Purpose and Management

Production support assists business areas across an organisation after implementing core systems or enhancements in the live environment. The support encompasses IT service desk operations, system maintenance, and minor enhancements triggered by service requests. It focuses on resolving incidents, improving IT service performance, maintaining smooth operations as part of business processes, and ensuring adherence to service level agreements (SLAs). Sponsored by the business and managed by IT service management teams, production support follows established frameworks and best practices, such as ITIL (Information Technology Infrastructure Library).

Focus Areas

Production support has critical focus areas that ensure efficient service delivery, including:

• Service request management: Handling user requests for information, advice, or standard changes to services.
• User support: Providing assistance and troubleshooting for end-users to resolve technical issues and improve user experience.
• Incident management: Addressing and resolving IT service incidents to restore normal operations quickly.
• Problem management: Identifying and resolving the root causes of incidents to prevent recurrence.
• Knowledge management: Creating and maintaining a repository of information to support IT service management processes.
• Change control: Managing the lifecycle of all changes to ensure minimal disruption to IT services.
• System maintenance: Performing regular updates, patches, and upgrades to keep IT systems running smoothly and securely.
• Service performance monitoring: Continuously track system performance to proactively identify and address potential issues.

Value Proposition

Affecting various aspects of IT operations and user experience, the success of production support relies on their timely execution. They deliver value across business areas by:

• Resolving incidents quickly to minimise downtime and disruption to business operations.
• Enhancing system performance and reliability to support ongoing business activities.

Example of a Production Support Initiative

An example of a minor initiative completed by production support is cleansing policing data in an existing RMS to improve data accuracy and quality without introducing new features or changing the system.

Project Overview

The Ironclad Police Department must enhance data integrity in its records management system. The Copper Purge initiative cleans police data while addressing security, privacy, and confidentiality sensitivities. The project aims to boost operational efficiency and uphold compliance standards by refining existing records, ultimately supporting informed decision-making.

Project Justification

Inaccurate or outdated policing data can significantly impede decision-making and operational effectiveness. Ensuring data integrity is essential for public safety and operational transparency. The Copper Purge initiative seeks to rectify these issues, enhance service delivery, and support the overall performance of IT services.

Current State Process Analysis

Analysing the existing data management processes highlights common issues, such as duplicate records, incorrect details, and outdated information. These findings justify the need for a structured data-cleansing approach.

Requirements

Requirements for the Copper Purge initiative are communicated through service tickets submitted by the business. These tickets detail specific data integrity issues, such as:

• Duplicate records: Identifying and consolidating records that represent the same entity. 
• Incorrect data points: Correcting inaccuracies in fields like names, addresses, and case numbers.
• Outdated information: Updating records no longer reflect the current status or relevant details.
• Data entry guidelines: Implementing stricter guidelines for entering data to prevent future inaccuracies.

Selecting the Technology

Requirements for the Copper Purge initiative are communicated through service tickets submitted by the business. These tickets detail specific data integrity issues, such as:

Building the Team

A cross-functional team comprising RMS administrators, data analysts, and IT support staff is assembled. This team is responsible for conducting the data audit, implementing the cleansing process, and ensuring ongoing data integrity through continuous monitoring and adjustments.

Implementation

The initiative begins with a comprehensive audit of existing policing data within Copper RMS. This assessment identifies prevalent issues, such as duplicate records and incorrect details, while ensuring compliance with privacy and security protocols. The structured data cleansing process follows, which involves:

• Removing duplicate entries: Streamlining records ensures each entity is represented only once.
• Correcting inaccurate data points: Ensuring all data is accurate and up-to-date.
• Updating outdated information: Refreshing records with current and relevant information.
• Implementing stricter data entry guidelines: Establishing best practices for data entry to minimise future inaccuracies.

Organisational Change Management

Production support initiatives like Copper Purge do not require formal training. Instead, effective communication with relevant stakeholders is established after completing the data cleansing exercise. This ensures awareness of the changes and their implications for operational processes, fostering a culture of accountability regarding data integrity.

Monitoring and Feedback

To sustain improvements, the project introduces automated tools that routinely scan the database for inconsistencies and flag potential errors for review. Metrics are established to regularly assess data quality and system performance, enabling proactive management.

Continuous monitoring ensures that new data issues are promptly identified and addressed. Feedback from RMS users is collected to identify ongoing challenges and inform future enhancements.

Outcome

Post-implementation, Copper RMS significantly improves data integrity, enhancing efficiency in policing operations. The department experiences a reduction in time spent resolving data-related issues and an increase in the accuracy of reports generated for strategic decision-making.