✏️Danyel Fisher - Collected Quotes

"A dimension is an attribute that groups, separates, or filters data items. A measure is an attribute that addresses the question of interest and that the analyst expects to vary across the dimensions. Both the measures and the dimensions might be attributes directly found in the dataset or derived attributes calculated from the existing data." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

"A well-operationalized task, relative to the underlying data, fulfills the following criteria: (1) Can be computed based on the data; (2) Makes specific reference to the attributes of the data; (3) Has a traceable path from the high-level abstract questions to a set of concrete, actionable tasks." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

"An actionable task means that it is possible to act on its result. That action might be to present a useful result to a decision maker or to proceed to a next step in a different result. An answer is actionable when it no longer needs further work to make sense of it." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

"Every dataset has subtleties; it can be far too easy to slip down rabbit holes of complications. Being systematic about the operationalization can help focus our conversations with experts, only introducing complications when needed." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

"Color is difficult to use effectively. A small number of well-chosen colors can be highly distinguishable, particularly for categorical data, but it can be difficult for users to distinguish between more than a handful of colors in a visualization. Nonetheless, color is an invaluable tool in the visualization toolbox because it is a channel that can carry a great deal of meaning and be overlaid on other dimensions. […] There are a variety of perceptual effects, such as simultaneous contrast and color deficiencies, that make precise numerical judgments about a color scale difficult, if not impossible." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

"Creating effective visualizations is hard. Not because a dataset requires an exotic and bespoke visual representation - for many problems, standard statistical charts will suffice. And not because creating a visualization requires coding expertise in an unfamiliar programming language [...]. Rather, creating effective visualizations is difficult because the problems that are best addressed by visualization are often complex and ill-formed. The task of figuring out what attributes of a dataset are important is often conflated with figuring out what type of visualization to use. Picking a chart type to represent specific attributes in a dataset is comparatively easy. Deciding on which data attributes will help answer a question, however, is a complex, poorly defined, and user-driven process that can require several rounds of visualization and exploration to resolve." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

"Dashboards are a type of multiform visualization used to summarize and monitor data. These are most useful when proxies have been well validated and the task is well understood. This design pattern brings a number of carefully selected attributes together for fast, and often continuous, monitoring - dashboards are often linked to updating data streams. While many allow interactivity for further investigation, they typically do not depend on it. Dashboards are often used for presenting and monitoring data and are typically designed for at-a-glance analysis rather than deep exploration and analysis." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

"Designing effective visualizations presents a paradox. On the one hand, visualizations are intended to help users learn about parts of their data that they don’t know about. On the other hand, the more we know about the users’ needs and the context of their data, the better we can design a visualization to serve them." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

"Dimensionality reduction is a way of reducing a large number of different measures into a smaller set of metrics. The intent is that the reduced metrics are a simpler description of the complex space that retains most of the meaning. […] Clustering techniques are similarly useful for reducing a large number of items into a smaller set of groups. A clustering technique finds groups of items that are logically near each other and gathers them together." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

"Maps also have the disadvantage that they consume the most powerful encoding channels in the visualization toolbox - position and size - on an aspect that is held constant. This leaves less effective encoding channels like color for showing the dimension of interest." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

"[…] no single visualization is ever quite able to show all of the important aspects of our data at once - there just are not enough visual encoding channels. […] designing effective visualizations to make sense of data is not an art - it is a systematic and repeatable process." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

"[…] the data itself can lead to new questions too. In exploratory data analysis (EDA), for example, the data analyst discovers new questions based on the data. The process of looking at the data to address some of these questions generates incidental visualizations - odd patterns, outliers, or surprising correlations that are worth looking into further." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

"The field of [data] visualization takes on that goal more broadly: rather than attempting to identify a single metric, the analyst instead tries to look more holistically across the data to get a usable, actionable answer. Arriving at that answer might involve exploring multiple attributes, and using a number of views that allow the ideas to come together. Thus, operationalization in the context of visualization is the process of identifying tasks to be performed over the dataset that are a reasonable approximation of the high-level question of interest." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

"The general concept of refining questions into tasks appears across all of the sciences. In many fields, the process is called operationalization, and refers to the process of reducing a complex set of factors to a single metric. The field of visualization takes on that goal more broadly: rather than attempting to identify a single metric, the analyst instead tries to look more holistically across the data to get a usable, actionable answer. Arriving at that answer might involve exploring multiple attributes, and using a number of views that allow the ideas to come together. Thus, operationalization in the context of visualization is the process of identifying tasks to be performed over the dataset that are a reasonable approximation of the high-level question of interest." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

"The goal of operationalization is to refine and clarify the question until the analyst can forge an explicit link between the data that they can find and the questions they would like to answer. […] To achieve this, the analyst searches for proxies. Proxies are partial and imperfect representations of the abstract thing that the analyst is really interested in. […] Selecting and interpreting proxies requires judgment and expertise to assess how well, and with what sorts of limitations, they represent the abstract concept." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

"The operationalization process is an iterative one and the end point is not precisely defined. The answer to the question of how far to go is, simply, far enough. The process is done when the task is directly actionable, using the data at hand. The analyst knows how to describe the objects, measures, and groupings in terms of the data - where to find it, how to compute, and how to aggregate it. At this point, they know what the question will look like and they know what they can do to get the answer." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

"The intention behind prototypes is to explore the visualization design space, as opposed to the data space. A typical project usually entails a series of prototypes; each is a tool to gather feedback from stakeholders and help explore different ways to most effectively support the higher-level questions that they have. The repeated feedback also helps validate the operationalization along the way." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

"Rapid prototyping is a process of trying out many visualization ideas as quickly as possible and getting feedback from stakeholders on their efficacy. […] The design concept of 'failing fast' informs this: by exploring many different possible visual representations, it quickly becomes clear which tasks are supported by which techniques." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

"Too many simultaneous encodings will be overwhelming to the reader; colors must be easily distinguishable, and of a small enough number that the reader can interpret them."  (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

"Visualizations provide a direct and tangible representation of data. They allow people to confirm hypotheses and gain insights. When incorporated into the data analysis process early and often, visualizations can even fundamentally alter the questions that someone is asking." (Danyel Fisher & Miriah Meyer, "Making Data Visual", 2018)

✏️Dina Gray - Collected Quotes

"Although performance measurement is often linked to tools such as scorecards, dashboards, performance targets, indicators and information systems, it would be naïve to consider the measurement of performance as just a technical issue. Indeed, measurement is often used as a way of attempting to bring clarity to complex and confusing situations." (Dina Gray et al, "Measurement Madness: Recognizing and avoiding the pitfalls of performance measurement", 2015)

"'Big Data" is certainly changing the way organizations operate, and our capacity to do planning, budgeting and forecasting, as well as the management of our processes and supply chains, has radically improved. However, greater availability of data is also being accompanied by two major challenges: firstly, many managers are now required to develop data-oriented management systems to make sense of the phenomenal amount of data their organizations and their main partners are producing. Secondly, whilst the volume of data that we now have access to is certainly seductive and potentially very useful, it can also be overwhelming." (Dina Gray et al, "Measurement Madness: Recognizing and avoiding the pitfalls of performance measurement", 2015)

"[...] introducing an excessive number of measures is only the start of the problem. The other is that measures tend to stick, unless questioned and revised. As the world changes, so does the environment in which an organization operates. Priorities change, new drivers of performance emerge, and different operating models are employed. It would therefore make sense that the performance measurement system is also revised to reflect these changes." (Dina Gray et al, "Measurement Madness: Recognizing and avoiding the pitfalls of performance measurement", 2015)

"Measurement is often associated with the objectivity and neatness of numbers, and performance measurement efforts are typically accompanied by hope, great expectations and promises of change; however, these are then often followed by disbelief, frustration and what appears to be sheer madness." (Dina Gray et al, "Measurement Madness: Recognizing and avoiding the pitfalls of performance measurement", 2015)

"Measurement is often seen as a tool that helps reduce the complexity of the world. Organizations, with their uncertainty and confusion, are full of people, patterns and trends; and measurement seems to offer a promise of bringing order, rationality and control into this chaos." (Dina Gray et al, "Measurement Madness: Recognizing and avoiding the pitfalls of performance measurement", 2015)

"One of the most puzzling things about performance measurement is that, regardless of the countless negative experiences, as well as a constant stream of similar failures reported in the media, organizations continue to apply the same methods and constantly fall into the same traps. This is because commonly held beliefs about the measurement and management of performance are rarely challenged." (Dina Gray et al, "Measurement Madness: Recognizing and avoiding the pitfalls of performance measurement", 2015)

"Performance measures by themselves are simply tools that may or may not be used by managers and staff. However, if your organization has an addiction to measurement, sooner or later people will start relying on measures excessively, and common sense will gradually begin to be replaced by the measures themselves leading the organization into the eye of the measurement madness hurricane." (Dina Gray et al, "Measurement Madness: Recognizing and avoiding the pitfalls of performance measurement", 2015)

"Regularly, and unfortunately more often than might be expected, organizations can become so fixated on the narrow task of measuring and reporting performance that measures lose their meaning, and no one relies on them for real decision-making. [...] More worryingly, sometimes performance measures are introduced without any intention of providing meaningful data for making decisions in the first place. In this case, such indicators are often treated with contempt." (Dina Gray et al, "Measurement Madness: Recognizing and avoiding the pitfalls of performance measurement", 2015)

"Since perfect measures of performance do not exist, organizations use proxies - indicators that approximate or represent performance in the absence of perfect measures. [...] Over time, proxies are perceived to rep￾resent true performance." (Dina Gray et al, "Measurement Madness: Recognizing and avoiding the pitfalls of performance measurement", 2015)

"When all you see and believe is numbers, it becomes increasingly difficult to decide when to react and intervene. [...] The most obvious course of action is to set aside the numbers and try to understand the underlying causes of these changes. However, the over-reliance on measurement instead drives many managers to design 'thresholds' or 'colour codes' for numbers, thus adding another layer of abstraction to measurement and keeping these managers firmly desensitized to the meaning of per￾formance information." (Dina Gray et al, "Measurement Madness: Recognizing and avoiding the pitfalls of performance measurement", 2015)