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Homework answers / question archive / Scenario: You have recently been hired as a Chief Information Governance Officer (CIGO) at a large company (You may choose your industry)

Scenario: You have recently been hired as a Chief Information Governance Officer (CIGO) at a large company (You may choose your industry)

Computer Science

Scenario: You have recently been hired as a Chief Information Governance Officer (CIGO) at a large company (You may choose your industry). This is a newly created position and department within the organization that was founded on the need to coordinate all areas of the business and to provide governance of the information. You will need to hire for all positions within your new department. The company has been in business for more than 50 years and in this time has collected vast amounts of data. Much of this data has been stored in hard copy format in filing cabinets at an offsite location but in recent times, collected business data is in electronic format stored in file shares. Customer data is being stored in a relational database, but the lack of administration has caused data integrity issues such as duplication. There are currently no policies in place to address the handling of data, business or customer. The company also desires to leverage the marketing power of social media, but has no knowledge of the types of policies or legal issues they would need to consider. You will also need to propose relevant metrics that should be collected to ensure that the information governance program is effective. The CEO and Board of Directors have tasked you to develop a proposal (paper) that will give them the knowledge needed to make informed decisions on an enterprise-wide Information Governance program, addressing (at a minimum) all of these issues, for the company. Requirements: The paper should include at a minimum of the following sections: a. Title page b. Executive Summary (Abstract) c. Body i. Introduction (including industry discussion – 1-2 pages) ii. Annotated Bibliography (2-3 pages) iii. Literature review (2-3 pages) iv. Program and technology recommendations, including: 1. Metrics 2. Data that matters to the executives in that industry, the roles for those executives, and some methods for getting this data into their hands. 3. Regulatory, security, and privacy compliance expectations for your company 4. Email and social media strategy 5. Cloud Computing strategy d. Conclusion e. References 2. You must include at least two figures or tables. These must be of your own creation. Do not copy from other sources. 3. Must cite at least 10 references and 5 must be from peer reviewed scholarly journals (accessible from the UC Library). 4. This paper should be in proper APA format and avoid plagiarism when paraphrasing content. It should be a minimum of 8 pages in length (double-spaced), excluding the title page and references. Milestones: • • • • Total: Week 3 – Introduction Section - A 1-2 page paper describing the industry chosen and potential resources to be used. 25 pts. Week 6 - Develop a full annotated bibliography (2-3 pages). 25 pts. Week 12 – Develop the literature review (2-3 pages). 25 pts. Week 15 - Completed final research paper (all milestones combined together and include the last sections as discussed in the list above). 50 pts. 125 pts 1 Literature Review: Information Governance SIVAIAH PALETI University of the Cumberlands ITS-833-m50 Professor : Dr. Toevs Date : 07-25-2021. INFORMATION GOVERNANCE 2 Literature Review: Information Governance Basically, information governance is one of the most critical aspects of the workplace in the current workplace environment. The reason behind such is because its value in the workplace has become broad through incorporating issues such as managing corporate information, drafting, implementing roles and metrics within the business. Research has established that significant steps taken within the workforce have been courtesy of information governance (Kooper et al., 2011). The primary basis and importance of information management in the workplace have been managing resources in place of business. Managing resources in the area of work are broad. They include providing specific resources, especially when needed since that is the only time such resources might be of value. Another feature that raises the vitality of information governance and its sudden favour in the place of work includes that it helps the management reduce costs of storage, among other charges. With a proper managing system in place, it helps plan on what to keep and what to discard depending on the schedules, among other determining factors. In that line of thought, various organisations have credited information management for reducing some of the risks that come with doing business. For example, one common risk that has been reduced within the workplace includes legal risks. Legal troubles are reduced when information governance helps in making sure that all requirements in doing business are fulfilled in time and as needed. This, in part, saves costs for organizations by assisting them to avoid litigation, among other aspects. Therefore, information governance sets an organization in line with many factors in doing business, which could not be possible without a proper information governance system. In short, information governance allows the company to discover its business meaning and, in turn, place them in a better position in meeting its business objectives. Therefore, the whole impression of information governance appears to be generated from the idea of a business being in a better place to maximize their opportunities and deal with its weakness. INFORMATION GOVERNANCE 3 There are various challenges that an organization will be exposed to when incorporating information governance strategies and frameworks in the workplace. Research and studies have indicated that information governance possesses some typical problems within most businesses and enterprises in the workplace (Elizabeth, 2010). One of the common and most observed challenges that arise include compliance and regulatory issues. When having an information governance system in place, an organization is usually required to submit the information needed to approve a business engagement. This is usually a challenging task for the organization, especially when the required data is within bulk information, making it hard to find and isolate. Many organizations with an information governance system have expressed numerous nightmare scenarios that their legal teams have had to deal with to comply and pass the regulatory steps required (White Paper, 2011). Therefore, the success of the information governance system or framework, in most cases, is down to the system's efficiency in offering crucial information when needed. Other challenges that can be faced within an organization include big data and machine learning. Most organizations within the world of business have information governance systems that help them to be in a better position in predicting and forecasting major business events before they take place. However, for that to be possible, information and data within the organization have to be enormous. For that reason, studies and researches conducted on big data and machine learning have shown that there are challenges in managing such a significant amount of data for organizations. According to researchers, asserting data integrity and their sources has been an incredible challenge for many organizations that wish to use bulk information for predictive aspects of the business. This is because, while analyzing data helps predict and forecast future happenings, the accuracy of the same depends on the integrity of the information used, which has been a great challenge. INFORMATION GOVERNANCE 4 In a nutshell, information governance in the workplace is usually tailored using specific aspects of an organization. This is because each organization has its unique elements such as goals, policies, roles, and responsibilities, among many others. Therefore, based on the success of companies that utilized effective information governance frameworks, some essential features had to be used in designing a practical framework (Gianni, 2015). One of the areas that draw the utmost importance in creating an effective information governance system is the organization's policies. Policies have been the driving force in the success of an organization; thus, the need for an organization’s policies to be incorporated into the organization’s information governance system is essential. Other aspects that have been important to the success of an information governance framework have been regarding how a framework defines the process for implementing policies in place. Additionally, the successful organization has also outlined that their success has been due to having an information governance system that includes; metrics, compliances, tremendous and clear scope, management criteria, among other aspects. In conclusion, previous scholars have focused on research that highlights some of the most fundamental aspects of information governance. The importance of information governance has been at the bedrock of most researchers' work, as highlighted above. That’s not all; there has been a critical emphasis among researchers on the need for organizations to design an information governance system capable of delivering their organization goals wholesale. Aligning such aspects has been vital in the success of both the information governance system and the implementation of the organization's goals (Dai & Wardlaw, 2016). Therefore, by organizations focusing on what scholars have proven to be fundamental in the success of an information governance system, organizations will be a better place in having a successful business that meets their objectives set earlier. INFORMATION GOVERNANCE 5 INFORMATION GOVERNANCE 6 Reference Dai, Wei; Wardlaw, Isaac (2016). Data Profiling Technology of Data Governance Regarding Big Data: Review and Rethinking. Information Technology, New Generations. Advances in Intelligent Systems and Computing. 448. pp. 439–450. doi:10.1007/978-3319-32467-8_39. ISBN 978-3-319-32466-1. Elizabeth Lomas, (2010) Information governance: information security and access within a UK context. Records Management Journal, https://doi.org/10.1108/09565691011064322 Vol. . 20 Issue: Available 2, to pp.182-198, download at http://discovery.ucl.ac.uk/1543932/] Gianni, D., (2015). Data Policy Definition and Verification for System of Systems Governance, in Modeling and Simulation Support for System of Systems Engineering. Kooper, M., Maes, R., and Roos Lindgreen, E. (2011). On the governance of information: Introducing a new concept of governance to support the management of information. International Journal of Information Management, 31(3), 195-200] White Paper (2011). Ledergerber, Marcus (ed.). How the Information Governance Reference Model (IGRM)Complements ARMA International's Generally Accepted Recordkeeping Principles (PDF). EDRM and ARMA International. p. 15. Running Head: TRANSLATIONAL BIOINFORMATICS TRANSLATIONAL BIOINFORMATICS Student’s Name : SIVAIAH PALETI University of the Cumberland’s Date: May 23,2021. 1 TRANSLATIONAL BIOINFORMATICS 2 TBI (Translational Bioinformatics) is a new area in health informatics that brings together cellular metabolomics, biostatistics, computational pathology, and medical sciences. Its main objective is to apply information systems methods to biomedical and genomic data to create information and medical tools that scientists, physicians, and patients can use. Furthermore, it entails using computer-based information technology to implement biomedical research to improve human health (Altman 2012). TBI uses biomedical informatics data mining and analysis to produce clinical information for use. Finding patterns in patient groups, analyzing biological data to recommend therapy interventions, and predicting health outcomes are clinical experience examples. The key reason for choosing this industry is the vital link between data mining and how those in charge of managing information handle data. Translational bioinformatics is a field that seeks to make use of different types of biological data to convert valuable information into clinical practice. However, the massive influx of omics data makes analyzing and interpreting these data extremely difficult (Shah & Tenenbaum 2012). As a result, new efficient computational methodologies, particularly data mining approaches, are in high demand for translational bioinformatics. The major challenge in genomics is figuring out how gene variants are related to a specific disease and, in a broader sense, how gene associations change due to environmental and lifestyle factors. This field of translational bioinformatics has a lot of evidence, but it lacks molecular-level guidance in disease depiction. The development of clinically valuable malignancies is vital to translational research's progress, but few new tests have been developed to date. Transferring promising research assays to everyday laboratory practice can take years and require several repetitive and procedural steps in TRANSLATIONAL BIOINFORMATICS 3 laboratory medicine. Methodological deficiencies in animal and human pathogenesis are frequently to blame for this issue (Shah & Tenenbaum 2012). Furthermore, there is significant doubt about the results' accuracy and replication because of sample handling and pre-analytical variability. As a result, before a novel and promising diagnostic technique can be translated to daily laboratory practice, the most feasible and systematic collection procedures must be developed. A crucial stage in translational research is the preclinical proof of concept analysis. The main obstacles in planning, conducting, and interpreting preclinical studies are data heterogeneity and interpretation prejudice. The effect size must surmount the model's uncertainties to detect the consequences of mediation. Preliminary studies are particularly vulnerable to the impact of missing data; the addition or absence of a single data point can significantly alter a study's findings. As a result, a long-term strategy for dealing with missing data and deviations is critical (Altman 2012). Where appropriate, animal deaths and the management of missing data and outliers should be addressed alongside the study results. Profusion in statistical research is a problem that is often overlooked in preclinical studies. Clinically, given the apparent inter-subject variability in drug response, the conventional method of treating large populations of patients with a single approach is not well adapted to the production of microscopically targeted drugs (Shah & Tenenbaum 2012). While designing drugs with particular patient emphasis is much more complex, it can increase drug development success rates and support patients and, eventually, healthcare costs. In general, computer science expertise and skills are used in translational bioinformatics for various purposes, including DNA organization and sequence analysis, to name a few. TRANSLATIONAL BIOINFORMATICS 4 References TRANSLATIONAL BIOINFORMATICS 5 Altman, R. B. (2012). Translational bioinformatics: linking the molecular world to the clinical world. Clinical Pharmacology & Therapeutics, 91(6), 994-1000. Shah, N. H., & Tenenbaum, J. D. (2012). Focus on translational bioinformatics: The coming age of data-driven medicine: translational bioinformatics' next frontier. Journal of the American Medical Informatics Association: JAMIA, 19(e1), e2. 1 Translational Bioinformatics SIVAIAH PALETI University of the Cumberland’s Summer 2021 - Information Governance (ITS-833-M50) Faculty name : Dr. Toevs Date : June,13,2021. 2 Annotated Bibliography Qazi, S., & Raza, K. (2021). Translational bioinformatics in healthcare: past, present, and future. In Translational Bioinformatics in Healthcare and Medicine (pp. 1-12). Academic Press. Peer-reviewed research paper. Use in Methodology research. The journal explains how various biologists, chemists, computational scientists, and statisticians use a common platform to analyze the human genome. They refer to the project as The Human Genome Project. The project outcome provides the source of the new branches and provides the genomic data. They term Bioinformatics as ameba because of its irregular shape and size, thus hard to give a specific size or shape info. Bioinformatics has originated from genomic analysis and grown several branches such as immunology, network biology, epigenetics, cheminformatics, drug designing, and more. The journal explains bioinformatics germination growth in various chemical, biologic, computational sciences, physical and more. Also, it gives details on the translational bioinformatics fundamental concepts, healthcare application, scope, related ethical issues, and future perspectives. Alexander, M. S., & MD MHA, E. M. C. Ethical Analysis of Normative Biases in Data-Driven Medicine. Peer-reviewed research paper. Use in literature research. The journal defines evidencebased medicine as incorporating the patient’s values, the physician’s judgment, and the available data through decision-making philosophy. Information technology has eased information access in the 21st century, thus encouraging data-driven healthcare practices. However, over-dependence on data leads to hard times when dealing with harsh conditions whenever the data is not available. In such a situation, the physicians’ judgment becomes critical and should be confident enough when dealing with patients. However, the healthcare sector cannot entirely depend on evidence-based medicine to avoid 3 medical uncertainties like protecting physicians’ from sharing ideas on judgments. Their judgments are valuable and not ethical to deny the patients the tool. Shameer, K., Badgeley, M. A., Miotto, R., Glicksberg, B. S., Morgan, J. W., & Dudley, J. T. (2017). Translational bioinformatics in the era of real-time biomedical, health care, and wellness data streams. Briefings in bioinformatics, 18(1), 105-124. Peer-reviewed research paper. Use in literature research. The article explains translational bioinformatics today and the effectiveness of the data streams and healthcare. Precision medicine refers to treatment and prevention strategies that define various individuals. Thus precision medicine implementation requires data integration from the biomedical investigation with genomes and clinical evaluations to give the patients characteristics. However, implementation requires efforts and data integration from healthy and infected people. The community approach has improved genomic medicine implementation in the short term. The journal also explains the digital health tool adoption because many people today use the internet. These devices include sensors and other devices that individuals can put on. In addition, real-time monitoring in the data mining concept is evolving and crucial in stock trading and financial engineering. Huang, Z., Han, Z., Shao, W., Xiang, S., Salama, P., Rizkalla, M & Zhang, J. (2021). TSUNAMI: translational bioinformatics tool suite for network analysis and mining. Genomics, Proteomics & Bioinformatics. Peer-reviewed research paper. Use in a literature review. The journal article explains translational bioinformatics tools suitable for network mining and analysis. Gene coexpression network mining involves the highly correlated gene modules. The mining helps the researchers identify novel gene purpose, discover gene interactions, and extract molecular characteristics from particular groups to help identify the disease. The journal 4 explains various translational bioinformatics analyses like “weighted network coexpression analysis” used in data processing and performing intense gene co-expression networks, which takes place through ImQCM algorithm implementation. The downstream enrichment analysis involves various studies such as molecular function, biological process, genome browser, and more. The article sums up by claiming.

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