Cognitive Data Analyzer for Enterprise Agility Recommendations
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Solution Overview
Problem
Businesses face challenges in identifying and assessing improvement opportunities due to information overload and imperfect processes, particularly in modernizing technology and reimagining processes, which conventional solutions struggle to address effectively.
Innovation Solution
A system and method utilizing a cognitive data analyzer to compute agility performance parameters, including contextual and affinity factors, from structured and unstructured performance data, to recommend improvement opportunities in enterprise operations, leveraging a set of benchmark values and historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional solutions track defects to generate KPIs, then quality metrics can be measured, but the processes remain imperfect and improvement opportunities are missed
Solution Approach 1:
The system implements a feedback mechanism where performance data is continuously analyzed against benchmark values, and improvement recommendations are fed back to the enterprise operations. The cognitive data analyzer processes performance data and generates recommendations that are returned to the operations team, creating a closed-loop system that continuously improves processes rather than just measuring defects.
Solution Approach 2:
The system performs preliminary analysis of performance data to identify potential improvement opportunities before defects occur. By analyzing performance data proactively and comparing it with benchmark values, the system predicts and recommends improvements in advance, preventing imperfect processes from deteriorating rather than reacting to defects after they occur.
2Productivity
If teams focus on voluminous and repetitive work, then operational tasks are completed, but time for technology modernization and process improvement is insufficient
Solution Approach 1:
The system enables self-service by automatically analyzing performance data and generating improvement recommendations without requiring dedicated time from teams for manual analysis. The cognitive data analyzer processes performance data autonomously, freeing teams to focus on operational tasks while the system handles the analytical work of identifying improvement opportunities.
Solution Approach 2:
The system acts as an intermediary between operational work and improvement initiatives. It automatically processes performance data and translates it into actionable recommendations, serving as a bridge that allows teams to maintain focus on operational tasks while still benefiting from continuous improvement analysis without allocating dedicated time for either function.
3Adaptability or versatility
If a recommender system provides recommendations based on qualitative metrics, then improvement opportunities are identified, but the system complexity increases
Solution Approach 1:
The system achieves universality by using a unified cognitive data analyzer that handles multiple types of performance data (structured and unstructured) and generates recommendations across different enterprise operations. This single multi-functional component replaces what would otherwise require multiple specialized systems, reducing overall complexity while maintaining adaptability across various business contexts.
Solution Approach 2:
The system manages complexity by dynamically adjusting parameters such as benchmark values and performance thresholds based on historical data and contextual factors. Rather than requiring complex rule-based systems for each scenario, the system adapts its analysis parameters to different situations, simplifying the overall architecture while maintaining high adaptability to various improvement opportunities.
Data Source
AI summary
This disclosure relates generally to method and system for recommending improvement opportunities in enterprise operations. Due to recent advancement, cognitive business operations face challenges in identifying new business opportunities. The present disclosure receives statistics about performance data as inputs from each business operations to identify gaps of improvement specific to the context using an agility recommender technique. The received performance data are analyzed using a cognitive data analyzer comprising a structured data and an unstructured data which is an indicative factor of enterprise operations agility. The agility recommender technique computes the contextual factor based on a plurality of contextual parameters, a contextual intercept, and a coefficient of the contextual intercepts. Further, a set of improvement opportunities are determined to recommend the enterprise operations based on a plurality of agility performance parameters deviation identified from the set of performance data compared with historical data.