Module 2 Review Questions And Assignment Recap 

Module 2 Review Questions This Module Review assignment requires you to answer


This assignment involves answering complex questions related to the topics presented in the module readings, aimed at developing your critical thinking skills. Use the course textbook and outside primary sources to support your answers. Follow the guidelines in the Module Review Questions Requirements section, including using current APA style and pasting the questions in bold before each answer.


Questions:


    

  1. Companies create business records of many types and store the electronic files using an electronic records management (ERM) system. Explain why an ERM is a senior management issue and not simply an IT issue?
    2. 

  2. Describe each of the four V’s of data analytics: variety, volume, velocity, and veracity.
    3. 

  3. Identify the primary functions of a database and a data warehouse. Explain why enterprises need both of these data management technologies.
    4. 



Your document submission is due by the posted due date. Your instructor will use the rubric for evaluating and grading your submission. Save your assignment using a naming convention that includes your first and last name and the activity number (or description). Do not add punctuation or special characters.


Paper For Above instruction


Electronic Records Management (ERM) systems are pivotal in organizational data governance, underscoring the strategic importance of data stewardship at the senior management level. While technically managed within the IT department, ERM’s implications extend far beyond mere technical parameters, touching on compliance, corporate governance, risk management, and strategic decision-making. This necessitates active involvement from senior executives who can prioritize data management initiatives, allocate resources effectively, and ensure alignment with organizational goals.


Firstly, ERM systems facilitate the systematic creation, classification, storage, retrieval, and disposition of electronic records, ensuring that organizational data is accurate, accessible, and compliant with legal and regulatory standards (Dyar & Greaney, 2020). The strategic importance lies in the fact that data is an organization’s valuable asset, affecting legal liability, operational efficiency, and reputation. Therefore, senior management must oversee ERM to embed data governance into the organizational culture, establish policies, and monitor compliance to mitigate risks associated with data loss, mismanagement, or breaches (Kang & Kim, 2021).


Moreover, senior leadership’s involvement ensures that ERM initiatives support broader business objectives such as operational resilience, legal defensibility, and competitive advantage (Sutherland & Abraham, 2019). For example, in regulated industries like healthcare or finance, non-compliance with data retention and privacy laws can lead to hefty fines and legal consequences, emphasizing the need for executive oversight (Verizon, 2021). Consequently, ERM cannot be relegated solely to IT, which typically manages technical infrastructure; instead, it requires strategic direction and accountability from top management to foster a culture of data integrity and security.


The four V’s of data analytics—variety, volume, velocity, and veracity—are foundational concepts that define the nature of big data. The 'variety' refers to the diverse types and sources of data, including structured data from relational databases and unstructured data such as social media posts, images, and sensor data (Gandomi & Haider, 2015). Managing heterogeneity requires advanced analytics tools capable of integrating and analyzing multiple data types to glean meaningful insights.


'Volume' pertains to the vast quantities of data generated daily, driven by digital platforms, IoT devices, and enterprise operations. The ability to analyze large datasets enables organizations to identify patterns, trends, and anomalies that would be invisible in smaller samples (Manyika et al., 2011). Conversely, 'velocity' measures the speed at which data is created, processed, and analyzed, demanding real-time or near-real-time data processing capabilities to support timely decision-making (Gartner, 2020).


Finally, 'veracity' relates to the trustworthiness, accuracy, and quality of data. Data with high veracity is reliable and can be used confidently in analytics; poor data quality can lead to flawed insights, misguided strategies, and operational risks (Katal, Wazid, & Goudar, 2013). Maintaining data veracity involves rigorous data validation, cleaning processes, and context-aware analysis to ensure integrity.


Databases and data warehouses serve essential yet distinct functions within enterprise data management frameworks. A database is a structured collection of data managed through DBMS (Database Management System) software, designed primarily for transaction processing—storing, retrieving, updating, and deleting day-to-day operational data (Coronel & Morris, 2015). It supports routine business operations such as order processing, payroll, and customer relationship management (CRM).


In contrast, a data warehouse is a large, centralized repository tailored for analytical processing and reporting. It consolidates data from multiple sources, including operational databases, external data, and historical data, transforming it into a format suitable for complex queries and analytics (Inmon, 2005). Data warehouses facilitate decision support by enabling business intelligence activities like trend analysis, forecasting, and strategic planning.


Enterprises need both these data management technologies because they serve complementary purposes. Operational databases ensure the smooth running of daily activities by supporting real-time transactions. Meanwhile, data warehouses provide a platform for historical data analysis, driving insights that inform strategic initiatives. Together, they enable organizations to operate efficiently and make informed, data-driven decisions (Kimball & Ross, 2013).


In summary, ERM is a strategic, top-management concern vital for organizational compliance and governance. The four V’s of data—variety, volume, velocity, and veracity—highlight the complexities and opportunities of big data analytics. Finally, databases and data warehouses are integral components of enterprise data architecture, fulfilling distinct roles that collectively enhance operational efficiency and strategic insight.


References
  • Coronel, C., & Morris, S. (2015). Database Systems: Design, Implementation, & Management. Cengage Learning.
  • Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144.
  • Gartner. (2020). Real-time analytics and data processing. Gartner Reports.
  • Inmon, W. H. (2005). Building the Data Warehouse (4th ed.). Wiley.
  • Kang, D., & Kim, S. (2021). Data governance and strategic management: An integrated approach. Journal of Data Management, 12(3), 45-60.
  • Katal, A., Wazid, M., & Goudar, R. H. (2013). Big data: Issues, challenges, tools, and practices. Journal of Big Data, 2(1), 3.
  • Kimball, R., & Ross, M. (2013). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling. Wiley.
  • Dyar, L., & Greaney, T. (2020). Managing Electronic Records in the Digital Age. Records Management Journal, 30(2), 147-163.
  • Sutherland, J., & Abraham, J. (2019). Data governance in the modern enterprise: Strategy and implementation. Harvard Business Review.
  • Verizon. (2021). Data Privacy and Security in Financial Services. Verizon Data Privacy Report.

Related Assignments