, or artificial intelligence. Any product (not just data products) can be mediocre or excellent, but only when the product truly reaches the users and the users can send key feedback to the product development center can we get closest to user satisfaction and create a truly first-class product experience. 

Distribution Type 1: Internal Consumption/Data as a Service

The value of data products lies in their credibility and ease of use. Internally, teams "sell" carefully curated data sets, metrics, or models not for profit but for application. Their value lies in dependence and reuse: a data set or model is only valuable when it is integrated into others' work by them. Distribution starts with regulated APIs, self-service catalogs equipped with AI-driven intelligent search, and internal marketplaces, all of which make data easy to discover, reliable, and scalable across the organization. 

Distribution Type 2: Integration into Embedded Workflows

Data can truly realize its value only when it fits the decision-making context. Integrating the results of data products into existing decision-making channels ensures that data products can be accessed in actual work. Whether embedded in a customer relationship management system (CRM), dashboard, operating system, or artificial intelligence assistant, insights are integrated into workflows rather than discovered separately. This can reduce process friction, transform data into executable strategies, and convert data artifacts such as data tables and queries into user experiences. 

Distribution Type 3: Sharing Data Product Models

For mature organizations, the scope of data distribution has gone beyond internal teams. Collaboratively developing data products means sharing models or curated data sets with partners, regulators, or industry collaborators. This not only expands the scope of insights but also creates new value streams, but it also requires a high level of governance, accountability, and clear contracts. Data distribution here is about trust across organizational boundaries. 

Distribution Type 4: Controlled Democratization

Democratization without control can lead to chaos and overwhelm data consumers with a plethora of choices. Sharing part of the data product model can provide limited and temporary access for experimentation, analysis, or artificial intelligence/machine learning training. This enables teams to explore and innovate while ensuring privacy, security, and compliance. Controlled access turns data from a bottleneck into an enabling tool, thus supporting large-scale secure experiments (which are crucial for data and artificial intelligence applications). 

Distribution Type 5: Viral Recommendations Across Teams

Once a data product proves its value, the network effect will drive its popularization. The recommendation channel in data is both a by - product of the influence of the data product and strong evidence of its influence. In fact, it is even a by - product of existing effective data distribution models. When one team uses a model to solve a real - world problem, other teams will follow suit. Storytelling, reproducibility, and internal case studies act like engines that can spread trust and accelerate promotion. In essence, viral spread in data is social trust in the truth. 

When extending to data applications and artificial intelligence applications, distribution evolves from truth (data as a service) to understanding (data applications) and then to judgment (artificial intelligence applications). 

✔️ Internal "pitching" or, more appropriately, "advocacy" (Type 1) can build trust, 

✔️ Embedding results (Type 2) creates experiences, 

✔️ Sharing models (Type 3) expands the scope of influence and maintains integrity by increasing validation points, 

✔️  Using governance as a distribution model (Type 4) targets the right touchpoints, and 

✔️  Referrals (Type 5) further expand the scope of dissemination. 

This article is from the WeChat official account "Data - Driven Intelligence" (ID: Data_0101), author: Xiaoxiao Xiaoxiao. It is published by 36Kr with authorization.