Keep Up With Your Quants Article analysis
Reply to the following post in no less than 250 words.
“Keep Up With Your Quants”, is simply about how becoming a competitive necessity for your organization since we live in an era of big data. The era of big data includes financial services, consumer goods, travel and transportation, or industrial products. As the banking example shows, having big data doesn’t solve every problem. Companies need general managers who can partner effectively to ensure their work yields better strategic and tactical decisions. It all comes to one thing and one thing only which is the shift toward data-driven decision making. How do you avoid the fate of the loss-making mortgage bank head and instead lead your company into the analytics revolution, or at least become a good individual within the business? The article states, having big data and event people who can manipulate it successfully is not enough. Start by thinking of yourself as a consumer of analytics. The producers are the quants whose analyses and models you’ll integrate with your business experience and intuition as you make decisions. Producers are good at gathering the available data and making predictions about the future. But most lack sufficient knowledge to identify hypotheses and relevant variables and to know when the ground beneath an organization is shifting. The job as a data consumer is to generate hypotheses and determine whether results and recommendations make sense in a changing business environment which is therefore very important to consider. It means accepting a few key responsibilities. Some require only changes in attitude and perspective. Others demand a bit of studying. For someone to learn more about analytics, they must first understand the process for making analytical decisions, including when you should step in as a consumer and recognize that every analytical model is built on assumptions that producers ought to explain and defend. The article provides readers with the six key steps in the analytics decision-making process and it advises future business leaders to align themselves with the right king of quant. To become more data literate, enroll in an executive education program in statistics, take an online course, or learn from the quants in your organization by working closely with them on one or more projects.
- Which are the three most critical issues for each of this (these) Chapter(s)? Please explain why? And analyze, and discuss in great detail.
- The first critical issue here was discussed in this chapter to focus on the beginning and the end of the report on analytics.When performing an analytic, quant must frame the problem. Framing a problem is identifying it and understanding how the others solved it in the past. This is one of the most important stages of the analytical process for a consumer of big data from the chapter.
- The second critical issue here is the six significant steps of analytics-based decision making. When someone uses big data to make important decisions, those who are not focused on quantitative analysis should focus on the first and last step of this process that the article provides. The steps are to recognize the problem or question, review previous findings, model the situation and select the variables, collect the data, analyze the data, and present and act on the results.
- The last critical issue in this chapter is that businesses should align themselves with the correct type of quant. The effective quantitative decisions are not about math but the relationships between quants and consumers of their data. Consumers of data get much better results if they form deep, which allows them to exchange information and ideas without any struggle.
- Which are the three most relevant lessons learned for each of these(these) Chapter(s)? Please explain why? And analyze, and discuss in great detail.
- One of the most important lessons learned in this chapter is the type of questions a non-quant should ask about quantitative analysis. The chapter states one should ask the smart quants in the firm equally smart questions about their models and assumption as well. The questions that should be asked are: What was the source of your data? How well do the sample data represent the population? Does your data distribution include outliers? How did they affect the results? Why did you decide on that particular analytical approach? There is just so much information to be learned from asking many questions.
- The second most important lesson learned in this chapter is the type of questions to be asked along the way. The author states, no matter how much you trust your quants, don’t stop asking tough questions. That is to become more informed of the result of the report and be able to form your conclusion of the data.
- The third important lesson learned in the chapter is the six steps mentioned in the chapter for the decision-making process. The steps explain how one should go by performing an analytic report and when reading to how to comprehend the report made. Non-quant should focus on the final step in the process of analytic, which is presenting and communicating results to other executives. Because it’s one that many quants discount or overlook when making such a style of reports.
- Which are the three most important best practices for each of this(these) Chapter(s)? Please explain why? And analyze, and discuss in great detail.
- The first best practice for this chapter is how to make the analytical decisions. The chapter provides readers with six significant steps to use when making important decisions based on the results. The steps are: recognize the problem or question, review previous findings, model the solution and select the variables, collect the data, analyze the data, and present and act on the results.
- The second best practice is for “non-quants” to establish a culture of inquiry, not advocacy. The chapter explains analytics consumers should never pressure their producers with broad comments, such as finding evidence in the data to support the data. Their goals should be to find only the truth.
- The last important practice is non-quant should be asking a lot of questions regarding the analytic performed by the quant. Questions asked should regard the sources used, the original hypothesis, and conclusions formed from the analyses.
- How can you relate each of these(these) Chapter(s) with the topics covered in class? Please explain, analyze, and discuss in great detail.
In the chapter, I learned of how analytics is becoming a competitive necessity for many
organizations. The chapter also discussed how important it is of questioning quantitative analyses with their sources and findings of a particular topic. To make decision-based on the analytical data, you must go to the six significant steps.
- Do you see any alignment of the concepts described in each of this(these) Chapter(s) with the class concepts reviewed in class? Which are those alignments and misalignments? Why? Please explain, analyze, and discuss in great detail.
Yes, the concepts covered in class align with the material in the chapter. This chapter discussed how analytics is becoming a competitive necessity for many organizations. It covered both the importance of data analysis and how it can predict the future.
Executive Summary: Chapter 2
“A Simple Exercise to Help You Think Like a Data Scientist”, this will open your eyes to the millions of small data opportunities and enable you to work a bit more effectively with data scientists, analytics, and all things quantitative. It begins with something of interest or bothersome, like consistently late-start meetings. Form a question regarding that interest. Afterwards, to think through the data that can help answer the question and make a plan. The next step is for one to collect the data. The must data must be significant and the analyst must trust its data. Following their findings, analyses often demand more of a feel for variation. The article states understanding variation leads to a better feel for the overall problem, deeper insights, and novel ideas for improvement.
- Which are the three most critical issues for each of this(these) chapter(s)? PLease explain why? And analyze, and discuss in great detail.
The most critical issue is discussed in the chapter is questions that should go along with the analysis. Once the data is collected, get the tools to create an analytical summary, they shouldn’t stop. Understanding variation leads to a better overall problem with greater insights. The second critical issue is collecting the data. You must trust the data as well. The process called for you to modify your definition and protocol as you go. The last critical issue is getting started with the analysis. The analyst needs something that interests them and writes it down. The analyst then collects the data and uses the tools available to create the summary.
- Which are the three most relevant lessons learned for each of this(these) chapter(s)? Please explain why? And analyze, and discuss in great detail.
The lesson learned for the chapter is to have tools to create an analytical summary. Always question the summary to have a feel of the variation. Second, is learning how to turn something interesting into a researchable question. Meetings always seem to start late. Is it true? Last, is to demonstrate the importance of analytics of others using pencil and paper which opens eyes to millions of small data opportunities.
- Which are the three most important BEST PRACTICES for EACH of this(these) Chapter(s)? Please explain why? and analyze, and discuss in great detail …
The most important practice of the chapter is to exercise the eyes for small opportunities which enable an individual to work effectively. The second important practice is to collect data from an interesting topic and turn it into a researchable question to create an analytical summary. Lastly, which is to question the results and gain a feel for variation.
- How can you relate EACH of this(these) Chapter(s) with the TOPICS COVERED in class? Please explain, analyze, and discuss in great detail …
I do not relate to the chapter but the chapter mainly discussed the ability to help others to open their eyes to the millions of opportunities and enable work efficiency with data scientists considering everything being quantitative.
- Do you see any alignment of the concepts described in EACH of this(these) Chapter(s) with the class concepts reviewed in class? Which are those alignments and misalignments? Why? Please explain, analyze, and discuss in great detail
The steps are to begin with something of interest. Form a question with the interested topic. Next, collect the data. The data must be significant while the analyst trusts the data. Utilize the tool to calculate the data. Finally, the summary must be developed statistically.