Order ID 53563633773 Type Essay Writer Level Masters Style APA Sources/References 4 Perfect Number of Pages to Order 5-10 Pages Description/Paper Instructions
Instructions:
Respond to the post below with any inputs or suggestions.• All posts (both initial and responses) must be substantial (several paragraphs each) and each of your initial posts must be supported by 3 peer reviewed or authoritative sources, not including the textbook, cited properly in APA format. Responses should have proper support with at least 1 different source as applicable.
Currently, many companies from different sectors are looking for ways to exploit their data in order to improve their operations and consequently obtain greater benefits from them. Over the years, the data processed by organizations and companies have experienced a substantial increase in volume, speed, and variety, so selecting the appropriate technology to process it and take advantage of the knowledge generated by it is crucial in the future of these entities.
Artificial Intelligence (AI) has great potential to solve a wide spectrum of real-world business problems, but the lack of trust from the perspective of potential users, investors, and other stakeholders towards AI is preventing them from adoption. To build and strengthen trust in AI, technology creators should ensure that the data which is acquired, processed, and being fed into the algorithm is accurate, reliable, consistent, relevant, bias-free, and complete. Similarly, the algorithm that is selected, trained, and tested should be explainable, interpretable, transparent, bias-free, reliable, and useful. Most importantly, the algorithm and its outcomes should be auditable and properly governed. (Srinivasan, et al., 2020)
Companies need to understand their desired outcomes to select the right data. Then they need to know exactly where the data comes from, how it was collected and what it represents. That information is key to understanding the context of such data and building trust in the quality of the data being employed in strategy and planning. (Rizzo, 2019)
The payoff to an effective data management strategy is the value of the trusted information. This information can provide advantageous competitive insight, enable sophisticated business performance management, increase employee productivity and satisfaction, and deliver a superior customer experience. The ultimate benefit will be visible in revenue growth, improved operational efficiency, increased customer and employee retention, and increased profitability. (Sidi, et al., 2013)
We need to demonstrate integrity, honesty, and transparency as to what happens to data and what level of control people can, or cannot, expect. We must embed ethical rigour in all our data-driven processes. We must guarantee that data analysis and storage are not compromised by data breaches that reveal personal information. Sanctions for such breaches must be clear with meaningful effect. Development of solutions whereby security is achieved in concurrent layers is required: reducing data travel, separating personal identifiable data from payload data, using effective anonymization and encryption methods. (Lawler, et al., 2018)
The organizations should take a systematic approach to trust that spans the lifecycle of analytics and is founded on four key anchors of trust: quality, effectiveness, integrity, and resilience. (KPMG, 2016)
Quality:The initial trust in the data depends primarily on its quality. In order to drive quality in Data &Analytics, organizations need to ensure that both the inputs and development processes for D&A meet the quality standards that are appropriate for the context in which the analytics will be used. In many organizations, questions are raised about choice of data sources and data ‘lineage’ (i.e., where the data originated and what process it took to arrive as input data to a system or decision engine). (KPMG, 2016)
There are many examples of inadvertent quality issues which have had massive knock-on impacts for individuals, organizations, markets, and whole economies. And as analytics move into critical areas of society, such as decision engines for drug prescribing, machine learning ‘bots’ as personal assistants, and navigation for autonomous vehicles, it seems clear that D&A quality is now a trust anchor for everyone. (KPMG, 2016)
Effectiveness:
It means that the outputs of models work as intended and deliver value to the organization. This is the top concern of those who invest in D&A solutions, both internal and external to the organizations. (KPMG, 2016)
Organizations that are able to assess and validate the effectiveness of their analytics in supporting decision-making can have a huge impact on trust at board level. The corollary of this, of course, is that organizations that invest without understanding the effectiveness of D&A may not move the needle on trust or value at all. (KPMG, 2016)
Integrity:
Data without integrity–that is not complete, accurate, and consistent–is not very useful. Standards and governance rules provide a disciplined approach to managing business processes and source applications, reinforcing the “truth” and enabling the seamless sharing of data. (Sidi, et al., 2013)
If algorithms are well ‘trained’, then race or gender biases, for example, can be removed. It stands to reason, therefore, that an effective combination of human and machine can offer fairer, more trusted decisions. If not well managed throughout the D&A lifecycle, algorithms can also introduce unintentional, hidden biases as a consequence of the data on which they have been trained.Automated decision engines can also make the ethical consequences feel emotionally distant to the humans who are nominally accountable. For example, board members may blame misbehavior on a rogue algorithm or claim they could not possibly understand the detail of complex models, and therefore absolve themselves of responsibility. (KPMG, 2016)
Resilience:
Resilience in this context is about optimization for the long term in the face of challenges and changes. Failure of this trust anchor undermines all the previous three. (KPMG, 2016)
Basic resilience is key to winning customer trust. It only takes one service outage or one data leak for consumers to quickly move to (what they perceive to be) a more secure competitor. It also only takes one big data leak for the regulators to come knocking and for fines to start flying. (KPMG, 2016)
Before ending my post, I would like to present the example of Facebook, whose biggest challenge has been to gain the trust of its users.From the start, Facebook attempted to win our trust by showing us they took privacy seriously. The fact there was at least an illusion of privacy was enough to get a lot of people on board the social media revolution. By default, anything a user shared was shared only with a trusted group of friends, It also offered switches allowing individual aspects of a person’s data to be made public or private. (Marr, 2016)
Facebook has revolutionized the way we communicate with each other online by allowing us to build our own network and choose who we share information about our lives with.This data holds tremendous value to advertisers, who can use it to precisely target their products and services at people who are, according to statistics, likely to want or need them. (Marr, 2016)
Gaining the trust of users is essential. Aside from data thefts and such illegal activity, users can become annoyed simply by being subjected to adverts they aren’t interested in, too frequently. So, it’s in Facebook’s interests, as well as the advertisers, to match them up effectively. (Marr, 2016)References:
KPMG (2016). Building Trust in Analytics. https://assets.kpmg/content/dam/kpmg/xx/pdf/2016/10/building-trust-in-analytics.pdf
Lawler, M., Morris, A. D., Sullivan, R., Birney, E., Middleton, A., Makaroff, L., Knoppers, B. M., Horgan, D., Eggermont, A. (2018). A roadmap for restoring trust in Big Data. Lancet Oncology, 19(8), 1014-1015. http://dx.doi.org/10.1016/S1470-2045(18)30425-XMarr, B. (2016). Big data in practice: How 45 successful companies used big data analytics to deliver extraordinary results. ProQuest Ebook Central https://ebookcentral.proquest.com
Rizzo, M. (2019, May 30). Implementing A Modern Data-Driven Digital Strategy. Mondaq Business Briefing. https://link.gale.com/apps/doc/A587117657/GPS?u=lirn99776&sid=GPS&xid=151c5827
Sidi, K. N., Hutchinson, D. A. (2013, September-October). The trusted information payoff: productivity, performance, and profits. Information Management Journal, 47(5), 35+.https://link.gale.com/apps/doc/A352038803/GPS?u=lirn99776&sid=GPS&xid=2b6d773b
Srinivasan, A. V., de Boer, M. (2020). Improving trust in data and algorithms in the medium of AI. Maandblad voor Accountancy en Bedrijfseconomie [MAB], (3/4), 147+. https://link.gale.com/apps/doc/A621598415/GPS?u=lirn99776&sid=GPS&xid=2a00bad3
RUBRIC
QUALITY OF RESPONSE NO RESPONSE POOR / UNSATISFACTORY SATISFACTORY GOOD EXCELLENT Content (worth a maximum of 50% of the total points) Zero points: Student failed to submit the final paper. 20 points out of 50: The essay illustrates poor understanding of the relevant material by failing to address or incorrectly addressing the relevant content; failing to identify or inaccurately explaining/defining key concepts/ideas; ignoring or incorrectly explaining key points/claims and the reasoning behind them; and/or incorrectly or inappropriately using terminology; and elements of the response are lacking. 30 points out of 50: The essay illustrates a rudimentary understanding of the relevant material by mentioning but not full explaining the relevant content; identifying some of the key concepts/ideas though failing to fully or accurately explain many of them; using terminology, though sometimes inaccurately or inappropriately; and/or incorporating some key claims/points but failing to explain the reasoning behind them or doing so inaccurately. Elements of the required response may also be lacking. 40 points out of 50: The essay illustrates solid understanding of the relevant material by correctly addressing most of the relevant content; identifying and explaining most of the key concepts/ideas; using correct terminology; explaining the reasoning behind most of the key points/claims; and/or where necessary or useful, substantiating some points with accurate examples. The answer is complete. 50 points: The essay illustrates exemplary understanding of the relevant material by thoroughly and correctly addressing the relevant content; identifying and explaining all of the key concepts/ideas; using correct terminology explaining the reasoning behind key points/claims and substantiating, as necessary/useful, points with several accurate and illuminating examples. No aspects of the required answer are missing. Use of Sources (worth a maximum of 20% of the total points). Zero points: Student failed to include citations and/or references. Or the student failed to submit a final paper. 5 out 20 points: Sources are seldom cited to support statements and/or format of citations are not recognizable as APA 6th Edition format. There are major errors in the formation of the references and citations. And/or there is a major reliance on highly questionable. The Student fails to provide an adequate synthesis of research collected for the paper. 10 out 20 points: References to scholarly sources are occasionally given; many statements seem unsubstantiated. Frequent errors in APA 6th Edition format, leaving the reader confused about the source of the information. There are significant errors of the formation in the references and citations. And/or there is a significant use of highly questionable sources. 15 out 20 points: Credible Scholarly sources are used effectively support claims and are, for the most part, clear and fairly represented. APA 6th Edition is used with only a few minor errors. There are minor errors in reference and/or citations. And/or there is some use of questionable sources. 20 points: Credible scholarly sources are used to give compelling evidence to support claims and are clearly and fairly represented. APA 6th Edition format is used accurately and consistently. The student uses above the maximum required references in the development of the assignment. Grammar (worth maximum of 20% of total points) Zero points: Student failed to submit the final paper. 5 points out of 20: The paper does not communicate ideas/points clearly due to inappropriate use of terminology and vague language; thoughts and sentences are disjointed or incomprehensible; organization lacking; and/or numerous grammatical, spelling/punctuation errors 10 points out 20: The paper is often unclear and difficult to follow due to some inappropriate terminology and/or vague language; ideas may be fragmented, wandering and/or repetitive; poor organization; and/or some grammatical, spelling, punctuation errors 15 points out of 20: The paper is mostly clear as a result of appropriate use of terminology and minimal vagueness; no tangents and no repetition; fairly good organization; almost perfect grammar, spelling, punctuation, and word usage. 20 points: The paper is clear, concise, and a pleasure to read as a result of appropriate and precise use of terminology; total coherence of thoughts and presentation and logical organization; and the essay is error free. Structure of the Paper (worth 10% of total points) Zero points: Student failed to submit the final paper. 3 points out of 10: Student needs to develop better formatting skills. The paper omits significant structural elements required for and APA 6th edition paper. Formatting of the paper has major flaws. The paper does not conform to APA 6th edition requirements whatsoever. 5 points out of 10: Appearance of final paper demonstrates the student’s limited ability to format the paper. There are significant errors in formatting and/or the total omission of major components of an APA 6th edition paper. They can include the omission of the cover page, abstract, and page numbers. Additionally the page has major formatting issues with spacing or paragraph formation. Font size might not conform to size requirements. The student also significantly writes too large or too short of and paper 7 points out of 10: Research paper presents an above-average use of formatting skills. The paper has slight errors within the paper. This can include small errors or omissions with the cover page, abstract, page number, and headers. There could be also slight formatting issues with the document spacing or the font Additionally the paper might slightly exceed or undershoot the specific number of required written pages for the assignment. 10 points: Student provides a high-caliber, formatted paper. This includes an APA 6th edition cover page, abstract, page number, headers and is double spaced in 12’ Times Roman Font. Additionally, the paper conforms to the specific number of required written pages and neither goes over or under the specified length of the paper. GET THIS PROJECT NOW BY CLICKING ON THIS LINK TO PLACE THE ORDER
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