Modernizing business analytics capability with DataOps: A decision-making agility perspective
2020
- 642Usage
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Usage642
- Abstract Views566
- Downloads76
Article Description
Increased proliferation of new data sets and the persistent desire to stay ahead of the competition has compelled organizations to harness business analytics (BA) to drive strategic business decisions. However, issues such as the lack of collaboration among stakeholders, poor data quality, and slow delivery times are inhibiting the ability of organizations to utilize BA capability for making datadriven decisions in an agile manner. Data operations (DataOps), an emerging discipline, is believed to have the potential to modernize the BA capablity in such way to improve collaboration, data quality and accelrate delivery times, thereby enhancing the speed and accuracy of data-driven decisions. The goal of this on-going study is to examine how organizations can modernize their BA capability by employing DataOps. We propose a theoretical framework that explains how key components of BA capability (people, process, technology, and organization) are impacted by DataOps and how BA capability enabled by DataOps improves decision-making agility. We plan to: a) conduct expert interviews with BA professionals involved in DataOps implementations in both the consulting and corporate domains and b) conduct multiple in-depth case studies with organizations that have matured in their practice of DataOps to explain how DataOps modernizes BA capability.
Bibliographic Details
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