Discussion: More Data Are Collected, Stored, And Processed

Discussionas More And More Data Are Collected Stored Processed And

Discussion: As more and more data are collected, stored, processed, and disseminated by organizations, new and innovative ways to manage them must be developed. Discuss how the data resource management methods of today will need to evolve as more types of data emerge. Why is the role of a data steward considered innovative? Explain. Discussion Continued: When it comes to telecommunication network installation and management, as a business manager, you have to make a crucial decision to choose between hiring an expert to do the work or do it yourself. Which choice is better, and why? What criteria should be considered in making that decision?

Paper For Above instruction

Introduction

The exponential growth of data in recent years has necessitated the evolution of data resource management methods. As organizations amass diverse data types, from structured databases to unstructured multimedia, the frameworks and strategies for managing these resources must adapt to ensure efficiency, security, and usability. The role of a data steward has emerged as a critical component of this transformation, exemplifying innovative approaches to data governance. Simultaneously, operational decisions such as the choice between outsourcing telecommunication network installation or managing it internally reflect strategic considerations that affect organizational performance and resource allocation.

The Evolution of Data Resource Management

Traditional data management techniques predominantly focused on structured data stored in relational databases, with emphasis on data accuracy and access control. However, the current data landscape is characterized by increasing volume, variety, and velocity—often termed the '3Vs' of Big Data (Laney, 2001). Managed effectively, these diverse data sources include not only transactional data but also social media feeds, sensor outputs, and multimedia content, each requiring specialized storage and processing solutions (Chen, Mao, & Liu, 2014). This complexity demands that organizations transition toward more flexible, scalable, and intelligent management infrastructures.

Innovative data management solutions such as cloud-based data lakes enable the storage of raw data, unlocking analytical potential without the constraints of predefined schemas (Zikopoulos et al., 2012). Furthermore, the integration of artificial intelligence (AI) and machine learning algorithms supports real-time data processing, anomaly detection, and predictive analytics, fostering more responsive data ecosystems (Mayer-Schönberger & Cukier, 2013). As data types evolve, so must management strategies—adopting modular architectures, adopting data governance frameworks compatible with cloud and hybrid environments, and emphasizing data lineage and provenance to maintain trustworthiness (Kannan & Palanisamy, 2018).

The Role of the Data Steward as an Innovative Concept

The role of a data steward has gained prominence due to the increasing complexity of data governance in contemporary settings. A data steward acts as an entity responsible for ensuring data quality, compliance, and proper usage within an organization (Gordon & Redman, 2018). This role is innovative because it formalizes accountability in data management, bridging technical and business domains. It introduces a proactive approach—rather than merely managing data reactively, data stewards develop policies, standards, and procedures that promote data integrity and usability (Ladley, 2012).

Moreover, data stewards facilitate a culture of data literacy, empowering various departments to leverage data responsibly. They serve as champions of emerging data privacy regulations such as GDPR and CCPA, ensuring organizational compliance and mitigating risks (Purdy & Dai, 2019). By fostering transparency and trust in data assets, data stewards innovate on traditional data management roles, making data governance more dynamic, collaborative, and aligned with organizational objectives.

Decision-Making in Telecommunication Network Installation and Management

For a business manager facing the choice of hiring an expert versus managing telecommunication network installation independently, an analytical decision-making process is essential. The decision hinges upon factors such as cost, expertise, time constraints, and strategic importance of the network. Hiring an expert is often advantageous due to specialized knowledge, reduced risk of errors, and faster deployment. Experts bring experience with the latest technologies and regulatory standards, potentially resulting in better performance and future scalability (Wang et al., 2020).

Conversely, managing the installation internally might be cost-effective if the organization possesses in-house technical skills, has long-term control needs, or seeks to develop internal capabilities. This choice might also foster greater understanding and customization tailored to specific business requirements (Narasimhan & Nair, 2019). Ultimately, criteria such as cost-benefit analysis, availability of skilled personnel, project complexity, timeline, and strategic priorities should guide the decision. In scenarios requiring rapid deployment and high reliability, outsourcing to experts often presents a safer and more efficient option.

Conclusion

The accelerating proliferation of diverse data types demands that organizations adopt more flexible, intelligent, and governance-oriented management practices. The emergence of roles like data stewards exemplifies innovative governance models aimed at ensuring data quality, compliance, and strategic value. Meanwhile, operational decisions like telecommunications installation exemplify broader strategic considerations—balancing expertise, cost, control, and risk. Navigating these evolving challenges requires careful analysis and adaptation to sustain organizational growth in a data-driven world.

References

  • Chen, M., Mao, S., & Liu, Y. (2014). Big Data: A Survey. Mobile Networks and Applications, 19(2), 171–209.
  • Gordon, M., & Redman, T. (2018). Data Governance: How to Design, Deploy, and Sustain an Effective Data Governance Program. McGraw-Hill.
  • Kannan, P., & Palanisamy, R. (2018). Data Lake Architecture for Big Data Analytics. In Proceedings of the International Conference on Data Engineering and Computer Communication Technologies.
  • Ladley, D. (2012). Data Governance: How to Design, Deploy, and Sustain an Effective Data Governance Program. Morgan Kaufmann.
  • Laney, D. (2001). 3D Data Management: Controlling Data Volume, Velocity, and Variety. Gartner Conference.
  • Yitzhak Zikopoulos, C., et al. (2012). Managing Data in Motion: Data Strategies for Today’s Intelligent Digital Business. McGraw-Hill.
  • Mayer-Schönberger, V., & Cukier, K. (2013). Big Data: A Revolution That Will Transform How We Live, Work, and Think. Eamon Dolan/Houghton Mifflin Harcourt.
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  • Wang, Y., et al. (2020). Outsourcing vs. In-House Network Management: A Cost and Performance Perspective. Journal of Network and Systems Management, 28, 1–20.

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