ICT5151 Data and Information Management Report 4 Sample
Assessment Detail
Data are vital to business. Whether it is easy to access real-time data or the availability of advanced technology to process and analyse these data, enterprises are trying to make the most of this precious raw material. Data management in today’s world needs a modern approach to effectively handle the complex problems of dealing with multiple data formats.
Data management is the process of collecting, sorting, arranging, and managing vast amounts of data to help businesses derive valuable insights from it. From data governance to data architecture and security, modern data management is a multifaceted discipline. But following the modern data management approach is not easy, especially for enterprises that are used to traditional business methods.
In your report you need to research on the modern data management approach in an enterprise.
You need to research and explain the following topics:
• What are structured and semi-structured data?
• Explain pros and cons of structured and semi-structured data.
• Research typical use cases (applications, business areas, information systems) for structured and semi-structured data
• Explain the necessity of data management for every business.
• Explain Data Management in the following areas:
o Data Security
o Data Destruction
o Data Warehousing and business intelligence management
o Document and record storage
o Data governance
o Data quality management
• Research and explain modern data architecture components and characteristics (infrastructure architecture, parallel and distributed processing, scalability, open data access, automation, elasticity, intelligence, governance etc.).
• Research the modern data architecture of a selected Cloud service provider. For example, you can consider Amazon Web Services (AWS), Google Cloud, Microsoft Azure etc.
Your report should contain the following sections:
1. Cover page
2. Table of contents
3. Abstract
4. Introduction
5. Background to study: literature review, and how your study fits in
6. Methods: how the study was carried out, and how data were analysed
7. Discussion of results
8. Conclusion
9. References
Solution
Introduction
With immense data growth represented by today's business environment, it has moved into an important asset which serves as a driving force that fosters innovation, information competition and strategic decision within enterprises. The pace of the exponential data growth and development of technology not only need but require a modern outlook to the data management in order to use it for maximum efficiency. This report deals with complicated details of modern data management in enterprises, giving a special thought to structured and semi-structured data, security, and governance, and making a distinction between the data components within modern data architecture. The report undertakes to discuss these and other issues with the aim of giving an understanding of the problems, the recommended practices, and the overall implications for businesses in a world which is increasingly driven by data.
Background to Study
Structured and Semi-Structured Data- Characteristics, Pros and Cons
Typical Use Cases for Structured and Semi-Structured Data
Structured and semi-structured data find application across various industries and business areas, each leveraging their unique characteristics for specific use cases-
Structured Data Use Cases:
• Financial Services: In banking and insurance, structured data is applied broadly for transaction processing, insurance premium calculation, and for ensuring compliance to regulations (Sahal et al., 2023).
• Healthcare: The structured data in electronic health records (EHRs) is vital in that it helps clinicians to diagnose illnesses and store patient information more efficiently which ensures effective management of medical histories.
• Retail: Structured data helps in stock keeping, report generation and customer relationship management (CRM) systems which in turn produce personalized marketing and specific promotions especially.
• Manufacturing: The Enterprise Resource Planning (ERP) systems, which make use of structured data for the tasks such like the supply chain management, production planning and quality control, also use this kind of data (Rudenko, 2022).
• Telecommunications: Primitives-based system ensures billing systems, network monitoring, and customer service operations work efficiently, as well as provides correct billing (Azad et al., 2020).
Semi-Structured Data Use Cases:
• Social Media Analysis: Through half-establishing data from platforms like Twitter and Facebook, like, sentiment analysis, trend detection, and customer feedback, are performed for university assignment help.
• IoT (Internet of Things): Generated data from sensors, i.e. temperature sensors or smart meters, usually is semi-structured and is used to do maintenance without interference, control assets and environmental monitoring (IBM Cloud Education, 2021).
• Log File Analysis: Semi-structured log messages from a web servers, applications and network infrastructures collected for the purpose of troubleshooting, security auditing and achieving optimal performance.
• Content Management: The XML and JSON documents, having a semi-structured format, can be employed in content management systems for handling, searching and retrieving digital contents.
• E-commerce Recommendation Systems: Snippets of non-structured data that come from the browsing and buying behavior of the users, are employed to offer one-on-one recommendations and boost the interaction (Ryen et al., 2022).
Necessity of Data Management for Every Business
Data management is indispensable for every business, regardless of its size or industry, due to several compelling reasons.
• Informed Decision-Making: Organizations ensure that they have accurate, timely and sound information if they have good data management system. Through data organization and utilization of its systemized collections, decision makers will be enabled to use data-based information to make the best choice on strategic and operational decisions (Qi, 2020). This extends to several ways such as scanning for market trends, recognizing customer interests, channeling resources, and incurring risks.
• Regulatory Compliance: The activities of enterprises are driven by the complex grid of the regulations that aim to ensure the confidentiality, integrity, and correct use of data. Data management processes guarantee that entities are without violating any regulations like General Data Protection Regulation (GDPR), Healthcare Insurance Portability and Accountability Act (HIPAA), and Sarbanes-Oxley Act (SOX). From the standpoint of avoiding expensive penalties and dealing with legal repercussions, these regulations should be strictly observed as a sign of reliability and credibility among customers and stakeholders.
• Customer Satisfaction: Data management is one of the tendents, where customer expectations can be understood and met properly. This is through targeting customers, tracking customer behaviors, anticipating their needs, and appending their data into a single profile. This expands customer loyalty, retention, and advocacy as well, generating additional income with an enhanced position in the market (Janssen et al., 2020).
• Risk Management: Data management practices involve measurers to make sure data integrity, confidentiality, and available are at the highest level. Through tight and strict protocols of security with providing backups and disaster recovery plans, businesses lower the risks of data breaches, cyberattacks, and operational disruptions. Proactive risk management boosts the confidence level among stakeholders and assures quality brand and reputation of the company.
• Enhanced Efficiency and Productivity: Data management turns out to be the heart of business through its incredible role of reducing manual efforts, minimizing errors and cutting out the possibility of repetition. Via a centralized architecture of data repositories, gather information faster, partner with others fluidly and do tasks in a better way. It results in higher productivity, agility and adaptation reactiveness to the nature of the markets (Ghasemaghaei and Calic, 2020).
Data Management
.png)
Modern Data Architecture Components and Characteristics
Modern data architecture encompasses various components and characteristics designed to meet the evolving needs of data-driven enterprises:
• Infrastructure Architecture: Modern data architecture will rely on “scalable and flexible infrastructure components” which can include “cloud computing resources, on-premises data centers, and hybrid solutions”. Atlas Cluster offers well-optimized infrastructure components scaling up to high performance, reliability, and costs ensuring normal workloads operation and various storage formats (Mumuni and Mumuni, 2022).
• Parallel and Distributed Processing: Parallel and distributed processing methods allow the proper solution of huge amounts of data using breakthrough of tasks into many pieces that are processes at the same time using groups of nodes or clusters. This way, the processing speed, scalability, and fault tolerance factors, which is an important issue for real time analytics, and batch processing, respectively, are increased.
• Scalability: Scale is essential feature of any modern data architecture; it makes systems to evolve smoothly with growing data volumes, user loads, and processing capacity. Scalability obtained via horizontal scaling (starting multiple servers or nodes) or vertical scaling (adding resources to the already-existing servers) will be performed, and the system will maintain its performance and availability in the face of varying workloads (Eckerson, 2022).
• Open Data Access: Modern data architecture is developed with open access in mind, by isolating the data sources and applications, integrating them into different platforms and allowing them to interoperate perfectly. Open protocol and API (Application Programming Interface) with an easily-exchangeable data formats can facilitate the implementation of cross-supply chain collaboration easily and innovatively.
• Automation: Automation is central to data management process, is a way to save labor and making more efficient process. Developed data architecture deploys automation via engines and flowlines compassing data ingestion, processing, cleaning and integration increasing implementation speed and giving users freedom to perform analytics by themselves (Shakir et al., 2021).
• Governance: Governance is adopted by data architecture as one of the foundational elements, and it includes policies, processes, and controls that give data its integrity, security and compliance. Data governance frameworks outline roles, responsibilities, standards, data-access control, and metadata management, to coordinate the management of data across its lifecycle; this framework builds trust, transparency, and accountability in the data-driven decisions.
Modern Data Architecture of Amazon Web Services (Aws) Cloud Service Provider
Methods
The method of research was secondary relying on the data collection and analysis gathered from the available sources. The next step of the research is the investigation of the scientific literature, journals, industry publications, and online resources about modern data management practices at enterprises. The research process started with narrowing the scope by identifying key topics, data types, and key research questions such as structured and semi-structured data, data security, data governance, and modern data architecture aspects. To accumulate a diversified collection of sources from reputable sources like (e.g., PubMed, IEEE Xplore, Google Scholar), and other official documentation of the providers like (e.g., AWS) was performed using a thorough search strategy (Newman and Gough, 2020).
Data were organized systematically and in accordance with the research plan and the topics and questions which were defined ahead. All the information from different sources were digested and examined for common trends, themes and good business data management practices. The analyst had to classify and compose the most important points, compare different opinions and suggest options for improvement or possible additional investigations. By relying on the secondary research method, I was able to build basic information about the intricate problems of the management of data in enterprises. By using the existing knowledge and varied resources as the study base, the article was in a position to give a straightforward overview on the modern communication architectural components' characteristics, details, and their significance for organizations (Newman and Gough, 2020).
Discussion of Results
The results discussion elaborates on primary taken of the research on an efficient performance of data management systems in companies. It covers the whole process of handling with the determined trends, issues, and standards in the data mastery, security, governance and modern data architecture. The will discuss data security and governance processes as vital to avoidance of both non-compliance and loss of trust by data users. The architecture components mentioned here as modern are scalability, elasticity and automation which enable agility and efficiency in data management processes. Nonetheless, the discussion examines the relevance of the results in terms of management in organizations by highlighting the need for companies to adopt total data management that embraces people, processes, and technology as whole. Additionally, it is a reminder of the continuous nature of adaptation and innovation as well as an industry; it must adapt to changing data management issues and seize opportunities that are presented to in a digital world.
Conclusion
Modern data management in enterprises plays a crucial role in the organization's survival in the digital world. Data structure, semi-structure of data and strong data security, governance, and modern data architecture underpin workable data utilization and decision making. The complexity of data management in businesses is growing; however, comprehensive data management strategies are more and more required. Through adapting to contemporary data management approaches, enterprises can benefit from the comprehensive utilization of their data asset, nourishing the innovation and staying competitive in the data-driven business world.
References
Agrawal, D., Das, S. and El Abbadi, A., 2022. Data Management in the Cloud. Springer Nature.
Amazon Web Services, Inc. (2024). adidas: Building a streaming analytics application with AWS Analytics. [online] Available at: https://aws.amazon.com/big-data/datalakes-and-analytics/ [Accessed 26 Apr. 2024].
Azad, P., Navimipour, N.J., Rahmani, A.M. and Sharifi, A., 2020. The role of structured and unstructured data managing mechanisms in the Internet of things. Cluster computing, 23, pp.1185-1198.
Bharadiya, J.P., 2023. A comparative study of business intelligence and artificial intelligence with big data analytics. American Journal of Artificial Intelligence, 7(1), p.24.
Bigelow, S.J. and Hefner, K. (2024). data destruction. [online] Storage. Available at: https://www.techtarget.com/searchstorage/definition/data-destruction#:~:text=Data%20destruction%20is%20a%20critical,subject%20to%20data%20lifecycle%20
management. [Accessed 26 Apr. 2024].
Diène, B., Rodrigues, J.J., Diallo, O., Ndoye, E.H.M. and Korotaev, V.V., 2020. Data management techniques for Internet of Things. Mechanical Systems and Signal Processing, 138, p.106564.
Eckerson, W. (2022). Ten Characteristics of a Modern Data Architecture. [online] Eckerson Group. Available at: https://www.eckerson.com/articles/ten-characteristics-of-a-modern-data-architecture [Accessed 26 Apr. 2024].
Ghasemaghaei, M. and Calic, G., 2020. Assessing the impact of big data on firm innovation performance: Big data is not always better data. Journal of business research, 108, pp.147-162.
Hyatt, L. (2022). Let’s Architect! Modern data architectures | Amazon Web Services. [online] Amazon Web Services. Available at: https://aws.amazon.com/blogs/architecture/lets-architect-modern-data-architectures/ [Accessed 26 Apr. 2024].
IBM Cloud Education (2021). Structured vs. Unstructured Data: What’s the Difference? [online] IBM Blog. Available at: https://www.ibm.com/blog/structured-vs-unstructured-data/ [Accessed 26 Apr. 2024].
Janssen, M., Brous, P., Estevez, E., Barbosa, L.S. and Janowski, T., 2020. Data governance: Organizing data for trustworthy Artificial Intelligence. Government information quarterly, 37(3), p.101493.
Mumuni, A. and Mumuni, F., 2022. Data augmentation: A comprehensive survey of modern approaches. Array, 16, p.100258.
Newman, M. and Gough, D., 2020. Systematic reviews in educational research: Methodology, perspectives and application. Systematic reviews in educational research: Methodology, perspectives and application, pp.3-22.
Qi, C.C., 2020. Big data management in the mining industry. International Journal of Minerals, Metallurgy and Materials, 27(2), pp.131-139.
Rudenko, E. (2022). What Is the Difference Between Structured and Unstructured Data – NIX United. [online] NIX United – Custom Software Development Company in US. Available at: https://nix-united.com/blog/structured-vs-unstructured-data-differences-and-use-cases/ [Accessed 26 Apr. 2024].
Ruslin, R., Mashuri, S., Rasak, M.S.A., Alhabsyi, F. and Syam, H., 2022. Semi-structured Interview: A methodological reflection on the development of a qualitative research instrument in educational studies. IOSR Journal of Research & Method in Education (IOSR-JRME), 12(1), pp.22-29.
Ryen, V., Soylu, A. and Roman, D., 2022. Building semantic knowledge graphs from (semi-) structured data: a review. Future Internet, 14(5), p.129.
Ryen, V., Soylu, A. and Roman, D., 2022. Building semantic knowledge graphs from (semi-) structured data: a review. Future Internet, 14(5), p.129.
Sahal, R., Breslin, J.G. and Ali, M.I., 2020. Big data and stream processing platforms for Industry 4.0 requirements mapping for a predictive maintenance use case. Journal of manufacturing systems, 54, pp.138-151.
Shakir, A., Staegemann, D., Volk, M., Jamous, N. and Turowski, K., 2021, July. Towards a concept for building a big data architecture with microservices. In Business information systems (pp. 83-94).
Stedman, C. and Vaughan, J. (2022). What is data management and why is it important? [online] Data Management. Available at: https://www.techtarget.com/searchdatamanagement/definition/data-management [Accessed 26 Apr. 2024].
Taleb, I., Serhani, M.A., Bouhaddioui, C. and Dssouli, R., 2021. Big data quality framework: a holistic approach to continuous quality management. Journal of Big Data, 8(1), p.76.
Wittig, A. and Wittig, M., 2023. Amazon Web Services in Action: An in-depth guide to AWS. Simon and Schuster.
Would you like to schedule a callback?
Send us a message and we will get back to you
Highlights
Earn While You Learn With Us
Confidentiality Agreement
Money Back Guarantee
Live Expert Sessions
550+ Ph.D Experts
21 Step Quality Check
100% Quality
24*7 Live Help
On Time Delivery
Plagiarism-Free
81 Isla Avenue Glenroy, Mel, VIC, 3046 AU
