Data strategy for digital platform businesses

Does our data strategy and internal API implementation support extension towards open platform business models and ecosystem participation?

This article is part of the Digital Platforms hub

Platform ecosystems are complex systems. While platform organizations participate within larger ecosystems, a platform organization internally is also a complex system coordinating across various resources and workflows. When incumbents transform to a platform business model, the platform organization needs to be set up to facilitate workflows across diverse resources and coordinate access to these organizational resources (both data and services). Again, as mentioned above, setting up standardized access and coordination also needs to be complemented with an organizational model that empowers loosely coupled innovation teams to innovate off these standardized services.

The technical interfaces enable coordination across the various organizational building blocks, enabling them to communicate with each other.

Every platform organization needs:

• An explicit architecture design to specify the various functional building blocks across the organization;

• A definition of interfaces that specifies how the organizational building blocks interact with each other; and
• Organization-wide set of standards.

A platform organization that adheres to these core technological design principles is well set up to expand towards ecosystem orchestration. Internal organizational resources, accessible through APIs, are coordinated across the same standards as external ecosystem resources. By extending internal standards and opening out interfaces to the ecosystem business, the platform firm effectively coordinates across internal and external stakeholders.

To guide organizational and technical architecture, every platform organization needs a well defined data strategy, which articulates how data will be acquired, managed, transformed, and deployed as a crucial business resource to enable and inform business strategy. Every platform organization needs a cohesive data strategy and a structured roadmap for implementation. In most traditional organizations, data acquisition is incidental to existing business processes. At a platform firm, data acquisition needs to be strategically planned and must be determined by business goals.

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Data Helps a Platform Organization Unlock Business Value by Enabling

1. Creation of data-driven products and business models;
2. Differentiation of offerings to consumers and producers;
3. Delivery of highly personalized and relevant interactions across the ecosystem; and
4. Analytics to generate business insights and inform strategy

A data strategy and roadmap should start with identifying the to-be data inventory needed to achieve the platform business goals. The firm should then determine the data gap between the data available today and the data required to power the platform business model at various stages of the business roadmap. The data roadmap also requires coordination with individual business and organizational units to understand the current roadmap of their product and service launches and determine how much of the data gap gets addressed through new data acquired along this roadmap. On an ongoing basis, this may involve informing business units on data needs of the platform firm and incentivizing individual units to capture this data.

Data is one of the primary sources of value creation in a platform business and enables a key control point over the ecosystem business. A firm that isn’t set up to manage data internally is unlikely to gain and maintain a central position in a data-rich ecosystem. To effectively leverage data internally, a platform firm must invest in data standardization and interoperability. Data is replicable, transformable, transportable, and creates value through learning and training. These properties of data require the platform firm to have internal porosity and interoperability to best create, manage, and deliver the value of data across the firm. APIs are crucial to external access as well as internal interoperability. APIs provide functional interfaces and communication protocols and enable seamless coordination across internal and external resources. APIs also enable bidirectional data flow, enabling firms to capture data across external interactions. They enable firms to more flexibly structure and distribute products externally, as well as leverage external capabilities to run critical operations internally.

As firms across the value chain shift business processes to the cloud, API-based connectivity enables inter-process communication, and eventually inter-firm coordination across the ecosystem. Internal APIs help drive internal data porosity. Every business unit that deals with user data acts as a provider and as a customer of the relevant APIs.

An API-centered approach to building technology infrastructure requires the interfaces to be specified so that every organizational unit prepares its resources and workflows to operate across these interfaces.

In addition to facilitating interoperability through internal APIs, platform firms will also need to invest in aggregating data expertise across the organization and pooling data centrally, which will likely require a change in data governance internally as well as new rules for data usage across the organization. Instead of every unit building out its own data resources and experts, a platform should seek to integrate existing isolated data pools, while ensuring that future data collection all works with one common central data pool.

The importance of data strategy and interoperability to a platform firm’s infrastructural readiness cannot be overstated. Firms that embark on a platform journey without internal interoperability will soon realize that their efforts don’t scale despite the best customer empathy and service innovation.

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Frequently Asked Questions

How do platform organizations align data strategy with business goals during transitions to platform ecosystems?

Platform organizations ensure alignment between their data strategy and business objectives by developing a cohesive data strategy and roadmap. This involves identifying the necessary data inventory to achieve platform goals and addressing gaps between existing and required data. Strategies may include prioritizing data acquisition based on business needs and coordinating with individual business units to integrate data collection into their product and service launches. The challenge lies in strategically planning data acquisition rather than treating it as incidental to existing processes.

What internal API strategies succeed in platform organizations for data interoperability? How do they transition to centralized data governance?

Successful implementation of internal APIs within platform organizations involves establishing functional interfaces and communication protocols to enable seamless coordination across organizational units. These APIs facilitate data interoperability and porosity, allowing every business unit dealing with user data to act as both provider and customer. Organizations manage the transition to centralized data governance by incentivizing units to contribute to and benefit from a common central data pool. Examples of such implementations showcase how internal APIs drive internal data porosity and enable bidirectional data flow across external interactions.

How do platform organizations tackle challenges in adopting API-centric infrastructure, including interface specification, data interoperability, and governance?

Platform organizations face several challenges when implementing an API-centered approach to building technology infrastructure. These challenges include specifying interfaces to drive internal data interoperability, managing data governance, addressing privacy concerns, and overcoming resistance to change from individual business units. Strategies to mitigate these challenges involve establishing clear data governance rules, providing training and support for data integration, and ensuring compliance with data usage regulations and standards. Additionally, organizations aggregate data expertise and pool data centrally to integrate existing isolated data pools into a common central data pool, ensuring that future data collection aligns with central data governance principles.