Reply to the following post in no less than 250 words.
In chapter 1, one of the most important issues outlined is data analytics role in a business’s management and success. In driving the relevance of analytics, the book assumes the business owner the consumer of the analytics. This is because the business developer requires analyses and models integrated into the business development process to attain the venture’s business success (HBR book, 2018).
As outlined in the chapter, the role of analytics in business development is critical as it outlines a significant application of data analytics in day-to-day activity. It describes data analytics role in economic growth development through business development, which is a key element in economic growth.
The second critical issue that has been outlined in the chapter is a description of how to become a data analytics expert. In the description, simple processes such as understanding the basics of regression, experimental design, and statistical inference are outlined. It also gives the most important considerations to be made when forming analyses. Also, the chapter explains the programs that one needs to enroll in to become a good data analytics professional.
The description of the processes used in becoming a data analysis expertise is crucial because it offers a foundation to any individual, whether in the business world or private practice, who might be interested in becoming a data expert. Also, it opens up ideas on key areas that one needs to focus on if interested in understanding ways to perfect and advance skills learned in past school experiences.
The third critical issue outlined in the chapter is the process of decision-making which has been outlined as a six-step process with the reliance on big data. In the process, the first step is recognizing the problem or question after the previous findings are analyzed. Following the review, the variables used in the model are selected, then the data is collected, analyzed, and the results portrayed will be presented.
The process of decision-making using data analytics is important as it sets the framework used in ensuring that the right processes in enhancing the efficiency of the decision-making process are used. It is also critical that it provides a source of data that can be used to make comparisons with other decision-making processes in the organization to identify gaps or errors in the decisions.
In chapter 2, one of the critical issues outlined is the starting point in thinking like a data scientist. The chapter sets up a clear and brief description of the preparations required in setting the mind right for data analytics.
Preparations required in setting up the right mindset are an important stage for a non-quant intending to apply data analytics in developing solutions to business problems.
The second critical issue outlined is the process of data collection as part and element of data analytics. In describing the process’s importance, the chapter explains the need to have a plan or protocol to explain the process to be used in collecting the data.
Data collection is a critical issue in data analytics as a manager has to collect the relevant data which needs to be considered in the business model.
The third critical issue outlined in the chapter is the statistics’ role in summarizing the findings made from the study. The chapter gives tips that should be considered in summary statistics and how to represent findings or the results through a graph.
This is an essential matter as it describes ways to enhance the easy-understanding of the users of the business models or analytics models. Regardless of the data scientists’ level of competence, the mode of representation is important in determining the analytic model’s quality.
Lessons learned
In chapter one, one of the most relevant lessons learned is data analytics in credit management. This is outlined at the onset of the chapter through the briefcase story of a bank in the U.S that lost millions from poor mortgage management. The absence of a business model costs the bank the losses as they would not make the right predictions based on the available data.
The lesson is important as it explains the role of data analytics in managing risks such as credit defaults. It also explains the risks associated with the inappropriate handling of big data in a business environment. Also, it equips the learner with the importance of data analytics as a competitive necessity in the organization.
The second relevant lesson studied in chapter one is how to solve problems using data analytics by focusing on the beginning and the end. The chapter outlines the process of resolution to the business process, which starts with identifying the problem and understanding how others have solved the situation in the past. An application scenario is used in the chapter on two corporate parent organizations and how data analytics was used in solving problems within the organization by the business executives.
The lesson is relevant because it outlines ways to achieve the desired outcome in a problem resolution and maximize resources. Also, it helps in digesting how business executives can customize their efforts to achieve the set targets of their organization. This is attained through the suggestions on how business leaders can link the first step in decision-making and the last step as acting on the results. It also explains the role of non-quants in problem analysis and resolution in corporate management.
The third relevant lesson outlined in chapter one is on the questions to ask during data analytics and the importance of such questions in enhancing the decision-making process’s relevance and reliability. One of the importance of generating questions is to enhance the understanding of the models and the assumptions used in developing the data analytics models. This is useful particularly in cases where the quants or the model developers are external parties to the organization.
The lesson about addressing the questions is important. It outlines the role of managers and business executives in ensuring that the organization’s business models are suitable for the organization’s business processes. Also, it explains the need for enhancing clarity when developing an analytic model.
In chapter two, the major lesson learned is that there is a high possibility of non-quant becoming a good data scientist. This is also applicable in becoming a data analytics expert or a researcher as it outlies various ways of handling big data.
The lesson is necessary and relevant as it outlines a way in which a manager can polish skills on the use of big data in enhancing the efficiency of the management function in the organization.
The second lesson outlined in the chapter is the importance of selecting the right and appropriate variables during the formation of analysis. This is outlined through the process of learning how to think like a data scientist. The chapter outlines the value of having a clear plan on how the data will be collected, analyzed, and interpreted.
The lesson is important and relevant as it outlines the importance of determining all the factors necessary in making a given decision and elements that can alter the decision made from the use of big data.
The third lesson outlined in the chapter is the importance of conducting regular reviews of the analyses by reflecting the data gaps identified during the process of collecting the data. The chapter outlines the need to make adjustments to the protocols as a way of enhancing the accuracy and reliability of results,
The lesson on the need to have close monitoring of business model development is essential as it outlines the importance of adjusting the model to suit the target organization. It drives the relevance of making regular adjustments to the model to maximize the efficiency of a model.
Best practices
One of the best practices sketched in chapter one is vulnerability, where the chapter outlines the importance of being vulnerable in business management. The chapter explains the need for business executives and managers not to act anonymously but instead, working as a team. The consequences of acting anonymously are evident through the case study of the U.S bank that lost huge funds as a result of the ignorance of a business executive.
Vulnerability, as implied indirectly in the chapter, is a key element of best practice in business management. It drives to a higher level of efficiency through cooperation as business managers appreciate the need to complete each other.
The second-best practice outlined in the chapter is clarity in data analytics which is evident through the lesson on the need to ask many questions. The chapter explains the questions and their role in data analytics. The chapter explains the role of business executives in seeking clarity on the specs of the analytic models introduced in the business by external parties.
Clarity is an important best practice in data analytics and its application in a business environment. This is because it enhances alignment among the managers and other stakeholders in the organization. Also, it ensures that there is optimum utilization of resources.
The third best practice outlined in the chapter is the art of rewarding effort s outlined as an example of the importance of applying analytics efforts in an organization. This involves the art of getting a form of incentive for the application of certain skills or hard work in management.
Rewarding efforts is an essential best practice in emphasizing the relevance of data analytics in a business environment and an organization’s management. An organization that applies data analytics effort is rewarded by attaining efficiency in the decision-making processes.
In chapter two, one best practice outlined is creating an effective data science organization. This is evident through the lesson on ways of attaining the thinking of a data scientist. The chapter explains the importance of understanding the mechanisms of processing data and applying it in an organizational setup.
The best practice is relevant as it sensitizes the importance of understanding how to use available data to the advantage of the organization.
The second-best practice outlined in the chapter is selecting the right tools and metrics to match the job. This is evident through the planning and preparation of protocols before the collection and analysis of data. The protocol outlines the methodology used in the analytics by the data scientist.
The importance of the practice is outlining the role of using the right variables in analytics or the metrics to tie the data science results to the goals of the organization.
The third best practice outlined in the second chapter is communicating widely and frequently. This has been explained through the emphasis on the need to review the data analyses process to ensure that information gaps are addressed before the summary of the results is prepared and decisions made from the decisions. The best practice is relevant and vital as it describes the importance of improving the strategies used in attaining the vision and purpose of the project or the organization.
Relevance to classwork
The two chapters relate to the topics covered in classwork through the analysis provided on ways that managers can apply business analytics in the management of the organization. For instance, in chapter one, there is a description of a comprehensive process in which managers can apply analytics in business decisions through the formation of a hypothesis.
Alignment
There is high alignment between the concepts outlined in the chapters with the concepts reviewed in class. One of the alignments is the process of decision-making using big data, as outlined in chapter one. In class, problem formulation and resolution are important for big data aligned to the problem framing discussed in chapter one.
