A lifecycle can seem like a series of projections: birth, growth, peak growth, slowed growth, even slower, slowest, death. Products go through this. The technologies underlying those products go through this.

A lifecycle is a stochastic process. It can be a Bayesian process, or it can be a Markovian process. The process can be imposed and enforced, or learned. A Bayesian process enforces a previously established process. A Markovian process discovers new states in a process in an ongoing manner.

Grammars can be prescriptive, or descriptive. Prescriptive grammars tell you the correct way to do things. Descriptive grammars capture use or novelty. A grammar usually does both, or it fades away. If English didn’t exhibit novelty we would be speaking a different language.

Bayesian processes are prescriptive. Markovian processes are descriptive.

Lifecycles, like the English language, will exhibit novelty, so it is both Bayesian and Markovian. Lifecycles are grammars.

Beyond a grammar or a process, a lifecyle is a network. In the recent past networks were considered to consist of nodes and links. Recently, hubs were added to the definition of networks, so now networks consist of hubs, nodes, and links. Hubs are central nodes that have many more links than other nodes. Hubs are stable. Nodes change often. Links used to be considered to be associative by theorists, but fixed in implementation. Even XSLT has yet to facilitate richer linking. Mathematicians see links as being random. In biochemistry, randomness facilitates links, but containers structure the space of the linking improving the probability of a link, so containers play a role in a network.

Lifecycles and their process states act as containers, structuring the networks associated with a product. These networks associated with products include feature networks, user and customer populations and subpopulations, concept or ontology networks, and content networks. Some of this is malleable, some of it is stable. Some of it will be hubs. Some of it will be links. Some of this will be predictable; some volatile. Knowing which is which is key.

Lifecycles are clocks as well, asynchronous clocks. So beyond knowing which network components are stable and which are volatile, knowing when a state will change, when the clock will tick is the other key.

The lifecycle becomes the geography over which you will build your technology platform plan and product roadmap. Your populations will be distributed over that geography. The populations define the lifecycle, or the lifecycle defines the populations–you decide. But, statistical aggregates will muddy your populations.

Can you describe your product’s lifecycle? Can you validate that description?


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