Many companies aspire to build platforms that shape their industry, and "platform" may become one of the more overused terms in technology. Platforms bring producers and consumers of services together for mutual benefit. With a successful platform, the service producers find a market for their offerings, while the consumers have convenient access and choice of services for their business needs. The platform provider, in turn, is compensated by charging for the hosting of the services or transaction fees. In the best possible outcomes, network effects generate a self-feeding process with more services leading to more consumers which in turn attracts more service producers.
Cloud platforms are a good example of these dynamics. In fact, cloud deployments are now so compelling that higher level platforms can increasingly build on the access and reach provided by public clouds. Commercial cloud platforms are differentiated by their ability to provide and attract special purpose platforms. One recent example is the emergence of big data analytics and artificial intelligence platforms, and specialized data-driven and knowledge-based platforms, for instance for the internet of things or managed information technology (IT) services. Over time, much of the value generated through cloud will come from these types of platforms and their ecosystems.
For further discussion of what makes technology platforms successful, let’s look at their main constituent parts.
1. Consumable services are directly accessible by users (consumers) of the platform and provided by an ecosystem of service producers (developers).
2. Common services are provided by the platform owner and are reused by consumable services.
3. Common content enriches and supports consumable services. Content like best practices patterns and knowledge bases as well as adapters and connectors to data sources add value to the consumable services. Much of this common content will be provided by communities of users.
For the new breed of data-driven and knowledge-based platforms, the richness of content is perhaps the most critical element.
As an illustration, let’s look at the example of a platform for managed services like the IBM services platform with Watson.
With Watson, consumable services can be used directly by operations teams and include operational analytics like problem tickets, knowledge-based advisor tools and service management automation. Core common services like artificial intelligence (AI) functions or a DevOps pipeline are provided by the underlying cloud platform and could also include capabilities specific to managed services and their life cycle. Common content includes curated knowledge bases and their ontologies, automation runbooks, and a data lake for operational data.
Since the value of any platform clearly depends on the availability of a rich set of consumable services and content, making it attractive for developers of services and contributors of content is imperative. I believe that a data and knowledge platform encouraging ecosystem development needs to have the following capabilities:
• A robust set of open application programming interfaces (APIs) to provide access to common services and content
• Capabilities for composing and orchestrating services
• Deep integration of AI technologies and a pluggable framework for expansion
• Functions to combine privately owned data and knowledge with publicly available or licensed data and knowledge that offers users a choice about what to share and what to keep private
• Data analytics and visualization technologies that allow for self-service
• A federation mechanism for pluggable knowledge-bases for storing information that has underlying expandable ontologies
• Community support, including a mechanism for social curation of knowledge bases
• Support for agile development of content and services
• Support for third-party monetization
For organizations consuming a platform and its services, these capabilities create a basis and an incentive to use the platform for innovation, leading to acceleration through reuse. Using the platform can thus become a driver for transformation. To ensure success, you should also use a systematic approach to capture and maintain organizational knowledge that becomes engrained in the culture and processes of your organization and addresses the entire life cycle of knowledge. Additionally, you need a strong knowledge community with technical leadership that acts as stewards of the knowledge life cycle. Your platform should also reward role models and executive commitment to foster a culture of knowledge sharing and social curation of knowledge, and it should have strong metrics that demonstrate the business value of investment in the knowledge life cycle.
A platform can support this transformation, for instance through tools that deliver immediate value to practitioners, like advisor tools that allow an individual to tap into the collective knowledge of the community (potentially inside and outside their own company) and experience the value of shared knowledge, while also contributing back additional knowledge and experience. This ultimately leads to an agile and community-powered approach to the development of these tools and the supporting knowledge bases.
Bringing all these elements together significantly increases the value and growth potential for data-driven and knowledge-based platforms.
