06 October 2018

Data Science: Users (Just the Quotes)

"Knowledge is a potential for a certain type of action, by which we mean that the action would occur if certain tests were run. For example, a library plus its user has knowledge if a certain type of response will be evoked under a given set of stipulations." (C West Churchman, "The Design of Inquiring Systems", 1971)

"To conceive of knowledge as a collection of information seems to rob the concept of all of its life. Knowledge resides in the user and not in the collection. It is how the user reacts to a collection of information that matters." (C West Churchman, "The Design of Inquiring Systems", 1971)

"Averages, ranges, and histograms all obscure the time-order for the data. If the time-order for the data shows some sort of definite pattern, then the obscuring of this pattern by the use of averages, ranges, or histograms can mislead the user. Since all data occur in time, virtually all data will have a time-order. In some cases this time-order is the essential context which must be preserved in the presentation." (Donald J Wheeler," Understanding Variation: The Key to Managing Chaos" 2nd Ed., 2000)

"Visualizations can be used to explore data, to confirm a hypothesis, or to manipulate a viewer. [...] In exploratory visualization the user does not necessarily know what he is looking for. This creates a dynamic scenario in which interaction is critical. [...] In a confirmatory visualization, the user has a hypothesis that needs to be tested. This scenario is more stable and predictable. System parameters are often predetermined." (Usama Fayyad et al, "Information Visualization in Data Mining and Knowledge Discovery", 2002)

"Dashboards and visualization are cognitive tools that improve your 'span of control' over a lot of business data. These tools help people visually identify trends, patterns and anomalies, reason about what they see and help guide them toward effective decisions. As such, these tools need to leverage people's visual capabilities. With the prevalence of scorecards, dashboards and other visualization tools now widely available for business users to review their data, the issue of visual information design is more important than ever." (Richard Brath & Michael Peters, "Dashboard Design: Why Design is Important," DM Direct, 2004)

"Data are raw facts and figures that by themselves may be useless. To be useful, data must be processed into finished information, that is, data converted into a meaningful and useful context for specific users. An increasing challenge for managers is being able to identify and access useful information." (Richard L Daft & Dorothy Marcic, "Understanding Management" 5th Ed., 2006)

"[...] construction of a data model is precisely the selective relevant depiction of the phenomena by the user of the theory required for the possibility of representation of the phenomenon."  (Bas C van Fraassen, "Scientific Representation: Paradoxes of Perspective", 2008)

"The thread that ties most of these applications together is that data collected from users provides added value. Whether that data is search terms, voice samples, or product reviews, the users are in a feedback loop in which they contribute to the products they use. That’s the beginning of data science." (Mike Loukides, "What Is Data Science?", 2011)

"What is really important is to remember that no matter how creative and innovative you wish to be in your graphics and visualizations, the first thing you must do, before you put a finger on the computer keyboard, is ask yourself what users are likely to try to do with your tool." (Alberto Cairo, "The Functional Art", 2011)

"As data scientists, we prefer to interact with the raw data. We know how to import it, transform it, mash it up with other data sources, and visualize it. Most of your customers can’t do that. One of the biggest challenges of developing a data product is figuring out how to give data back to the user. Giving back too much data in a way that’s overwhelming and paralyzing is 'data vomit'. It’s natural to build the product that you would want, but it’s very easy to overestimate the abilities of your users. The product you want may not be the product they want." (Dhanurjay Patil, "Data Jujitsu: The Art of Turning Data into Product", 2012)

"By giving data back to the user, you can create both engagement and revenue. We’re far enough into the data game that most users have realized that they’re not the customer, they’re the product. Their role in the system is to generate data, either to assist in ad targeting or to be sold to the highest bidder, or both." (Dhanurjay Patil, "Data Jujitsu: The Art of Turning Data into Product", 2012)

"Generalizing beyond advertising, when building any data product in which the data is obfuscated (where there isn’t a clear relationship between the user and the result), you can compromise on precision, but not on recall. But when the data is exposed, focus on high precision." (Dhanurjay Patil, "Data Jujitsu: The Art of Turning Data into Product", 2012)

"Successful information design in movement systems gives the user the information he needs - and only the information he needs - at every decision point." (Joel Katz, "Designing Information: Human factors and common sense in information design", 2012) 

"You can give your data product a better chance of success by carefully setting the users’ expectations. [...] One under-appreciated facet of designing data products is how the user feels after using the product. Does he feel good? Empowered? Or disempowered and dejected?" (Dhanurjay Patil, "Data Jujitsu: The Art of Turning Data into Product", 2012)

"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)

"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)

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