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Chapter 3 Summary
Chapter three focuses on the importance of selecting the “right” data. In conveying the importance of doing so, we are given insight as to what the typical business’s data acquisition and analysis processes usually look like, and then given explanations as to what the best methods are in order to increase their efficiency. Chapter three opens with a case study about a CEO of a premier consumer products company who insisted on conducting a monthly business review process that was excruciatingly long and data intensive to produce. The CEO would have his data analysts spend thousands of hours collecting, analyzing, reconciling and sorting the data in preparation for the monthly review. When the company switched CEO’s, the new CEO only requested quarterly reports instead of monthly. This reduced the amount of time that the employees would have to dedicate to these internal data reports and reduced the entire data production industry of the company. The new CEO figured that there was not a huge difference in the trends that the data would produce from a monthly to a quarterly basis, which was true.
Three critical issues
1. Over analyzing data can be just as inefficient as under analyzing it. Like with most things in life, too much of a good thing can prove to be bad. The same goes for analyzing data. If a company spends too much of their time doing detailed monthly reports of their data, it can prove to be just as inefficient as under analyzing the data. There is a happy medium that managers must find, in the example the chapter gives, the new CEO found that quarterly reports were sufficient tostay on top of while not lingering too much on the past.
2. Different CEO’s need their data to do different things.oSome CEO’s prefer their decisions “to be based on as much hard data as possible, while others want just enough to either reinforce or change their intuition” (page 34). As a manager, it is important to note the goals of the company and what the CEO wants the data to prove. Some CEO’s may even prefer a combination of hardanalytical data with anecdotal and qualitative input.
3. The importance of data in our present age, and the value that these data hold. This is something critical to note, because not only the utilization of data has increased as time has gone on, but also the sheer amount of data that is collected in hopes of being used.
Three lessons learned
1. The importance of setting out a clear-cut goal for the data one is collecting. This is because when performing an analysis of data of any sort, it is a time-consuming process and a process that makes use out of a lot of resources, so knowing what we are looking for cuts down on these lost resources. So if we are going to utilize these processes, it is most efficient, and makes the most sense, to use a strategy that only really would yield information on the variables that are necessary for the analyses we are looking to perform –and to the same token, analysis can only make predictions based on the data and factors that are accounted for, including bias, mistakes, etc., so being selective also matters.
2.The ways that data analyses can be useful to businesses. I was unaware of the extent to which businesses actually utilized data analyses, so to read about all the different ways it can be used, and what kinds of information can be drawn from these processes gave me a level of insight I did not previously have, in regards to the importance of these sorts of data analyses. The applications of these sorts of analyses really can shift the way a company does business, so to see companies using them so freely really helps us gain perspective on how useful they really are.
3. Know that the best automated tools will not be efficient unless managers know what they want to do with the data. This is not a new lesson in this class, but it continues to pop up in almost every chapter we read. There is a very large emphasis placed on this idea because as a manger we cannot solely count on automation to produce the data for us, we also must understand how to properly use the data to our advantage.
Three best practices
1. Be aware that while seeking out these positive consequences resultant of data collection and analyses, possessing the resources is not nearly enough to produce these kinds of results. Assessing and experimenting with data is where the real “meat” of the job of quantitative analysts comes from. A company can collect all the data they want, but if it is not put to good use, then it is worth the same as avoiding these techniques. Only through a properly involved business model are companies typically able to utilize these data in meaningful ways which produce results.
2.To maintain a healthy level of skepticism, and to utilize intuition more than you think one may have to. Because data analyses are essentially results of user inputted factors, along with trend-seekers based off previous trends, there is a considerable amount of error involved. So, one thing I did not really think of as necessary, suggested by the text, is to rely on one’s intuition. As in, do not feel the need to immediately take everything as fact, especially if it seems wrong.
3. Ensure that you are asking the right questions. Take time to develop your questions and make sure that you are asking the right ones that pertain to what you are trying to achieve. The chapter states that many companies often collect the data that is available, rather than the information that is needed to make the proper business decisions (page 35.) Instead of taking the easy route of analyzing what is in front of you, managers need to pose questions and gather the appropriate data and studies that will prove beneficial to the main problem or question.
Relate with topics covered in class
The focus of this chapter is to help you understand how much data you need to produce to answer your questions and states four important questions to ask yourself when analyzing data, all of which are discussed in my three best practices or lessons learned. The questions that are discussed in this chapter reminded me of the process introduced in chapter 2. Chapter 2 introduced the process of how to start a data analysis, and the first step in the process is asking the main question. Why are we doing this? What are we hoping to get out of this study? Chapter 3 focuses on these questions and introduces more insightful questions like “does our data help us look ahead rather than behind?”
Alignment of concepts covered in class
Chapter three introduced the idea of mixing quantitative and qualitative data to tell the full data story. This reminded me of the article “Is Optimization part of Analytics?” sent out on 10/24/2019. The article talks about how you can use the two to get a greater insight into data analytics and explains how optimization can be used to make better decisions. I think that this complements what chapter three was mentioning about using both quantitative and qualitative information to paint out your data story. Data analysts should be able to use a combination of different data and methods and summarize them to come up with their conclusion. This is another common theme that we see throughout this course and will likely continue to see.
Chapter 4 Summary
Chapter 4 of the text focuses on the sorts of tasks we need to be in communication with our data scientists about when performing the sorts of data analyses we typically see in a normal business setting. In doing so, the text takes its main focus on what the typical process is, and the sorts of areas we should be in close communication about with these specialists. We are also given insight as to the sorts of points of importance in these processes and even provided with questions we should be asking. In doing so, we get a panoramic view of everything critical in understanding basic data collecting techniques, and how professionals go about these processes in order to take in data to produce something more meaningful.
Three critical issues
1.The areas of information we need to be looking at when performing data analyses; while we are often provided with the information on the sorts of things we should be looking for while collecting/analyzing data, we are never really given a guide as to what the actual data scientists are looking for. When performing these sorts of processes, we are often left with so much more data than what we need in order to reach our goal, so seeing the sorts of questions we should be asking specialists, and the critical points of importance, really gives a better idea of the actual things we should be looking at –and the kinds of questions we should ask in order to reach these.
2.The significance that data can have, when framed the right way. I say this because, in the instance that someone were to conduct collecting data in order to solve a certain problem, all of their efforts would be in vain if the data being collected does not meet the criteria to answer the question they are trying to ask. Data can only be as useful as one makes it, so collecting the proper data, along with framing the questions in order to suit the data being collected are critical in producing content thatmatters.
3. Consequences of collecting data. Managers should note that there are various issues that can arise from obtaining and analyzing data. A manager needs to think beyond the data and consider the greater consequences of the data collection and work with the data scientist to fully understand and be aware of the consequences. There can be both ethical and financial consequences that can arise when data is collected inappropriately, and it is a manager’s job to be aware of them and prevent data from being collected in a way that leaves the company vulnerable to potential lawsuits.
Three lessons learned
1.Working with your data analyst. While this does not reference a page in the chapter, the main lesson I learned is that communication with your data scientist is extremely important if you want to achieve your goals in a timely and costly manner. Managers must work with data scientists and know how to clearly communicate their goals and ideas to produce the best data. We saw this in chapter 1 as well –how a manager needs to ask clear questions that have a focus and isn’t asking too many questions.
2.Structured versus unstructured data. Structured data is easy to add to a database and is typically easier and faster to manipulate. Unstructured data is not as easily stored or manipulated, but it makes up about 95% of the world’s data according to a report by professors of Ryerson University.
3. The importance of selecting variables that are relevant to the data in question. Because data collection can involve multiple variables –variables that are selected for observation by the user –it is critical to the conclusion(s) of the analyses to observe ones that have a significant connection to the dependent variable at hand.
Three best practices
1. To be aware that there is always a network of support that can offer assistance in any area of data collection or analysis. Based on this chapter and the last, we are given insight as to the resources that individuals in these fields utilize, and for what reasons –and in doing so, it is very revealing as to all the sources of information that these individuals have in order to manipulate their data. Because of this, I would also say that we should be more confident in the sorts of areas we are researching, because they likely are not as daunting as they seem, due to the levels of assistance on standby.
2. To ask the right questions, and to look at the right details. As in, effectively framing one’s question, and only giving importance in analyses to the variables involved in the questions being asked. By doing these, one can be more sure about the content of the data collected –being that it is all relevant and important. To ensure so, quantitative data analysts can be better equipped to search for patterns that matter, etc., and come to conclusions that are relevant to the proposed questions.
3. Keep it simple, stupid. Once the main questions have been asked, and you and your data scientist know what kind of data to gather, make sure the data is as simple and easy to understand as possible. Work with your data scientist to identify ways to simplify the information, and only save the more complex tools if absolutely needed. The simpler the data, the better. Managers should encourage their data scientists to keep their data clean and easy to interpret to avoid possible misunderstandings and confusion.
Relate with topics covered in class
The topic of this chapter is not new in this course. We saw something similar in chapter 1 “Keep Up with Your Quants” where it explains the importance of communicating with your data analysts, as well as the importance of educating yourself on data analysis so that you can understand the information presented and ask more educated questions to your data scientists.
Alignment of concepts covered in class
Chapter four takes its main focus on the concept of data optimization. This concept is also heavily aligned with the course work, being that the focus of the course is to take in a set (or sets) of data and interpret it in order to reach meaningful conclusions and answer questions about the data we collected. We have not thoroughly explored the contents of this subject through the course work, though, it can definitely be said that we were at least somewhat aware of the topics covered here –especially being that most of the techniques being taught through the text have been ones we have had to employ to get the work done.