Enterprise Data Analysis and Visualization: An Interview Study

By Sean Kandel, Andreas Paepcke, Joseph M Hellerstein, Jeffrey Heer. 2012

506 citations recorded by the source corpus.

Subfields: Sociology and Political Science

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Citations in the corpus

BoostClean: Automated Error Detection and Repair for Machine Learning (2017, background): “Recent papers and surveys of analysts suggest that such problems are pervasive [27,31,47].”
“Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI (2021, background): “reveal that practitioners consider data wrangling tedious and time-consuming [62].”
PrivateClean: Data Cleaning and Differential Privacy (2016, background): “However, data cleaning is often time consuming [21].”
Distil : A Mixed-Initiative Model Discovery System for Subject Matter Experts ( Demo ) (2018, background): “Data wrangling constitutes the bulk of the data science process (Kandel et al., 2012), encompassing data parsing, normalizing, cleaning, and imputing missing data attributes.”
Materialization optimizations for feature selection workloads (2014, background): “Conﬁrming the literature on feature selection [20,25], we found that features are selected (or not) for many reasons: their statistical performance, their real-world explanatory power, legal reasons, or for some combination of reasons.”
ActiveClean: Interactive Data Cleaning While Learning Convex Loss Models (2016, background): “Data often arrive dirty , including missing, incorrect, or inconsistent attributes, and analysts widely report that data cleaning and other forms of pre-processing account for up to 80% of their effort [3, 22].”
ActiveClean: Interactive Data Cleaning For Statistical Modeling (2016, background): “It is common for analysts to use the preliminary analysis on dirty data as a guide to help identify potential errors and design repairs [20].”
Detecting Data Errors: Where are we and what needs to be done? (2016, background): “Trifacta is the commercial descendant of DataWrangler [22].”
Challenges in Deploying Machine Learning: A Survey of Case Studies (2020, background): “t. Availability of high quality tools is essential for conducting any kind of data analysis. One area that practitioners ﬁnd particularly challenging in that regard is visualization for data proﬁling [27]. Data proﬁling refers to all activities associated with troubleshooting data quality, such as m”
Exploration and Explanation in Computational Notebooks (2018, unknown): “Analysts struggle to track which of the many versions of their code produced a particular result [11, 17].”
A Multi-Level Typology of Abstract Visualization Tasks (2013, methodology): “guide, share [23]* inform, elaborate [83]*, report [27],”
Impediment to insight to innovation: understanding data assemblages through the breakdown–repair process (2016, methodology): “This in turn led to wrangling as a repair strategy (Kandel, Paepcke, Hellerstein, & Heer, 2012), which included techniques such as ﬁ ltering, matching, and calibrating data.”
Interactive and Deterministic Data Cleaning (2016, background): “In fact, removing errors in order to get high quality data takes most of data analysts’ time [31], and some studies predict a shortage of people with the skills and the know-how for these tasks [33].”
A Survey of Visual Analytics Techniques and Applications: State-of-the-Art Research and Future Challenges (2013, background): “Some other new models and guidelines for visual analytics have also emerged in recent years, greatly boosting the advancement of the field[20-26].”
Wisteria: Nurturing Scalable Data Cleaning Infrastructure (2015, background): “But because data cleaning is often specific to the domain, dataset, and eventual analysis, analysts report spending upwards of 80% of their time on problems in data cleaning [9].”
Towards reliable interactive data cleaning: a user survey and recommendations (2016, background): “To understand data cleaning practice, Kandel et al. conducted a seminal interview study of industry to identify the key challenges in data analytics [25].”