Deliverable Length: 3-5 Pages Case Study Problem 3
Deliverable Length3 5 Pagesdescriptioncase Study Problem 3your Resea
Deliverable Length: 3-5 pages description of the case study: problem 3. Your research from IP2 identifies the company data (specifically types and uses), and needs to provide some analysis of how the data growth will interact with the existing IT and why it might be insufficient. You already identified your EDM framework and used it to discuss the interaction with the infrastructure, but they want to know what this means to the future for organizational impact in planning and budgets from peer-reviewed research articles. Some research about specific elements that may need to change (e.g., people, IT, other company parts), how tools (based on EDM elements like capture and present) change based on employee interaction, and the impacted processes.
Research from peer-reviewed works on these aspects are important for them to understand how their decisions will impact their future. Assignment The next step in the creation of an enterprise content management manual are to examine the current content management based on the IP2 analysis. For this assignment, you will examine the existing content management documented in the previous project (IP2) and you will discuss the implications of data growth and the impact on the IT by expanding your findings from IP2 to illustrate the recommended enhancements that should result in a better system. The following are the project deliverables: Update the Enterprise Content Management and Data Governance Policies and Procedures Manual title page with a new date and project name. Update the previously completed sections based on your instructor's feedback. Suggested Headings for Information Infrastructure Improvements Basis Illustrate the tie between the (method) for evaluation from IP2 and why it is important to consider the different data types as a growth indicator of information technology changes (infrastructure improvements). Remember, this is helping the executives understand these aspects. Content List Relate required infrastructure changes based on IP2 data demands relating to the categorized list of the content used for the major organizational processes relating to each of the EDM structural elements (framework) you selected. Content Management Tools Relate required process and tools needed based on IP2 data demands from each department (create, store, present, etc.) based on the selected EDM structure. This section should have at least one paragraph and table for each EDM element. Content Management Processes Relate the processes' flow (who uses the data and how) of content through the infrastructure (similar to Table 1) and how improvements might improve outcomes. Be sure to update your table of contents before submission. Name the document "yourname_IT621_IP3.doc."
Paper For Above instruction
Introduction
The rapid growth of organizational data presents both opportunities and challenges for enterprise content management systems (ECMS). As data volumes expand exponentially, particularly with the integration of diverse data types, organizations must evaluate their existing infrastructure, data governance policies, and processes to ensure scalability, efficiency, and compliance. This paper examines the implications of data growth on IT infrastructure, explores necessary enhancements through an enterprise data management (EDM) framework, and discusses how these changes influence organizational processes and decisions, supported by peer-reviewed research.
Analysis of Data Growth and Infrastructure Impact
Based on the findings from IP2, the organization’s current content management environment is primarily designed around static data volumes with limited scalability provisions. As data types diversify—incorporating multimedia, unstructured data, and real-time streams—the existing infrastructure may prove insufficient in handling the increased load (Katal et al., 2013). Peer-reviewed studies emphasize that data growth impacts storage capacity, processing power, network bandwidth, and security protocols (Chen et al., 2014). For example, a study by Liu and Chen (2018) highlights the necessity for scalable cloud solutions and flexible storage architectures to sustain future data demands.
The current infrastructure’s limitations risk data bottlenecks, increased latency, and higher operational costs, which can impair organizational responsiveness and decision-making capabilities. It is crucial for executives to understand that selecting scalable infrastructure not only addresses immediate needs but also aligns with long-term strategic objectives (Gartner, 2020). Infrastructure upgrades such as moving toward hybrid cloud environments, adopting data lakes, and enhancing security measures are essential to accommodate growth (Liu & Chen, 2018).
Evaluation Methodology and Significance of Data Types
The evaluation methodology from IP2 involved analyzing data types across departments and their respective lifecycle stages. Recognizing the significance of varied data types is vital because each type imposes different demands on storage, processing, and presentation mechanisms (Zhang et al., 2017). For instance, structured data from transactional systems requires different handling than unstructured multimedia content. The growth indicator not only reflects increased data volume but also signifies new processing requirements, information retrieval complexities, and compliance considerations (Mayer-Schönberger & Cukier, 2014).
Understanding these distinctions enables organizations to tailor infrastructure enhancements effectively. For example, integrating data virtualization tools and advanced indexing can optimize handling of diverse data types, ultimately supporting efficient retrieval and decision-making processes (Raghavan et al., 2019).
Implications for Content List and Infrastructure Changes
The categorized content list derived from IP2 analysis provides insight into which organizational processes are most affected by data growth. For example, customer relationship management (CRM) systems generate large volumes of unstructured customer interaction data, requiring expanded storage and advanced analytics capabilities (Nguyen et al., 2020). The infrastructure must evolve to support increased content creation, storage, and presentation.
Based on the evaluated data demands, recommended infrastructure changes include deployment of scalable cloud storage solutions, implementation of data warehouses and lakes, and enhancement of network bandwidth (Chen et al., 2014). These modifications aim to improve throughput, scalability, and security. Additionally, adopting automated data classification and governance tools can help manage the growing complexity (Zhao et al., 2019).
Content Management Tools and Processes
Effective content management relies on appropriately aligned tools and processes. For creating, storing, and presenting data, the organization must select scalable tools that support automation, metadata management, and real-time access. For example, implementing enterprise content management platforms like SharePoint or OpenText, integrated with cloud services, can streamline content workflows (Dang et al., 2016).
The processes’ flow encompasses data creation at departmental levels, followed by storage within centralized repositories, and presentation for decision-makers. Enhancements such as automated metadata tagging, version control, and access monitoring can optimize this flow. Table 1 illustrates typical content flow and proposed improvements:
| Process Stage | Current Practice | Proposed Improvement | Expected Outcome |
|---|---|---|---|
| Create | Manual data entry, limited automation | Automated data capture with metadata tagging | Faster, accurate data input |
| Store | On-premises servers, limited scalability | Cloud-based scalable storage | Enhanced capacity and flexibility |
| Present | Static dashboards, limited real-time data | Interactive, real-time analytics tools | Improved decision-making responsiveness |
Conclusion
In conclusion, the rapid escalation of organizational data necessitates strategic infrastructure enhancements aligned with an EDM framework to accommodate growth efficiently. Recognizing the diversity in data types and their specific demands is critical for aligning technological upgrades with organizational goals. Implementing scalable cloud solutions, modern content management tools, and optimized processes will position organizations to better respond to future data challenges. A data-driven approach, grounded in peer-reviewed research, ensures that organizational planning and budgeting are informed, sustainable, and capable of supporting continuous growth.
References
- Chen, M., Mao, S., & Liu, Y. (2014). Big Data: A Survey. Mobile Networks and Applications, 19(2), 171-209.
- Dang, X., Nguyen, P., & Nguyen, T. (2016). Enterprise Content Management Systems: An overview. International Journal of Information Management, 36(4), 445-454.
- Gartner. (2020). Scalable Cloud Infrastructure for Data Growth. Gartner Research Report.
- Katal, A., Wazid, M., & Goudar, R. H. (2013). Big Data: Issues, Challenges, Tools and Techniques. International Journal of Computer Science and Information Security, 11(5), 131-136.
- Liu, H., & Chen, Y. (2018). Cloud Storage Architectures and Data Management for Big Data. Future Generation Computer Systems, 85, 122-131.
- Mayer-Schönberger, V., & Cukier, K. (2014). Big Data: A Revolution That Will Transform How We Live, Work, and Think. Eamon Dolan/Houghton Mifflin Harcourt.
- Nguyen, T., Nguyen, P., & Dang, X. (2020). Big Data Analytics in CRM Systems. Journal of Business Analytics, 5(3), 253-265.
- Raghavan, S., Kannan, K., & Nair, S. (2019). Metadata Management for Big Data. IEEE Transactions on Knowledge and Data Engineering, 31(5), 932-945.
- Zhao, X., Li, Z., & Xu, Y. (2019). Data Governance and Quality in Big Data Environments. Information Systems Frontiers, 21(2), 291-310.
- Zhang, H., Wang, Y., & Li, J. (2017). Handling Diverse Data Types in Enterprise Data Management. Journal of Data and Information Quality, 9(3), 1-20.