Abstract: An approach for obfuscating sensitive data while preserving data usability is presented. The in-scope data files of an application are identified. The in-scope data files include sensitive data that must be masked to preserve its confidentiality. Data definitions are collected. Primary sensitive data fields are identified. Data names for the primary sensitive data fields are normalized. The primary sensitive data fields are classified according to sensitivity. Appropriate masking methods are selected from a pre-defined set to be applied to each data element based on rules exercised on the data. The data being masked is profiled to detect invalid data. Masking software is developed and input considerations are applied. The selected masking method is executed and operational and functional validation is performed.

Abstract: A method is implemented in a computer infrastructure having computer executable code tangibly embodied on a computer readable storage medium having programming instructions. The programming instructions are operable to receive disruption data for an enterprise and determine optimal selected disruption data from the received disruption data. Additionally, the programming instructions are operable to correlate triggering events of the optimal selected disruption data to one or more systemic root cause categories and identify a systemic root cause based on the triggering events of the optimal selected disruption data. Furthermore, the programming instructions are operable to determine an optimal enterprise solution set based on the identified systemic root cause and determine an optimal enterprise transformation set based on the optimal enterprise solution set.

Abstract: A method is implemented in a computer infrastructure having computer executable code tangibly embodied on a computer readable storage medium having programming instructions. The programming instructions are operable to receive disruption data for an enterprise and determine optimal selected disruption data from the received disruption data. Additionally, the programming instructions are operable to correlate triggering events of the optimal selected disruption data to one or more systemic root cause categories and identify a systemic root cause based on the triggering events of the optimal selected disruption data. Furthermore, the programming instructions are operable to determine an optimal enterprise solution set based on the identified systemic root cause and determine an optimal enterprise transformation set based on the optimal enterprise solution set.

Abstract: In one illustrative embodiment, a computer implemented method for an accelerated service delivery service is provided. The computer implemented method comprises receiving input data describing a source service model and a target service model, comparing the source service model and the target service model and, identifying differences between the source service model and the target service model to form a first set of identified differences, and responsive to an identification of differences, creating a set of transforms for the first set of identified differences. The computer implemented method further identifying differences between the set of transforms and the described target service model, to form a second set of identified differences. The computer implemented method evaluating the second set of identified differences to determine whether results are acceptable and responsive to a determination that results are acceptable, accepting the target service model.

Abstract: A method and system for obfuscating sensitive data while preserving data usability. The in-scope data files of an application are identified. The in-scope data files include sensitive data that must be masked to preserve its confidentiality. Data definitions are collected. Primary sensitive data fields are identified. Data names for the primary sensitive data fields are normalized. The primary sensitive data fields are classified according to sensitivity. Appropriate masking methods are selected from a pre-defined set to be applied to each data element based on rules exercised on the data. The data being masked is profiled to detect invalid data. Masking software is developed and input considerations are applied. The selected masking method is executed and operational and functional validation is performed.