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Why and How of Building a Decision Support System

It is more than collecting, aggregating, and presenting data – Integrating the data silos for better decisions is complex than ever. Moreover, companies try to get timely access to insights and achieve a quicker turnaround with enhanced data quality.

The existing decision architectures lack capabilities for advanced analytics. They comprise several independent or disparate systems with many integration points and usually require an immense reconciliation effort.


Pain points


Decision Support System (DSS)

Our decision support system acts as a foundation for all processes across the value chain. It serves as a single source of truth for all investment decisions. Further, it supports advanced and predictive analytics capabilities like visualization techniques and smartly handles complexity in investment strategies and products.


Characteristics or Features of DSS


Illustrative NextGen Analytics Engine

The next-gen analytics engine solves the above pain points with an adaptable, scalable, and integrated architecture. It facilitates seamless interactions between the three components, including embedded API gateway, built-in DSS, and investment allocation workflow.

The underlying framework also supports and accepts data at rest, data in motion, and data in many forms. It results in minimized interaction points, streamlined business processes, and improved business decision-making.

IBM’s Point of View on Enterprise Analytics in the era of Big Data


Suggested Implementation Model

The incremental roll-out approach comprises four stages – namely discovery, strategy, design & planning, and implementation

Discovery Phase

Strategy Phase

Design & Planning Phase

Implementation Phase


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