Electronic Records Management
Electronic Record Management (ERM) is a computer system or a group of computer programs that manage and store records (Penn & Pennix, 2017). The system captures and manages all records created by different applications within the organization. ERM is designed with control access protocols to guarantee integrity. ERM ensure easy sharing of information, speedy recovery, and reduce the costs of handling and maintaining data.
Business Intelligence and Analytic is the process by which algorithms are used to analyze business data to determine the relationship that exists between data and insights or metadata as an approach to making business decisions (Holsapple, Lee-Post, & Pakath, 2014). Business analytic involves all technologies that are used in collecting and analyzing data Business Intelligence focuses the process of making an informed business decision based on the data presented from BI.
Data and text mining are terms that are used simultaneously to refer to that act of exploring data to obtain important business insights. The distinction in data mining and text mining is that data mining extract data from data centers that are often structured whereas text mining derives high-quality data from unstructured data or text like from social media (Gandomi & Haider, 2015). Linguistic of statistical techniques are used in establishing trends and patterns from text and in data different algorithms are used.
Big data analytics and data discovery is the means of exploring large, big, complex and diverse information sets to uncover trend, patterns, correlations and information that can be used in making informed business decisions (Gandomi & Haider, 2015). Data discovery performs a limited depth as exploration as compared to data analytics.
Enterprise architecture is a design of the organization structure and operation processes on how it could best achieve its objectives. The organization analyse, design and execute change in all organization functions to achieve the desired business state and ensuring that the current and the future development align with the existing business strategies (Marosin, Zee, Ghanavati, & Sepideh, 2016).
Management Information System (MIS) is a computer database of organization information that is collected and analyzed to produce regular and special reports. The MIS is, therefore, an important decision-making tool (Laudon & Laudon, 2015). Management receives the reports and uses them to successfully support operation.
Data lifecycle are a series of steps that are involved in converting raw data to information. It involves all processes and policy that govern the creation and storage of information till it becomes obsolete. Therefore, it is an overview of all the steps in effective management and preservation of data. Data principles provide guidelines on access and use of data, by establishing access rights, responsibility and confidentiality. Data principles are meant to maintain the integrity of data and ensure availability (Ngai, Gunasekaran, Wamba, Akter, & Dubey, 2017).
Cloud computing, is a technique where different computing services are offered over the internet (Sun, Zhang, Xiong, & Zhu, 2014). An organization preferred to use share computing services rather than personalized resources. Rather than using localized servers, the organization decides to store, process and manage data using a network of remote servers.
Information or data is a crucial component in an effective decision-making process. In the past year, business decision-making process inhibited by a scarcity of data. With the advancement in technology and big data, the problem is no longer scarcity of data but the too much complex data. Data and text mining assist the organization to effectively manage big data to gain real-time insightful knowledge that assists in decision making. Now businesses have the potential to analyze large textual and structured internal and external data to gain knowledge and insight that was otherwise impossible. This data allows organizations to make a more informed decision and to gain a competitive advantage (Ngai, Gunasekaran, Wamba, Akter, & Dubey, 2017).
The most dominant challenge connected to cloud computing include security and trust. The cloud architecture or model is the main cause of alarm for data security. In cloud computing, data is stored in different resources, that is, different machines, servers and networks (Sun, Zhang, Xiong, & Zhu, 2014). The way the data is scattered rises security concerns. There are increased threats to unauthorized access, data loss and malicious attacks. Some cloud computing services do not offer adequate security measures and with the shared resources and the openness of the computing system, malicious attacks like phishing and social engineering are common than when an organization invest in local infrastructure.
Moreover, it has also become difficult for organizations to trust cloud computing services. Most businesses do not fill comfortable to store sensitive information outside their organization (Avram, 2014). Besides, organizations feel that they do not have full control over their resources. The limited trust has partly been contributed by the security concerns. The best way to gain the trust and confidence of clients will be ensuring data protection and security. With the increased access to resources, cloud computing organization should enact authorization protocols, to ensure that only authorized personnel gain access to data (Sun, Zhang, Xiong, & Zhu, 2014). The cloud computing providers need to ensure that they address security problems in their solution. Nevertheless, it is equally important for organizations to study the security policies before any commitment.
Why was an EIS designed and implemented?
EIS was developed to present the executive with both internal and external data, and KPI (Key Performance Indicators) related to the needs of the organization.
What problems did executives have with the EIS?
The system provided a junk of irrelevant data and omitted relevant data needed in making management decisions. It was difficult for the executives to do an accurate comparison because the outputs were not standardized.
What were the two reasons for those EIS problems?
The problems with EIS arose because the reporting was not personalized and because the user interface was complicated.
How did the CIO improve the EIS?
The system was bettered by having data governance procedures and strategies that ensured organization-wide standardized data formats and that assisted in eliminating any inconsistency.
What are the benefits of the new IT architecture?
The new IT architecture made is effortless for the executives to modify records and reduce the cost and the time that was required to do the analysis. It also increased the utilization of EIS within the organization.
What are the benefits of data governance?
The benefits of data governance is that it enhance the decision-making process because the quality and consistency of data are guaranteed.
5.
Why is it important for Coca-Cola to be able to process POS data in near real time?
Processing in real-time allows the company to obtain time-sensitive information on the market change, competition activities and customer preferences required in making critical business decisions and in collaborative planning, forecasting and replenishing.
Coca-Cola attempted to establish a conducive customer experience by changing their data store from decentralized to centralized so that they could also increase sells. The centralized approach enables the organization to respect data sensitive data.
The trusted view of the data allows Coca-Cola to respond fast and correctly to the market situations.
The Black Book model is a decision model established by Coca-Cola to manage the marketing complexity of the orange juice. It allows the company to blend the orange juice to develop a product that is align with the market transitions.
The Black Book model permits Coca-Cola to plan for distribution in advance. The model also allows the company to quickly re-plan, to respond to changes in the environment.
References
Avram, M.-G. (2014). Advantages and challenges of adopting cloud computing from an enterprise perspective. Procedia Technology, 12, 529-534.
Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144.
Holsapple, C., Lee-Post, A., & Pakath, R. (2014). A unified foundation for business analytics. Decision Support Systems, 64, 130-141.
Laudon, K. C., & Laudon, P. J. (2015). Management information systems (Vol. 8). Prentice Hall.
Marosin, D., Zee, M. V., Ghanavati, & Sepideh. (2016). Formalizing and modeling enterprise architecture (EA) principles with goal-oriented requirements language (GRL). International Conference on Advanced Information Systems Engineering, 205-220.
Ngai, E. W., Gunasekaran, A., Wamba, F. S., Akter, S., & Dubey, R. (2017). Big data analytics in electronic markets. Electronic Markets, 27(3), 243-245.
Penn, I. A., & Pennix, G. B. (2017). Records management handbook. Routledge.
Sun, Y., Zhang, J., Xiong, Y., & Zhu, G. (2014). Data security and privacy in cloud computing. International Journal of Distributed Sensor Networks, 10(7).
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