Please reply to the discussion below:
Detailed – Comprehensive Summary for each of this (these) Chapter(s).
HBR Guide to Data Analytics Basics for Managers is a textbook published by the Harvard Business Review Press. In this book, the author is showing the importance of quantitative analysis as part of the decisions taking by managers and executives. Historical information collected by organizations is studied by experts using different analytical methods, and these studies reach meaningful conclusions that help entrepreneurs to create, develop and improve products and services.
Chapter 3 is called “Do you need all that Data?” and it was written by Ron Ashkenas. This chapter corresponds to the second section of the book, and it focuses on gathering the right information to use analytics as part of the decision-making process for a company. There are different types of managers, and each of them has its own schedule. Some are data-minded, meaning that use analytics for every decision they make. Others rather trust in their intuition and use data analytics to ratify or challenge their choices, and some combine analytical data with qualitative information. In all circumstances, managers would need to improve their data process to reach meaningful and better conclusions.
Mr. Ashkenas guide managers through four topics or questions they would need to ask themselves to get substantial results. First, it is important to ask the right questions and focus on the information that is needed to help make decisions rather than all the information available. Instead of trying to expand and use all data, it is better to filter it to achieve quality results. Second, the data used must follow a pattern or trend that managers can work on. Data must always tell a story. Third, data would need to be used for forecasting rather than look behind. The information collected showed us historical events, but managers would need to use it to predict sales, costs, and other areas that would help the company to perform better. Finally, we would need to find a balance between qualitative and quantitative data. For example, good product and pricing decisions are determined by how much does the product gets sold, how frequently, and how do other products sell concerning that one. There are different parameters to determine the balance in a company.
Chapter 4 is called “How to ask your data scientists for data and analytics”, and it was written by Michael Li, Madina Kassengaliyeva, and Raymond Perkins. The topic discussed in this reading explains how the communication between business managers and data scientists must be, and the steps to a proper investigation that lead to meaningful results.
The authors give us tips about what questions to ask while working with data analysts; what data do us need and how to obtain it. Also, understanding the cost of data is one of the topics explained and the forms in which data comes. If data comes clean, meaning it is easy to read, then the analysis will be faster and scientists would manipulate it to get the best results. Finally, models and statistical techniques play an important role in data analysis. The authors clarify the simplicity of these models, and how complicated they could become.
Which are the three most CRITICAL ISSUES for this (these) Chapter(s)? Please explain why? And analyze, and discuss in great detail…
The three most CRITICAL ISSUES for these chapters are:
Each manager, CEO, or any person that uses data to make decisions creates different needs for data. Some would base their decisions on data results; others just use it to reinforce their instinct, and others prefer a combination of quantitative and non-quantitative data. There is no right or wrong way to use data, but it is definitely necessary to perform better.
There needs to be a balance between the information collected and its value. While organizations love data, and it is good to have a data warehouse, it is also important to the quality of the data. Misinformation can lead to wrongful decisions made by companies and can affect their performance.
Data must tell a story. This is essential to a logical explanation of the business situation, and also it would need to be considered by managers. The management team is responsible for the well-being of an organization, and they would need to consider all the steps and questions necessary to use and understand analytics in their decision-making.
It can be a challenge to request new data from data scientists. Data-driven philosophy is relatively new, and there is still misinformation or lack of knowledge as to what are we looking for when conducting data analysis, what questions do we ask, or what are factors to consider getting the right information for the investigation.
One of the critical issues explained in chapter 4 is how do we obtain the data? Information is needed to perform data analysis, however, there are costs implied in this process. Experiments are one of the most effective and reliable options to gather information, but they are expensive and difficult to perform. Also, companies can use their insights to establish new processes.
Models and their simplicity are the last critical issue for this chapter. Managers and data scientists need to work together and build simpler models and move to more complex if the first ones are inefficient.
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 three most relevant LESSONS LEARNED for each of this (these) chapter(s) are:
There are four important questions that every manager needs to ask about their data process: 1. Are we asking the right questions? 2. Does our data tell a story? 3. Does our data help us look ahead rather than behind? 4. Do we have a good mix of quantitative and qualitative data?
Data is important for organizations. There more numbers, information, reports, tendencies, and graphs, the better for the company. We have seen how data-driven companies such as Amazon and Google have reached success in their industries.
Human insight is very important in the analytical process. This was stated by author Ron Ashkenas at the end of chapter 3, as he said that even the best-automated tools will not be effective unless managers are clear about what questions to ask. The management team of an organization would need to focus on the four internal questions to ask while doing a test and working with data.
It is very important to have a goal in mind while working with a data scientist. A manager needs to know what would be the business impact that he/she intends to achieve, and how to collaborate with the analyst to define the goal, and find the information that best fits the objective of the person.
The second lesson learned is that managers would need to keep clear communication with their scientists and staff. Managers must know what they are asking for, and what would be their goal before starting the data collection process.
Finally, it is important to take the required steps to avoid extensive expenses while conducting data analysis. Lowe costs and risks while collecting data, ask the right questions, ensure proper collaboration and get the necessary information are essential to the success of data analysis and communication between scientists and the management team.
Which are the three most important BEST PRACTICES for each of these (these) chapter(s)? Please explain why? And analyze, and discuss in great detail…
The three most important BEST PRACTICES for each of these chapters are:
The best practice is to put into practice the four questions managers need to ask themselves about their data process. These questions are required to improve the decision-making of the executive team of companies.
Companies would need to invest in data tools and analysts to improve results. IBM is one of the companies that are investing billions of dollars in business intelligence and analytics. These tools would create a competitive advantage over other companies, but it is not the only resource to reach success.
Finally, Chapter 3 has shown us what data is used for. While data is information collected on past events and historical performances of a company, it is used to create trend lines and patterns to make decisions for the future. It is important to ask what data will help managers to look ahead and find solutions to reach success.
Asking questions is an important part of the process to ensure great results while collecting and working with data. The first best practice would be to ask the right questions, and know when and what type of questions to ask. Synchronization between managers and data scientists is required for an accurate data result, and this process starts with a good and right questions.
The second best practice is to simplify the data. This topic was explained as one of the critical issues for this chapter as well, as it may insufficient simple models. However, simplicity is often the best choice, according to the authors.
Finally, it is important to not look at the data collected to support decisions. Instead, it is better to analyze the data first to make conclusions. It is essential to keep in mind statistic models and data analysis to help make better decisions.
How can you relate each of these (these) Chapter(s) with the topic covered in a class? Please explain why? And analyze, and discuss in great detail…
Topics covered in class are all data-related since this is a statistic class. Most of the topic has a strong relationship with HBR Guide to Data Analytics Basics for Managers textbook, as it focuses on organizations and decisions made by organizations from a mathematical point of view, using analytical work in the findings and results of the company to ensure a better performance in the industry. Chapters 3 and 4 do relate to the topic in class, same as previous chapters. Asking questions, request information, collect data, know models and data techniques are part of the process to ensure great results, and easier and better decision making for managers and company owners.
Do you see any alignment of these concepts described in each of these (these) Chapter(s) with the class concepts covered in a class? Which are those alignments and misalignments? Why? Please explain, analyze, and discuss in great detail…
There is an alignment between concepts explained in these chapters with the class concepts covered in class. Most of our assignments from the course are done to use analytics tools to provide results and reach conclusions to be used by the executive team to make decisions. The textbook is very similar, just that it is less practical and more informative, and it provides the reader with a lot of examples from real life. Most of our homework requires working with data and historical data collected by companies. All this information needs to be filtered, and use the best information to conduct experiments. Chapters 3 and 4 show us what information do we need to look at, what questions can we ask, and steps to guarantee a correct data process. There is one topic explained in Chapter 4 that we have not discussed yet in class, and it is the cost involved in data analysis. We know that collecting data has become more available and cheaper for companies with the development of technology and tech apps, but there are methods explained in the book that can be very expensive for organizations such as experiments.
HBR guide to data analytics basics for managers. (2018). Boston, MA: Harvard Business Review Press.