Data analytics, cyber capabilities, climate change, and the most recent pandemic have fundamentally altered the way insurance is disseminated around the world. For one, the uncertainty surrounding insurance clauses such as Force Majeure, have caused insurance companies to pay higher costs related to business disruptions and legal expenses. Likewise, the widespread nature of claims, in certain instances, threaten the solvency of insurance companies who did not appropriate model large business disruptions throughout the world. As a result, there is fundamental shift in the way management engages with insurance companies, how insurances companies retain clients, and how risk is effectively transferred to avoid solvency issues.To begin, the recent COVID-19 pandemic has reshaped many definitions related to insured perils. This is heavily related to insurance contracts who often did not include epidemics or pandemics within the contract. As a result, insurance contracts now include specific language related to these perils and how…...
mlaReferences
1. Dercon, S., & Krishnan, P. (2003). Risk Sharing and Public Transfers. The Economic Journal, 113(486), C86–C94. http://www.jstor.org/stable/3590049
2. Houston, D. B. (2014). Risk, Insurance, and Sampling. The Journal of Risk and Insurance, 31(4), 511–538. https://doi.org/10.2307/250806
data collection mentioned earlier. This report shall focus on the data collection in particular. Within this report, there shall be a recitation of the data collection approach, a definition of the proper data collection channels, a description and depiction of the purchase of external data sets to assist in the problem, the development and description of data quality and other data governance issues and a planning of the ongoing storage and maintenance of the data. While planning and execute data analytics is not rocket science, as they say, it is something that is very important and needs to be done right the first time.
The author of this report knows that a ten million dollar investment is not insignificant. As such, there should be a proceeding with caution yet expediency. As noted in the prior project, JC Dollar is in the right general area of analysis but is obviously not…...
mlaReferences
Arrington, S.C. (2013). The Nays Have It: Computer Fraud and Abuse Act Should Not
Apply to Employees Who Violate Employer Imposed Computer Access and Data
Use Restrictions to Steal Company Data. Journal Of Internet Law, 16(12), 3-20.
Bata, S.A., Beard, J., Egri, E., & Morris, D. (2011). Retail revenue management:
DATA ANALYTICSData AnalyticsIntroductionFrom the onset, it would be prudent to note that the relevance of deploying data analytics in the realm of human resource management cannot be overstated. This is more so the case given that data analytics could come in handy as a crucial aid to decision making. A data-driven approach to human resource management seeks to ensure that organizations are directed by factual input in their decision making efforts, as opposed to guesswork or mere intuition. In the words of Waters, Streets, McFarlane, and Johnson-Murray (2018), H analytics can improve the credibility of the H function by showing the linkage between people and business outcomes (9). This write-up concerns itself with some of the scenarios whereby analytics could be deployed from a workforce perspective. More specifically, the specific workforce areas to be highlighted are: employee turnover and employee engagement.Idea 1: Employee TurnoverEmployee turnover (which is also routinely referred…...
mlaReferencesDessler, G. (2017). Human Resource Management. Precision Higher Education. Pease, G., Byerly, B. & Fitz-enz, J. (2013). Human Capital Analytics. John Wiley & Sons. Waters, S.D., Streets, V.N., McFarlane, L. & Johnson-Murray, R. (2018). The Practical Guide to HR Analytics: Using Data to Inform, Transform, and Empower HR Decisions. SHRM.
Analytics, Interfaces, & Cloud Technology
The use of analytics and cloud technology is a new advance in computing which allows data collection and analytics to be done using higher processing speeds. It allows organizations to take full advantage of the huge amount of data that is collected through web analytics services. Miller Inc. collects a huge amount of data which is difficult to process. It also takes a very long time to process the data since the data is dynamic in nature. There is also a challenge in keeping data secure since the company needs to ensure its business and competitive advantage is protected. Through providing analytics services through cloud technology, Miller Inc. will be able to leverage the data driven frontier of cloud analytics. Basically, the company will be able to leverage the use of technological and analytical tools and techniques which it will design for its clients. These will…...
mlaReferences
Antonopoulos, N., & Gillam, L. (2010). Cloud Computing: Principles, Systems and Applications. London: Springer London.
Buyya, R., Broberg, J., & Goscinski, A.M. (2010). Cloud Computing: Principles and Paradigms. New York: Wiley.
Jamsa, K.A. (2011). Cloud Computing. Burlington, Massachusetts: Jones & Bartlett Learning.
Appendix
Analytics and the Growing Dominance of Big Data are
evolutionizing Strategic Decision-Making
The level of uncertainty and risk that pervade many enterprises today is growing, as the dynamics and economics of markets are changing rapidly. The many rapid, turbulent structural changes in industries is also leading to a greater reliance on analytics and the nascent area of Big Data as well. The potential of this second area, Big Data, is in determining patterns in massive data sets that have in many cases been collected for decades within enterprises. The abundance of data within enterprises, when combined with Big Data aggregation and analytics techniques, can be used for drastically reducing risk and uncertainty in even the most challenging and fast-moving industries. Big Data is being hyped heavily by analytics systems and enterprise application providers as well, as this category of software allows for the use of long-standing analytics and business intelligence (BI)…...
mlaReferences
Bughin, J., Chui, M., & Manyika, J.. (2010). Clouds, big data, and smart assets: Ten tech-enabled business trends to watch. The McKinsey Quarterly,(4), 26.
Malcolm Chisholm. (2009). The Dawn of Big Data: Are we on the cusp of a new paradigm that goes beyond what we can do with traditional data stores?. Information Management, 19(8), 45.
Jim Ericson. (2010). Is data science: If ultra-large scale data can reinvent military intelligence, it could change the way we see information. Information Management, 20(7), 18.
Jacobs, A.. (2009). The Pathologies of Big Data. Association for Computing Machinery. Communications of the ACM, 52(8), 36.
Analytics and Business Intelligence
Assessing the Impact of Analytics and Business Intelligence
The pervasive adoption of analytics to mitigate risk has accelerated due to greater uncertainties in economic conditions, the accelerating pace of change in markets, and a reliance on quantified measurements of performance vs. qualitatively based (Hopkins, LaValle, Balboni, Shockley, Kruschwitz, 2010). The intent of this analysis is to look at how analytics and business intelligence can be used for automating the more mundane analytical and reporting tasks while also looking into how analytics and business intelligence are making finance and accounting systems more real-time and responsive to market conditions.
Automating outine Tasks with Analytics
Computing variances between actual and forecasted amounts by account, defining financial ratios and calculating them over a multi-year timeframes and across multiple divisions is how analytics is most often used in accounting and financial reporting today. These are relatively mundane tasks that often require accountants and financial analysts…...
mlaReferences
Hopkins, M., LaValle, S., Balboni, F., Shockley, R., & Kruschwitz, N.. (2010). 10 Insights: What Survey Reveals about Competing on Information & 10 Data Points: Information and Analytics at Work. MIT Sloan Management Review, 52(1), 22-31.
Soumendra Mohanty. (2011). Having Analytics May Not Be Enough: Organizations need to improve business intelligence and decision-making through guided, predictive analytics. Information Management, 21(1), 30.
Jayanthi Ranjan & Vishal Bhatnagar. (2011). Role of knowledge management and analytical CRM in business: data mining-based framework. The Learning Organization, 18(2), 131-148.
Data Warehousing: A Strategic Weapon of an Organization.
Within Chapter One, an introduction to the study will be provided. Initially, the overall aims of the research proposal will be discussed. This will be followed by a presentation of the overall objectives of the study will be delineated. After this, the significance of the research will be discussed, including a justification and rationale for the investigation.
The aims of the study are to further establish the degree to which data warehousing has been used by organizations in achieving greater competitive advantage within the industries and markets in which they operate. In a recent report in the Harvard Business eview (2003), it was suggested that companies faced with the harsh realities of the current economy want to have a better sense of how they are performing. With growing volumes of data available and increased efforts to transform that data into meaningful knowledge that can…...
mlaReferences
Agosta, L. (2003). Ask the Expert. Harvard Business Review, 81(6), 1.
Database: Business Source Premier.
Babcock, Charles (1995). Slice, dice & deliver. Computerworld, 29, 46, 129 -132.
Beitler, S.S., & Lean, R. (1997). Sears' EPIC Transformation: Converting from Mainframe Legacy Systems to Online Analytical Processing (OLAP). Journal of Data Warehousing (2:2), 5-16.
From Supply Chain Efficiency to Customer Segmentation Focus
Because of this focus on supply chain forecasting accuracy and efficiency, the need for capturing very specific customer data becomes critical. The case study portrays the capturing of segmentation data as focused on growing each of the brands mentioned that VF relies on this data to base marketing, location development and store introductions, and pricing strategies on. In reality, the data delivered for these marketing programs and location-based analyses is also providing an agile and scalable platform for VF to more effectively manage and mitigate its supply chain risk as well.
elying on Alteryx for data analysis as it has superior capability to Microsoft Access and Excel in conjunction with the use of SC Software for geo-demographic analysis, VF has created a workflow for translating data warehouses into the basis of marketing and supply chain strategies. The strategic goal of getting the right product…...
mlaReferences
Adnan, M., Longley, P., Singleton, a., & Brunsdon, C. (2010). Towards Real-Time Geodemographics: Clustering Algorithm Performance for Large Multidimensional Spatial Databases. Transactions in GIS, 14(3), 283-297.
Paul Sheldon Foote, & Malini Krishnamurthi. (2001). Forecasting using data warehousing model: Wal-Mart's experience. The Journal of Business Forecasting Methods & Systems, 20(3), 13-17.
Yang-Im Lee, & Peter R.J. Trim. (2006). Retail marketing strategy: The role of marketing intelligence, relationship marketing and trust. Marketing Intelligence & Planning, 24(7), 730-745.
Lewis, M., Hornyak, R., Patnayakuni, R., & Rai, a.. (2008). Business Network Agility for Global Demand-Supply Synchronization: A Comparative Case Study in the Apparel Industry. Journal of Global Information Technology Management, 11(2), 5-29.
SQL and ig Data
Gaining greater insights into terabytes of unstructured and structured data organizations have been collecting in many cases for decades across diverse computing and storage platforms are increasingly being unified through advanced data and system architectures. ig Data is the term used to define very large, diverse data sets that contain both structured and unstructured data that defy analysis using conventional database management and analytics applications (International Journal of Micrographics & Optical Technology, 2010). ig Data is an area generating much interest in enterprises as this collection of data analysis, aggregation and extraction techniques continue to deliver valuable insights into how companies can become more competitive (Datskovsky, 2013). Structured Query Language (SQL) is a widely accepted approach to querying databases, aggregating and analyzing data and creating useful reports that guide decision making in organizations (Rys, 2011). Enterprise software companies are creating ig Data analytics applications that include…...
mlaBibliography
Baker, B. 2013, "Enterprise Analytics: Optimize Performance, Process and Decisions Through Big Data," Quality Progress, vol. 46, no. 6, pp. 68.
Datskovsky, Galina, PhD., C.R.M. 2013, "Harnessing Big Data for Competitive Advantage," Information Management, vol. 47, no. 1, pp. 1.
Ferguson, R.B. 2013, "The Big Deal About a Big Data Culture (and Innovation)," MIT Sloan Management Review, vol. 54, no. 2, pp. 1-5.
Ferguson, R.B. 2012, "The Storage and Transfer Challenges of Big Data," MIT Sloan Management Review, vol. 53, no. 4, pp. 1-4.
Database Data Warehouse Design
Our company, Data Analytic Limited, specializes in collecting and analyzing data for various organizations. Over the years, we have assisted various companies to turn raw data into valuable information that assists the companies in making effective decision profitable in the short and long run. Our research and data analytics are geared towards giving extra edge to various companies. Our services include processing and analyzing terabytes of data to provide customer meaningful information for business decision and enhance competitive market advantages. ecent growth of our company necessitates the needs to design and develop data warehouse that will accommodate large volume of customer data.
Objective of this project is to design and develop the data warehouse for our company.
Importance of Data Warehousing for our Organization
Comprehensive portfolios of our business include Business, Market, and Financial research, Data processing services and Domain based analytics. While the relational database that our company is…...
mlaReference
Hillard, R. (2010). Information-Driven Business. UK. Wiley.
Microsoft (2012).Data Warehousing | Microsoft SQL Server 2012. Microsoft Corp.
Patil, P.S., Srikantha, R., Suryakant, B.P. (2011). Simplification in the Reporting and Analysis Optimization of the Data Warehousing System:, Foundation of Computer Science, 9 (6): 33 -- 37.
Rostek, K. (2010). Data Analytic Processing in Data Warehouses. Foundations of Management, 2(1), (2010), 99-116.
Internet also known as the big data to the market researchers. The article also aims to show the disadvantages associated with the big data like privacy issues. The article presents two dimensions of the Internet use, its benefits, and disadvantages. However, its analysis lies more on the benefits than the disadvantages. The major reasons for supporting these aims are premised on the authors' ability to identify the benefits and the risks associated with the big data and their analysis to support the claims. Through this, they succeed in convincing the reader about the benefits and dangers associated with of the Internet (Nunan & Di Domenico, 2013).
The authors have used different research strategies to support their purposes. They used empirical studies conducted by other researchers like MS 2006, Leigh 2010, Jacobs 2009, Ohm 2009, and IBM to support their case. For example, they obtained information from MS' 2006 research to…...
mlaReference
Nunan, D., & Di Domenico, M. (2013). Market research and the ethics of big data. The International Journal of Market Research, 55(4), 2-13.
Herodotou, H., Lim, H., Luo, G., Borisov, N., Dong, L., Cetin, F.B., & Babu, S. (2011). Starfish: A self-tuning system for big data analytics. In CIDR (pp. 261 -- "272). Retrieved from http://x86.cs.duke.edu/~gang/documents/CIDR11_Paper36.pdf
EPR Discussion
The client has been using an ERP system to integrate financial and non-financial information for decision-making. They have been hearing about Big Data. Briefly explain the concept of Big Data, including its advantages and disadvantages.
Generally speaking, the confluence of Enterprise Resource Planning, ERP, and Big Data is seen as a boon and "win" for the companies that use them to help their business. Indeed, businesses that can use Big Data to manage and wield massive amounts of data in ways that are advantageous from a business and strategic standpoint will tend to do much better than those that are unwilling or unable to do so. One upside to the melding of these two concepts and technologies is that the benefits are outstanding. A downside is that not all ERP systems are accustomed and designed to properly harness and use Big Data resources. Indeed, an ERP or Big Data Application…...
Discussion of Analytics1There are three main types of analytical techniques: descriptive, predictive, and prescriptive. Each type of analysis has its own strengths and weaknesses, and it is important to choose the right technique for the job at hand.Descriptive analytics is all about understanding what has happened in the past. It involves collecting data and then using that data to generate insights. For example, a company might use descriptive analytics to understand patterns in customer behavior. By analyzing data on customer purchases, interactions, and other behaviors, companies can gain insights into which customers are most loyal, what motivates them to buy, and what factors influence their decision-making. This information can then be used to tailor marketing and sales strategies to better meet the needs of specific customer groups (Frankenfield, 2020).Predictive analytics takes things one step further by using historical data to make predictions about what will happen in the future. For…...
mlaReferencesChen, C. H., Härdle, W. K., & Unwin, A. (Eds.). (2007). Handbook of data visualization. Springer Science & Business Media.Delen, D. (2014). Real-world data mining: applied business analytics and decision making. FT Press.Frankenfeld, J. (2020). Descriptive analytics. Retrieved from M., & Denis, D. (2005). The early origins and development of the scatterplot. Journal of the History of the Behavioral Sciences, 41(2), 103-130.Hsu, M. F., Hsin, Y. S., & Shiue, F. J. (2022). Business analytics for corporate risk management and performance improvement. Annals of Operations Research, 315(2), 629-669.Pröllochs, N., & Feuerriegel, S. (2020). Business analytics for strategic management: Identifying and assessing corporate challenges via topic modeling. Information & Management, 57(1), 103070.Seddon, P. B., Constantinidis, D., Tamm, T., & Dod, H. (2017). How does business analytics contribute to business value?. Information Systems Journal, 27(3), 237-269.Segal, T. (2021). What is prescriptive analytics. Retrieved from https://www.investopedia.com/terms/p/prescriptive-analytics.asp Shanks, G., & Bekmamedova, N. (2012). Achieving benefits with business analytics systems: An evolutionary process perspective. Journal of Decision Systems, 21(3), 231-244.Shmueli, G., Bruce, P. C., Yahav, I., Patel, N. R., & Lichtendahl Jr, K. C. (2017). Data mining for business analytics: concepts, techniques, and applications in R. John Wiley & Sons.https://www.investopedia.com/terms/d/descriptive-analytics.asp Friendly,
Federal Forensic Data Analytics
There are definite advantages to the Federal Bureau of Investigation's Digital Forensic Data Analytics program. Many of those advantages are well aligned with the integration of this program into the Strategic IT Plan for this organization. Essentially, the aforementioned program can increase the efficiency of the bureau's information technology initiatives, while simultaneously lowering costs and reducing the sort of infrastructure required to sustain this program. Additionally, this program can help this bureau maximize its current resources without needing to make substantial investments in other ones.
The analytics capabilities of the Digital Forensic Data Analytics program of the FBI far surpass those that it previously had for forensic analytics. One of the most immediate impacts of this program towards costs relates to the predictive capabilities of this program. By utilizing various components of cognitive computing including machine learning and other sophisticated algorithms such as deep learning and neural networks…...
mlaReferences
Cheng, W. (2016). What's a CFO's biggest fear, and how can machine learning help? www.analyticsweek.com Retrieved from https://analyticsweek.com/content/whats-a-cfos-biggest-fear-and-how-can-machine-learning-help/
Harper, J. (2016). Creating "Data Culture" with self-service analytics. www.analyticsweek.com Retrieved from https://analyticsweek.com/content/creating-data-culture-with-self-service-analytics/
As with any new idea, costs associated with the adaptation of a new application would be incurred mainly at the beginning as it personnel would need to be trained for using the StreamBase.
Security might be one of the main problems associated with StreamBase. Would the streaming data be encrypted or otherwise protected from malicious users? he organization adapting to StreamBase would need to be sure that the analyses were not vulnerable to security breaches. Finally, just as with streaming multimedia content, streaming data and data analysis might be problematic and prone to caching problems. Possible glitches may be due to server speeds, client PC speeds, and the speed of data transmission. If the organization relied on its own intranet and had a backup system for streaming, then it might be possible to mitigate any problems associated with real-time financial data analysis.
Vaas, Lisa. "StreamBase 2.0 argets Financials." eWeek. June 17,…...
mlaThe benefits of real-time financial data analysis would therefore far outweigh the costs. Restructuring and redesigning the organizations it department would be beneficial in other ways: forcing the introduction of new products, ideas, and processes. At the same time, increased revenues from the more robust data analysis system would more than make up for whatever costs were associated with implementing the new application. As with any new idea, costs associated with the adaptation of a new application would be incurred mainly at the beginning as it personnel would need to be trained for using the StreamBase.
Security might be one of the main problems associated with StreamBase. Would the streaming data be encrypted or otherwise protected from malicious users? The organization adapting to StreamBase would need to be sure that the analyses were not vulnerable to security breaches. Finally, just as with streaming multimedia content, streaming data and data analysis might be problematic and prone to caching problems. Possible glitches may be due to server speeds, client PC speeds, and the speed of data transmission. If the organization relied on its own intranet and had a backup system for streaming, then it might be possible to mitigate any problems associated with real-time financial data analysis.
Vaas, Lisa. "StreamBase 2.0 Targets Financials." eWeek. June 17, 2005. Retrieved Oct 18, 2008 at http://www.eweek.com/c/a/Database/StreamBase-20-Targets-Financials/1
Firms meet the challenge of technological change by redesigning their innovation ecosystems in several key ways. This approach involves a strategic overhaul of their internal and external operations, relationships, and culture to foster innovation and adaptability. Here's how they do it:
Embracing Open Innovation: Moving away from solely in-house R&D, firms are increasingly embracing open innovation. This involves collaborating with external entities like startups, academic institutions, and other companies to source new ideas, technologies, and methodologies. This collaboration can take various forms, including joint ventures, partnerships, and innovation hubs.
Investing in Digital Transformation: Firms are investing heavily in digital technologies such as....
One potential essay subject related to planning in the news could be the impact of urban planning on community development. For example, exploring how city planners are incorporating sustainability measures into their urban design to create more environmentally friendly and livable cities, or examining the role of gentrification in urban planning and its effects on local communities. Another potential topic could be the challenges and opportunities of disaster preparedness and emergency planning in the face of climate change and increasing natural disasters. This could involve discussing how cities and countries are adapting their emergency response plans to better protect their....
The Evolving Landscape of Lesson Planning: Navigating the Intersection of Technology, Data, and Student-Centered Learning
Introduction:
In the ever-evolving educational landscape, lesson planning has emerged as a pivotal element in shaping effective and engaging learning experiences. Recent news headlines highlight the impact of technology, data analytics, and student-centered approaches on the way lesson plans are conceptualized and implemented. This essay explores the transformative trends in lesson planning and their profound implications for teaching and learning.
Technology and Lesson Planning:
Technology has become an indispensable tool in lesson planning, offering educators a wide range of possibilities to enhance student engagement and deepen understanding. Interactive online....
Comprehensive Target Market Analysis and Segmentation Strategies
Examining Target Markets: A Holistic Approach to Customer Focused Strategies
The Art of Target Market Identification: Unlocking Market Potential and Growth
Target Market Optimization: A Comprehensive Guide to Tailored Marketing
Defining the Target Audience: Foundations of Effective Marketing Campaigns
Precision Target Marketing: Aligning Strategies with Consumer Needs
Consumer Segmentation and Target Market Definition: A Data-Driven Approach
Uncovering Market Segments: The Key to Personalized Marketing
Building Customer Profiles: A Framework for Target Market Identification
Target Market Strategies for Dynamic Market Landscapes
Target Market Analysis: A Case Study of Successful Market Penetration
The Evolution of Target Markets: Adapting to Changing Consumer Dynamics
Target Market Research: Unveiling Insights....
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