Change management is the process of planning, implementing, monitoring, and evaluating changes to an organization's information systems and related components. Change management aims to ensure that changes are aligned with the business objectives, minimize risks and disruptions, and maximize benefits and value.
One of the key aspects of change management is measuring its effectiveness, which means assessing whether the changes have achieved the desired outcomes and met the expectations of the stakeholders. There are various indicators that can be used to measure change management effectiveness, such as time, cost, quality, scope, satisfaction, and performance.
Among the four options given, the most appropriate indicator of change management effectiveness is the number of incidents resulting from changes. An incident is an unplanned event or interruption that affects the normal operation or service delivery of an information system. Incidents can be caused by various factors, such as errors, defects, failures, malfunctions, or malicious attacks. Incidents can have negative impacts on the organization, such as loss of data, productivity, reputation, or revenue.
The number of incidents resulting from changes is a direct measure of how well the changes have been planned, implemented, monitored, and evaluated. A high number of incidents indicates that the changes have not been properly tested, verified, communicated, or controlled. A low number of incidents indicates that the changes have been executed smoothly and successfully. Therefore, the number of incidents resulting from changes reflects the quality and effectiveness of the change management process.
The other three options are not as appropriate indicators of change management effectiveness as the number of incidents resulting from changes. The time lag between changes to the configuration and the update of records is a measure of how timely and accurate the configuration management process is. Configuration management is a subset of change management that focuses on identifying, documenting, and controlling the configuration items (CIs) that make up an information system. The time lag between changes and updates of documentation materials is a measure of how well the documentation process is aligned with the change management process. Documentation is an important aspect of change management that provides information and guidance to the stakeholders involved in or affected by the changes. The number of system software changes is a measure of how frequently and extensively the system software is modified or updated. System software changes are a type of change that affects the operating system, middleware, or utilities that support an information system.
While these three indicators are relevant and useful for measuring certain aspects of change management, they do not directly measure the outcomes or impacts of the changes on the organization. They are more related to the inputs or activities of change management than to its outputs or results. Therefore, they are not as appropriate indicators of change management effectiveness as the number of incidents resulting from changes.
References:
Metrics for Measuring Change Management - Prosci
How to Measure Change Management Effectiveness: Metrics, Tools & Processes Metrics for Measuring Change Management 2023 - Zendesk

IT application owners having sole responsibility for architecture approval (B) is a major concern because it indicates a lack of oversight and segregation of duties. EA decisions should be reviewed by a cross-functional governance body to ensure alignment with security, compliance, and business objectives.
Other options:
The CIO chairing the review board (A) may indicate centralized leadership but is not inherently a risk.
EA governing non-IT projects (C) may indicate scope expansion but is not a security risk.
Security requirements being reviewed (D) is a best practice and not a concern.
Reference: ISACA CISA Review Manual, IT Governance and Management of IT

Hashing is a technique that transforms data into a fixed-length value, called a hash or a digest, that uniquely represents the original data. Hashing can be used to validate the integrity of data communicated between production databases and a big data analytics system by comparing the hash values of the data before and after the communication. If the hash values match, the data has not been altered; if they differ, the data has been tampered with or corrupted. Hashing is a better security control than encrypting, running and comparing the count function, or hosting a digital certificate for this purpose because:
Encrypting in-scope data sets can protect the confidentiality of the data, but not necessarily the integrity.
Encryption algorithms can be broken or bypassed by malicious actors, or encryption keys can be compromised or lost. Moreover, encryption adds overhead to the communication process and may affect the performance of the big data analytics system.
Running and comparing the count function within the in-scope data sets can only verify the number of records or elements in the data sets, but not the content or quality of the data. The count function cannot detect any changes or errors in the data values, such as missing, duplicated, corrupted, or manipulated data.
Hosting a digital certificate for in-scope data sets can provide authentication and non-repudiation for the data sources, but not integrity for the data itself. A digital certificate is a document that contains information about the identity and public key of an entity, such as a person, organization, or device. A digital certificate does not contain or verify the actual data that is communicated between production databases and a big data analytics system.
References:
Ensuring Data Integrity with Hash Codes
Database Security: An Essential Guide
Control methods of Database Security

Comprehensive and Detailed Step-by-Step Explanation:
To ensurecompleteness of outbound transactions, alog with periodic validationis thebest control.
* Option A (Incorrect):Edit checkshelp withdata accuracy, not completeness.
* Option B (Incorrect):Sequential numberingdetects missing transactions but does not verifyactual transmission.
* Option C (Incorrect):Recipient ID validationis important foraccuracy, not for ensuring all transactions are sent.
* Option D (Correct):Maintaining and validating transaction logsensures thatall outbound transactions are properly accounted for, making it the best completeness control.
Reference:ISACA CISA Review Manual -Domain 3: Information Systems Acquisition, Development, and Implementation- Coversdata integrity, completeness controls, and transaction logging.