making sense of data
In this chapter, the author talks about how to detect the signal of assignable cause from a process behaviour chart. He first presents the characteristics of a predictable process, and then gives three major detection rules for an unpredictable process. However, my concern is how can we communicate them to
making sense of data
In Chapter 7: Visualize Your Process Behavior, the author introduces a tool called the XmR chart. XmR chart is used to detect assignable causes/ unpredictable events/ events that are outside routine variations. With that purpose in mind, the XmR chart includes two limits, Upper Nature Process Limit (UNPL) and Lower
making sense of data
In this chapter, the author introduces two types of variation, predictable and unpredictable, and the improvement strategies applied to each variation respectively. XmR chart is also presented as a tool to distinguish the types of variation. However, the author does not explain why XmR chart works in this Chapter. This
making sense of data
This chapter is also basic. It is about some common summary statistics, relationships between ratios, percentages, and proportions, and data types. I am not going to cover the summary statistics here since I am sure you already know what is averages, medians, ranges, root mean square deviations, and standard deviations.
making sense of data
I think Chapters 4, 5, 6 are some general introduction to visualization. Although at this stage, I haven’t completed Chapter 6 and onward, I think the chapters are to prepare readers for more advanced topics and the author put those chapters in for completeness. The summary of Chapter 5
making sense of data
Chapter 4 covers some basic charts that will be used to visualize data. I think most of my readers would know those charts very well. But, I would still like to recap some important points. Still, I learned some new things as well, such as Pareto Charts, and recalled some
making sense of data
In Chapter 2, the author introduces tools to visualize systems and processes. It mainly covers Brainstorming, Flowchart, Deployment Flowchart, PERT Diagram, Cause-and-Effect Diagram. It may seem elementary. But I still find it rewarding, and I have summarized the learnings in the following paragraphs. First, honesty is a prerequisite for useful
making sense of data
Chapter 2 discusses how to visualize processes and systems. Chapter 3 talks about some issues in collecting data so that we can avoid them when analyzing systems. The chapter mainly consists of three parts: general requirements to collect good data, definitions that are operational, and some common pitfalls in interpreting
making sense of data
Making Sense of Data, Donald J. Wheeler The chapter inspires me a lot. As a data professional, I believe in accurate measurements. We set metrics, KPI for our organizations to monitor how well our business performs. The metrics and KPI are usually result-oriented, for example, how many delinquency cases, how
effective data storytelling
This chapter further illustrates the psychological barriers of understanding and accepting new findings. If I have to use one sentence to summarize the chapter, it would be apart from logic, emotions also play an important role in decision making. The author quotes the idea from a book called Thinking Fast
effective data storytelling
Continue on Chapter 1, the author tries to persuade readers on why to tell stories with data. He provides some psychological experiments done to prove his points. Apart from that, he also mentioned the three elements of the data story in which he will devote one to two chapters to
effective data storytelling
I am currently reading a book called Effective Data Storytelling: How to Drive Change With Data, Narrative, and Visuals written by Brent Dykes. I found it quite enlightening, and want to write summary on each chapters for my own note taking, and at the same time, sharing with all of