Challenges of Managing Information Systems

Question
The Challenges of Managing Information Systems: Explore the challenges of managing information systems. Discuss issues such as system integration, data quality, and user adoption.

Answer
1. System Integration
System integration is the process of connecting different sub-systems within the whole system in order to maximize functionality of the system. By working on their coordination of each sub-system, they can be developed with maintaining their autonomy but also can be integrated to work together to serve the organization’s overall purpose. The goal of system integration is to not just share data, but to enhance the integrated organization’s performance. An important benefit of system integration is the ability for different systems to easily access and exchange information. The main problem faced is ensuring that the relevant data can be available on the new platform once it has been accessed. Data integration is a precursor to system integration; system integration is broader in scope in comparison to data integration. Integration of a new system to systems already present in the organization has caused a greater need for ETL (Extract, Transform, Load) tools to migrate data, as well as a data warehouse environment to facilitate the necessary data transformation and provide the integrated system with visible access to the data it requires. In the evolution of IS/IT technologies, the integration method has evolved from old custom coding methods to the more reusable option by using middleware technology. It has been another challenge to ensure different systems and middleware platforms can be integrated. System integration is an essential phase in more complex automation systems. Failure to integrate can cause delays in development or unnecessary additional costs.
1.1. Interoperability challenges
This often results in a situation where point-to-point integration is used with a custom-built interface, but this approach has been heavily criticized as costly and high in coupling between the integrated systems.
The most pressing interoperability issue lies with newer systems being deployed, as these will eventually become the legacy systems of the future, and so businesses will want to leverage the existing IT infrastructure. This creates a need for temporal interoperability, the ability for systems to exchange data and use the services of other systems, but in a way that can cope with future changes to those systems or deactivation of the system.
It has been suggested that due to the high level of complexity present in modern systems, achieving fully interoperable systems may, in fact, be infeasible. This is due to the difficulty of modeling and creating a standard for every automated business process that can be implemented by different systems but still allow meaningful data interchange.
Interoperability, the ability of a system to share data and services with other systems, is the crux of system integration. An absence of widely accepted system-interconnection standards and the related trust between organizations has made achieving system interoperability very difficult. This, in turn, has led to a situation where systems are very brittle and exhibit a low grace of failure. As a result, the cost of ownership of the system increases as organizations find themselves maintaining and remediating the same issues.
1.2. Legacy system compatibility
Legacy systems refer to systems that are considered outdated or obsolete. These systems are often proprietary and require just a few people to maintain them. Other times they are highly customized to perform specific functions for a particular business or organization. Legacy systems may not be replaced simply because they are critical to the business and the cost of replacement is too high to justify the implementation of a new system. Therefore, in these cases, the new system must be compatible with the old. This can pose major problems for both the company implementing the new system and the vendor providing it.
A) When firms attempt to integrate their systems with those of business partners or change to new software packages, they often find that the new applications either do not work together, or the business partners’ systems cannot operate with the latest technologies. The result is that firms are forced to maintain complex, costly, and extremely difficult to maintain links between systems. For example, the Australian Wool Exchange invested $8.5m for an online transaction processing system to handle the buying and selling of wool. This was to replace a system that had been in place for 40 years. However, wool brokers were unwilling to invest in the technology required to move the data from their systems to the exchange. This led to the abandoning of the project and a return to a manual process. This is a common situation for businesses. Almost every system in existence is connected with another in some form. Therefore, when a new system is implemented, it must work in tandem with the old system or the system being replaced.
1.3. Data migration issues
Major integration and development projects bring a high probability of data migration because most systems being integrated or replaced will need to maintain some level of data accessibility and functionality. However, data migration itself is one of the most challenging and critical components of the integration process. Rapid changes in technology and data structure make migration a difficult task, and failure in migration can lead to project delays or, in extreme cases, complete project failure. Data migration can most easily be described as moving data to one or more systems in an effective and efficient way in order to access and use that data when it is in the new location. It is often best to view migration as a process, rather than a single event. Usually, the process is automated, but it can involve manual steps. Direct data transfer is often the most appealing option, but there may be a need to modify data in order to match the new system’s requirements. This is a risky scenario, as altering data can lead to integrity loss, and if transfer methods are not well considered prior to the actual transfer, it can lead to much time and expense on recoding and rerunning the transfer process. If the data has a complex modern structure, it may be more efficient to rebuild the data in the new system, either by manual entry or with some form of data capture and processing. Many organizations underestimate the complexities involved in migrating data, and this is reflected in a general lack of knowledge in the area, and subsequently, data migration project failures are a common occurrence.
2. Data Quality
2.1. Accuracy and completeness
2.2. Consistency and reliability
2.3. Data governance and stewardship
2.4. Data security and privacy
3. User Adoption
3.1. Resistance to change
3.2. Training and education
3.3. User interface design
4. Information System Performance
4.1. Scalability and capacity planning
4.2. System reliability and uptime
4.3. Response time optimization
5. Information System Governance
5.1. IT strategy alignment
5.2. Risk management and compliance
5.3. IT project prioritization
6. Information System Security
6.1. Cybersecurity threats
6.2. Access control and authentication
6.3. Incident response and recovery
7. Information System Analytics
7.1. Data mining and analysis
7.2. Business intelligence tools
7.3. Predictive analytics
8. Cloud Computing and Information Systems
8.1. Cloud adoption challenges
8.2. Data sovereignty and privacy concerns
8.3. Vendor lock-in risks
9. Mobile Technologies and Information Systems
9.1. Mobile app development
9.2. Device compatibility and fragmentation
9.3. Mobile security and data protection
10. Artificial Intelligence and Information Systems
10.1. Machine learning applications
10.2. Ethical considerations
10.3. Human-AI collaboration
11. Emerging Technologies in Information Systems
11.1. Internet of Things (IoT)
11.2. Blockchain technology
11.3. Augmented and virtual reality
12. Big Data Management
12.1. Data storage and retrieval
12.2. Data processing and analysis
12.3. Data privacy and compliance
13. Knowledge Management Systems
13.1. Knowledge capture and sharing
13.2. Expertise location and retrieval
13.3. Collaboration and social networks
14. Change Management in Information Systems
14.1. Organizational change readiness
14.2. Communication and stakeholder engagement
14.3. Change implementation and evaluation
15. Project Management for Information Systems
15.1. Scope definition and requirements gathering
15.2. Resource allocation and scheduling
15.3. Risk identification and mitigation
16. IT Service Management
16.1. Incident and problem management
16.2. Service level agreements (SLAs)
16.3. Continual service improvement
17. Data Warehousing and Business Intelligence
17.1. Data extraction and transformation
17.2. Data modeling and schema design
17.3. Report generation and data visualization
18. System Development Life Cycle (SDLC)
18.1. Requirements analysis and specification
18.2. System design and prototyping
18.3. Testing and quality assurance
19. IT Governance Frameworks
19.1. COBIT (Control Objectives for Information and Related Technologies)
19.2. ITIL (Information Technology Infrastructure Library)
19.3. ISO 27001 (Information Security Management System)
20. Business Process Management and Information Systems
20.1. Process modeling and optimization
20.2. Workflow automation and orchestration
20.3. Performance monitoring and improvement