Key Management and Governance Challenges when Executing Data Science / Analytics Projects

Saltz, Jeffrey and Armour, Frank and Goul, Michael and Sharda, Ramesh (2018) Key Management and Governance Challenges when Executing Data Science / Analytics Projects. Oklahoma State University.

Key Management and Governance Challenges when Executing Data Scie.pdf

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Big data, data science and analytics have become increasingly important strategic assets because they can help organizations make better decisions, discover new insights, competitively differentiate, and they enable the embedding of intelligence into automated processes so organizations can efficiently respond at the speed of business. Effective organizational management and governance of data science practices are necessary in order to mitigate risks associated with analytics deployment. For example, organizations need to capture and manage critical meta-information detailing modeling and environmental assumptions underlying the analytics solutions, they also need to establish policies and a culture designed to ensure adherence to the highest ethical standards of data management and predictive model deployment. At a higher level, unleashing machine learning algorithms may require safeguards and risk mitigation monitoring to address these types of socio-technical challenges. This panel will foster a debate with respect to what are the most important concerns or potential issues that an organization should focus on while executing a data science/analytics project. Via a debate, the panel, along with the audience, will explore the field of data science and predictive analytics, and what are the key project risks that need to be mitigated.

Item Type: Article
Subjects: H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
Divisions: Faculty of Industrial Engineering and Informatics > Information System
Depositing User: staff repository 2
Date Deposited: 14 Sep 2018 17:07
Last Modified: 14 Sep 2018 17:07

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