Wave 3 – Archetypes

Getting to Market, and Transitioning from a Manual Solution -> Service-Oriented Analytics -> Self-Serve Product Platform

1) Companies that enrich and mix datasets

This includes teams leveraging their own rich new datasets, open-source datasets and existing data stores to produce never-before-captured value.

2) Disintermediation of the hardware and software stack

At the early stage, the software-hardware stack is often tightly bundled. But later on, the software evolves to become hardware agnostic (e.g. Linux); from there, the key goal is speed to scale given the initial defensibility established.

3) Network effects

Whereby (n+1) task/device/implementation inside a system provides increasing value to the system at large through developing increased data density. Extensibility, and data-driven network effects can ease the burden of a land-and-expand go to market strategy.

4) Leveraging an as-a-Service (“aaS”) business model

Robotics (RaaS), Platform (PaaS), etc models​ are shifting CapEx to OpEx. The service business model (in and out of M2ML) is critical to incentivize quality customer servicing and continuous improvement, protecting against commoditization. These models are attractive as they align interests and become 'sticky'.

5) Enabling out-of-the-box automation

And solving for speed to deploy. This includes building with minimal marginal costs of expansion in mind (e.g. an extensible architecture) and owning the customer relationship (e.g. obviating the Systems Integrator) to align incentives and avoid ballooning deployment costs.