Data to Knowledge to Results: Building an Analytic Capability
By T. Davenport, Jeanne Harris, David W. De Long, A. L. Jacobson. 2001
328 citations recorded by the source corpus.
Subfields: Biomedical Engineering, Education, Management Information Systems
View Paper: https://www.semanticscholar.org/paper/58d318ac13681a0b6b8e27f4f893d952044ac183
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Education
In Management Information Systems
- Patrick Mikalef
- John Krogstie
- Michail N. Giannakos
- Ilias O. Pappas
- Stefan Michel
- Maryam Ghasemaghaei
- Nima Kordzadeh
- Peter B. Seddon
Citations in the corpus
- An expanded and strategic view of discontinuous innovations: deploying a service-dominant logic (2008, background): “As a second example, data-mining software applications similarly help managers make better decisions on the basis of factual information about customers, combined with model-based algorithms that turn the data into information and then into knowledge (Davenport et al. 2001).”
- Enterprise systems and ongoing process change (2004, background): “However, building a robust analytic capability that uses data to make informed decisions requires far more than mere data access and technological tools (Davenport et al., 2001).”
- Communications of the Association for Information Systems (2011, unknown): “All strategies are designed to be “near-term” – that is, they can all be started immediately with the expectation of yielding results within months.”
- A systematic literature review on the impact of artificial intelligence on workplace outcomes: A multi-process perspective (2021, unknown): “…articles drawing on ‘training and development ’ , has looked at workplace outcomes associated with AI use, such as learning and skill development (Davenport et al., 2001; Nan, 2011; Salge & Vera, 2013), knowledge sharing/transfer (Haug, Hvam, & Mortensen, 2012; Murata & Katayama, 2010; Wang et…”
- Big data analytics capabilities: a systematic literature review and research agenda (2017, methodology): “The core attributes of the data scientist have been distilled to having Fact-based and learning culture Davenport et al. (2001), Davenport (2013), Kiron et al. (2014), Kiron and Shockley (2011), Gupta and George (2016), Lamba and Dubey (2015), Olszak (2014), Kamioka and Tapanainen (2014) and…”