Enterprise Data Analysis and Visualization: An Interview Study

Kandel, Sean; Paepcke, Andreas; Hellerstein, Joseph M.; Heer, Jeffrey

doi:10.1109/tvcg.2012.219

Cited by 341 publications

(303 citation statements)

References 33 publications

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“…Of particular relevance to our research is a study that interviewed 35 data analysts from 25 organizations to investigate the challenges and barriers that analysts face [21]. Common issues were provenance, the validity and consistency of assumptions, and the sensitivity of findings to choices made during analysis (e.g., parameter settings).…”

Section: Framework and Characterizationsmentioning

confidence: 99%

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Visualization System Requirements for Data Processing Pipeline Design and Optimization

Landesberger

Fellner

Ruddle

2017

IEEE Trans. Visual. Comput. Graphics

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Abstract-The rising quantity and complexity of data creates a need to design and optimize data processing pipelines -the set of data processing steps, parameters and algorithms that perform operations on the data. Visualization can support this process but, although there are many examples of systems for visual parameter analysis, there remains a need to systematically assess users' requirements and match those requirements to exemplar visualization methods. This article presents a new characterization of the requirements for pipeline design and optimization. This characterization is based on both a review of the literature and first-hand assessment of eight application case studies. We also match these requirements with exemplar functionality provided by existing visualization tools. Thus, we provide end-users and visualization developers with a way of identifying functionality that addresses data processing problems in an application. We also identify seven future challenges for visualization research that are not met by the capabilities of today's systems.

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Section: Framework and Characterizationsmentioning

confidence: 99%

“…The wider context in which the design process is conducted also influences the choices that users make during pipeline design and optimization (e.g., see [21]). This wider context is outside the scope of the present paper, and we limit our discussion to highlighting three key factors.…”

Section: Requirement Characterizationmentioning

confidence: 99%

Visualization System Requirements for Data Processing Pipeline Design and Optimization

Landesberger

Fellner

Ruddle

2017

IEEE Trans. Visual. Comput. Graphics

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“…Unlike hackers, they have more statistical and less technical proficiency. The last group, application users, work with spreadsheets or analysis applications such as SAS or SPSS [4].…”

Section: Research Backgroundmentioning

confidence: 99%

Data Scientistsâ€™ Skills in Detecting Archetypes in Iran

Iraj

Sohrabi

2017

JOIV : Int. J. Inform. Visualization

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Abstract-The use of data-driven decision making and data scientists is on the rise in Iran as companies have rapidly been focusing on gathering data and analyzing it to guide corporate decisions. In order to facilitate the process and understand the nature and characteristics of this transformation, the current study intends to learn about data scientists' skills and archetypes in Iran. Detecting skills archetypes has been done via analyzing the skills of data scientists which were self-expressed through an online survey. The results revealed that there are three archetypes of data scientists including high level data scientists, low level data scientists and software developers. The archetypal patterns are based on levels of data scientists' skills rather than the type of dominant skills they possess which was the most frequent pattern in previous studies.

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“…Many researches are dedicated of usage of DV in BI&A, firstly because of power they give to decision makers with visual display allowed [10,12]. The flow of enormous volume of data that can be analyzed for trends identification and predictions, bring additional values for companies.…”

Section: Related Workmentioning

confidence: 99%

Data Visualization in Business Intelligent &Analysis – Analysis of First Positioned Tools According to Gartner’s Magic Quadrant in Ability to Execute

Savoska

Bocevska²

2016

Proccedings of the ICAIIT2016

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-Data Visualization tools in Business Intelligent (BI) and Analysis are very effective because they allow gaining of deeper understanding of huge amounts of data stored in databases. For this reasons many market research companies take into consideration usage of Data Visualization tools as part of their BI solutions and analyze their competitive advantage at the market as well as the benefits and disadvantages. In this paper, the Data Visualization tools that are on the top of Gartner and Forrester researches, Tableau and Qlik, are taken into consideration. They are positioned higher on the "Ability to execute" axis and according to researchers' report, are faster growing sales tools and deserve analyses in details. They are used as Visual Data Analysis (VDA) tools from theoretical and practical side and are analyzed for previous defined Key Performance Indicators in order to gain deeper insights and make a comparison of their ability to execute.

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Enterprise Data Analysis and Visualization: An Interview Study

Cited by 341 publications

References 33 publications

Visualization System Requirements for Data Processing Pipeline Design and Optimization

Visualization System Requirements for Data Processing Pipeline Design and Optimization

Data Scientistsâ€™ Skills in Detecting Archetypes in Iran

Data Visualization in Business Intelligent &Analysis – Analysis of First Positioned Tools According to Gartner’s Magic Quadrant in Ability to Execute

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