There seems to be a mystery that hovers around innovation. There are those organizations that lock away a bunch of smart people in a room or a separate unit and expect innovation to occur. Other organizations hire outside consultants to ignite the innovation fires. Still more organizations try competitive benchmarks to infuse innovation. Personally, I think a great deal of innovation comes from experimentation. Not that these other methods can’t help contribute to the solution, enlightened experimentation is a favorite approach of many a modern organization, and it is growing rapidly. This is particularly important to all of us as we contemplate our journey to becoming masters of the digital age.
In the past, testing was relatively expensive, so companies had to be careful with the number of experimental iterations. Today, however, new technologies such as computer simulation, low-code rapid prototyping, big data analytics, and cognitive computing allow companies to create better learning more rapidly. That knowledge can be easily incorporated into more experiments at less expense. Because of the lowered cost of experimentation, a major development project can employ many experiments, all with the same objective: to learn whether the product concept or proposed technical solution holds promise for addressing a new need or problem. The results can then be incorporated into the next round of tests so that the best solution can move forward. Here are some low-code ways to create opportunistic applications that leverage experimentation that allows organizations to fail fast, but avoid big mistakes:
Model Driven Creation
Many modern development platforms leverage models of all kinds to create and use processes, tasks, organization units, work queues, cases, dashboards, user interfaces, data sources or decision representations. These executable models can be used in combination to experiment with a running process or application. This allows for cooperative development of new business ideas in short order. If they are completed at a high level and tested for results, they can then be used as a skeleton to hang together the detailed components necessary for a full production solution. Innovative solutions can be cobbled together quickly and cheaply with model-driven approaches.
Composition of Components
If there is an available inventory of working components, the creation of experimental processes and applications can be sped up even further. It matters not if these components were purchased or built in the past, having a catalog of these components leverages cost and speed of development of innovative approaches. These components can be nuggets of code of various types, process snippets, graphic objects, performance panels, decision models, resource managers, APIs, and many other various components of potential reuse. In some cases, it could be a whole skeletal application of process like a supply chain framework.
Most modern development platforms also allow for the use of dropdown option lists, contextual help, and auto-completion to further speed the coding effort. This keeps the detailed code to a minimum and allows for rapid change without driving to a detailed coding level.
By leveraging these low-code approaches together, innovation can be fostered, and several teams can be working in parallel to create alternative approaches to present to the decision-makers and stakeholders. Once an approach or a combination of approaches are selected, detailed operational issues can be built in leveraging as much of the low-code approaches as possible. Incremental reconfiguration can also be used as the innovative approaches need to be tuned for production. Keep in mind that these low-code approaches can also be leveraged in the change and operation once they are in production for rapid adjustment in flight.
Digital technologies have driven down the marginal costs of experimentation, just as information technologies, of the past, have decreased the marginal costs in some production, operational services, and distribution systems. An experimental system that integrates new digital technologies does more than lower costs; it also increases the opportunities for innovation of all types.
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