Successful context-aware decisions are possible by integrating context-aware services with collaboration tools that help enterprises deliver outstanding customer service, share ideas, and resolve issues.
There is always space for improvement in context discovery and automated decision making. Establishing a post-decision feedback loop and critically evaluating the decision comes under the purview of successful context-aware decisions. With the help of machine learning, the feedback analysis can be also automated.
The pattern of context discovery for each decision should be tagged and recorded. To evaluate the success of the decision, the calling application should look for ways that feedback the rating to the context data service. The service should have two interfaces exposed to the deciding application in order to facilitate this process: The call for the context data and the call to communicate back the success rate. The models to improve outcomes and the work of analyzing algorithms should be done outside of the deciding application. A custom code or a call to an external service is behind the interface. Human data scientists perform the feedback analysis and then adjust the models used in context data service. The earning in algorithmic decisions and the feedback loop may change over time - from simple to advanced. The operating model remains fundamentally the same but the interfaces may be request driven or event driven.
In the realm of mainstream applications, most business logics that support automated decision making are not externalized to dedicated services. It is rigid, not conducive to continuous improvement, and difficult to change since it is mixed up with the rest of the application logic code. When external data brokers like Experian are included, their use is direct and not extensible. To reduce lock-in of the application with external resources, external call is wrapped into an internal API. Moreover, for post-decision feedback loop or machine learning, there are no provisions. Therefore, a strong leadership is required to move towards a systematic approach for context-aware algorithmic business decisions in applications.
The consequences of bad decisions should also be taken into consideration. When the outcome of a bad decision is evaluated, system becomes aware of a failure. To compensate for a bad decision, a separate service that subscribes to the feedback event can be developed. The service may for instance, replenish the bank account that suffered due to an error or send an apology email to a customer. Thus, presence of an automated decision is strategic though the required course of action is specific to the application and the decision. Thereafter, systems will update the CIOs and the company’s leadership that the automation of the decision is applied with maximum attention to control the outcomes.
Today, refinement of decision automation metadata and analysis of decision outcomes is mostly done by data scientists. But with increase in investment in machine learning, strategic application planners can expect significant ability to automate the whole decision life cycle loop.
Naveen is the Founder and CEO of Allerin, a software solutions provider that delivers innovative and agile solutions that enable to automate, inspire and impress. He is a seasoned professional with more than 20 years of experience, with extensive experience in customizing open source products for cost optimizations of large scale IT deployment. He is currently working on Internet of Things solutions with Big Data Analytics. Naveen completed his programming qualifications in various Indian institutes.