In most scenarios, when an application is developed in-house, the data on which decision automation is based is mostly derived internally, or from a few well-structured external sources. However, with the need for newer, more innovative, and intelligent applications, the need for context awareness in business is being strongly felt.
Organizations often ignore the importance of context awareness in their business at their own risk. Especially in digital business and IoT initiatives, CIOs should all the more encourage architecture leaders to invest in systematic architectures for more contextualized algorithmic decisions in applications.
Instead of being left to human control, automated decisions are algorithmic as prescribed algorithms control them. For example, evaluation of a job candidate can be enriched by context look-ups against government sources, social media, and local news.
But at present, most of such inquiries are not performed by automated processes but by people. Via controlled workflows, packaged applications delegate consequential decisions to people. They draw on the limited context data available inside the application itself for automation processes.
External events, platforms, and locations often act as the trigger for business processes. An analysis of some external and internal context data is required to fully define the significance of the events, as content alone is not enough.
Internally collected knowledge is not sufficient for context awareness in business
The context data look-ups in local historic data stores such as master data management (MDM) store is increasingly insufficient due to the cross-application and cross platform impact of events in digital business.
For example, for successful fraud detection in a banking transaction, internally collected knowledge about a customer is not enough. Instead, for improving the quality of banking decision, researched external context data from other government agencies and social and news sources needs to be used.
Context awareness in business is sometimes expensive
Human situation awareness and guardrails are lost, when a decision is automated. Thus, to implement decision-making applications, context-aware cognitive analysis becomes a required component of modeling.
Analytical decision automation is not attempted when the cost of developing actionable context data is higher than the cost of delegating the decision to a human. However, due to the shift towards digital business and the Internet of Things (IoT), more decisions are urgent, which increases the cost incurred by a delay in a human response.
Businesses require decision automation, which demands greater digital situation awareness. Myriad opportunities are provided by modern technology for context-aware algorithmic business decisions.
For developing a systematic approach for locating and injecting external context data into decision logic of business applications, CIOs should direct enterprise architects towards developing a systematic approach.
(Disclaimer: This is a guest post submitted on Techstory by the mentioned authors. All the contents in the article have been provided to Techstory by the authors of the article. Techstory is not responsible or liable for any content in this article.)
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About The Author:
Naveen is currently the CEO at Allerin Tech Pvt Ltd. 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. Naveen is a keynote speaker and thought leader in the area of IoT solutions, Machine learning and Block Chain Technology.
Specialties: Solution Design and consultancy , Data Science, Machine Learning, Deep Learning Enterprise Application Planning, Cost Optimization and Blockchain.