Account Health Assessment, Risk Identification, and Remediation

• US 20150142506A1
• Filed: 11/18/2013
• Published: 05/21/2015
• Est. Priority Date: 11/18/2013
• Status: Abandoned Application

• Current Assignee: Kyndryl Incorporated
• Original Assignee: International Business Machines Corporation
• Inventors: Rao, Suryanarayana K., Kumar, Asheesh, Pathak, Ramesh Chandra
• Application Number:
  US14/082,427
• Publication Number:
  US 20150142506A1
• Time in Patent Office: Days
• US Class Current: 705/7.28
• CPC Class Codes: G06Q 10/0635 Risk analysis of enterprise or organisation activities

Abstract

A method and system for determining account health, identifying and rating hidden and visible risks, and identifying remediation actions in response to identified risks and as a means to improve account health scores is provided. The method includes retrieving metrics associated with a customer account of a customer. Aggregated metrics from the metrics and additional aggregated metrics are generated and stored. Weighting factors are applied to the aggregated metrics and the additional aggregated metrics. Attributes of events and symptoms of incidents are modeled to identify best fit & possible root causes. In response, overall health & risk scores for the customer account are calculated

Claims

1. A method comprising:

retrieving, by a computer processor of a computing system from a plurality of sources, metrics associated with a customer account of a customer;

generating, by said computer processor, aggregated metrics from said metrics with respect to said plurality of sources;

generating, by said computer processor, additional aggregated metrics from metrics associated with additional accounts of said customer, wherein said additional aggregated metrics are aggregated with respect to additional sources;

storing, by said computer processor within a repository data storage warehouse, said aggregated metrics and said additional aggregated metrics;

retrieving, by said computer processor, said aggregated metrics and said additional aggregated metrics;

applying, by said computer processor executing a weighting engine, weighting factors to said aggregated metrics and said additional aggregated metrics, wherein said weighting factors are associated with criticality and importance factors; and

calculating, by said computer processor based on said weighting factors applied to said aggregated metrics and said additional aggregated metrics, overall health and risk scores for said customer account and said additional accounts with respect to specified platforms and additional platforms, wherein said overall health and risk scores are associated with specified time periods.

2. The method of claim 1, further comprising:

determining, by said computer processor, incident metrics of said aggregated metrics and said additional aggregated metrics;

matching, by said computer processor, said incident metrics and associated issues to incident data of an incident database;

determining, by said computer processor based on a historical analysis, previously modified metrics associated with said customer account and said additional accounts;

determining, by said computer processor based on results of said matching and said previously modified metrics, recommended metrics.

3. The method of claim 2, further comprising:

extracting, by said computer processor, specified incident metrics of said incident metrics, wherein said specified incident metrics are associated with a specified endpoint of a plurality of endpoints of said plurality of sources.

4. The method of claim 3, further comprising:

matching, by said computer processor, a metric pattern of said specified incident metrics to associated risks of said customer account;

rating, by said computer processor, said associated risks with respect to corrective actions; and

generating, by said computer processor based on results of said matching and said rating, associated actions and recommendations.

5. The method of claim 4, further comprising:

aggregating, by said computer processor, said associated risks based on technology, said customer, a business domain, a system, subsystems, an application, and an environment;

automatically identifying, by said computer processor based on said aggregating, available best practices and solutions for said associated risks; and

performing , by said computer processor, a percentage fitment analysis and feasibility analysis with respect to said available best practices and solutions for said associated risks.

6. The method of claim 3, further comprising:

matching, by said computer processor, said specified incident metrics to a skill level of said customer;

identifying, by said computer processor based on said specified incident metrics, missing skills of said skill level with respect to said customer; and

generating, by said computer processor based on results of said matching and said identifying, recommendations for obtaining skills of said missing skills.

7. The method of claim 1, further comprising:

determining, by said computer processor, values and ranges of values for each metric of said aggregated metrics and said additional aggregated metrics;

generating, by said computer processor based on said values and ranges of values, categories for groups of metrics of said aggregated metrics and said additional aggregated metrics;

determining, by said computer processor, weighting scales for each said metric; and

determining, by said computer processor based on said weighting scales, relative weighting scales for each said metric.

8. The method of claim 1, further comprising:

determining, by said computer processor, levels of dependencies between endpoints of a plurality of endpoints of said plurality of sources; and

determining, by said computer processor based on said levels of dependencies, root causes of said specified incidents.

9. The method of claim 1, wherein said plurality of sources comprise sources selected from the group consisting of a plurality of endpoints of specified platforms, applications, tools, processes, documents, databases, middleware, operating systems, storage arrays, backup servers, network components, and SAN.

10. The method of claim 1, further comprising:

tracking, by said computer processor via a data warehouse, a performance history with respect to progress of a risk mitigation process and a health improvement process with respect to said customer account, an associated technology area, application group, and a business domain.

11. The method of claim 1, further comprising:

tracking, by said computer processor via a data warehouse, a performance history with respect to progress of a risk mitigation process and a health improvement process with respect to systems, subsystems, applications, middleware, and additional dependent components and subcomponents.

12. The method of claim 1, further comprising:

determining, by said computer processor, incident markers for specified incidents associated with sources of said plurality of sources;

determining, by said computer processor, related sources of said plurality of sources;

extracting, by said computer processor from said related sources, a first group of metrics;

applying, by said computer processor, said first group of metrics and said incident markers to a plurality of non-linear models; and

determining, by said computer processor, based on results of said applying said first group of metrics and said incident markers, root causes of said specified incidents.

13. The method of claim 1, further comprising:

identifying, by said computer processor based on said weighting factors applied to said aggregated metrics and said additional aggregated metrics, risks associated with said customer account and said additional accounts with respect to specified platforms and additional platforms;

assessing, by said computer processor based on results of said identifying, impacts associated with said risks; and

determining, by said computer processor based on results of said assessing, remediation actions associated with said risks.

14. The method of claim 1, further comprising:

providing at least one support service for at least one of creating, integrating, hosting, maintaining, and deploying computer-readable code in the computing system, said code being executed by the computer processor to implement;

said retrieving said metrics, said generating said aggregated metrics, said generating said additional aggregated metrics, said storing, said retrieving said aggregated metrics and said additional aggregated metrics, said applying, and said calculating.

15. A computing system comprising a computer processor coupled to a computer-readable memory unit, said memory unit comprising instructions that when executed by the computer processor implements a method comprising:

retrieving, by said computer processor from a plurality of sources, metrics associated with a customer account of a customer;

generating, by said computer processor, aggregated metrics from said metrics with respect to said plurality of sources;

generating, by said computer processor, additional aggregated metrics from metrics associated with additional accounts of said customer, wherein said additional aggregated metrics are aggregated with respect to additional sources;

storing, by said computer processor within a repository data storage warehouse, said aggregated metrics and said additional aggregated metrics;

retrieving, by said computer processor, said aggregated metrics and said additional aggregated metrics;

applying, by said computer processor executing a weighting engine, weighting factors to said aggregated metrics and said additional aggregated metrics, wherein said weighting factors are associated with criticality and importance factors; and

calculating, by said computer processor based on said weighting factors applied to said aggregated metrics and said additional aggregated metrics, overall health and risk scores for said customer account and said additional accounts with respect to specified platforms and additional platforms, wherein said overall health and risk scores are associated with specified time periods.

16. The computing system of claim 15, wherein said method further comprises:

determining, by said computer processor, incident metrics of said aggregated metrics and said additional aggregated metrics;

matching, by said computer processor, said incident metrics and associated issues to incident data of an incident database;

determining, by said computer processor based on a historical analysis, previously modified metrics associated with said customer account and said additional accounts;

determining, by said computer processor based on results of said matching and said previously modified metrics, recommended metrics.

17. The computing system of claim 16, wherein said method further comprises:

extracting, by said computer processor, specified incident metrics of said incident metrics, wherein said specified incident metrics are associated with a specified endpoint of a plurality of endpoints of said plurality of sources.

18. The computing system of claim 17, wherein said method further comprises:

matching, by said computer processor, a metric pattern of said specified incident metrics to associated risks of said customer account;

rating, by said computer processor, said associated risks with respect to corrective actions; and

generating, by said computer processor based on results of said matching and said rating, associated actions and recommendations.

19. The computing system of claim 18, wherein said method further comprises:

aggregating, by said computer processor, said associated risks based on technology, said customer, a business domain, a system, subsystems, an application, and an environment;

automatically identifying, by said computer processor based on said aggregating, available best practices and solutions for said associated risks; and

performing , by said computer processor, a percentage fitment analysis and feasibility analysis with respect to said available best practices and solutions for said associated risks.

20. A computer program product, comprising a computer readable hardware storage device storing a computer readable program code, said computer readable program code comprising an algorithm that when executed by a computer processor of a computer system implements a method, said method comprising:

retrieving, by said computer processor from a plurality of sources, metrics associated with a customer account of a customer;

generating, by said computer processor, aggregated metrics from said metrics with respect to said plurality of sources;

generating, by said computer processor, additional aggregated metrics from metrics associated with additional accounts of said customer, wherein said additional aggregated metrics are aggregated with respect to additional sources;

storing, by said computer processor within a repository data storage warehouse, said aggregated metrics and said additional aggregated metrics;

retrieving, by said computer processor, said aggregated metrics and said additional aggregated metrics;

applying, by said computer processor executing a weighting engine, weighting factors to said aggregated metrics and said additional aggregated metrics, wherein said weighting factors are associated with criticality and importance factors; and

calculating, by said computer processor based on said weighting factors applied to said aggregated metrics and said additional aggregated metrics, overall health and risk scores for said customer account and said additional accounts with respect to specified platforms and additional platforms, wherein said overall health and risk scores are associated with specified time periods.