[AISWorld] HICSS 52: Analytics, Big Data and Data Science Management and Governance, 2nd CFP

[Michael Goul]

Second Call for Papers

Hawaii International Conference on System Sciences
HICSS-52: January 8-11, 2019 | Grand Wailea, Maui

Deadline for submissions: June 15, 2018

Minitrack:
BIG DATA, DATA SCIENCE AND ANALYTICS MANAGEMENT, GOVERNANCE AND COMPLIANCE

Track:
Organizational Systems and Technology

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.  They can also provide inter-organizational partnerships with sharable, actionable insights leading to significant innovation.  Organizations need new theories, frameworks and methodologies that can help them:

  *   Realize strategies and principles for managing Big Data
  *   Streamline processes to develop and deploy analytical models and machine learning algorithms
  *   Design new KPIs and deploy actionable dashboards
  *   Manage and staff data science teams
  *   Structure and coordinate analytics functions/capabilities within organizations
  *   Design, staff and provide direction to data and analytics governance committees
  *   Manage project and deployment risk, and
  *   Advance analytics capability maturity

What is new?  Organizational roles like Chief Data Officer and Chief Analytics Officer are emerging.  Funding models for prioritizing analytics opportunities are more frequently being discussed.  Centers of Excellence and shared services entities are being created to handle and manage an increasing data science workload.  New agile methodologies are emerging, and building an organization-wide culture of evidence-based management is becoming a competitive necessity.   Big Data resource investment decisions are becoming more complex with the emergence of the Internet of Things.

Effective organizational management and governance of data analytic practices are necessary in order to mitigate risks associated with analytics deployment.  Organizations need to capture and manage critical meta-information detailing modeling and environmental assumptions underlying analytics solutions, and they need to establish policies and a culture designed to ensure adherence to the highest ethical standards of data management and predictive model deployment.  In addition, organizations need new legal analytics tool familiarity and to understand the role of legal analytics in compliance.  Unleashing machine learning algorithms that may take on a life of their own may require safeguards and risk mitigation monitoring.

This minitrack welcomes submissions of original work addressing challenges, theoretical lenses, frameworks, development ideals, evaluation methodologies, strategies and impact studies assessing the implications of Big Data, data science and analytics management, governance, and compliance. We also encourage submissions of research-in-progress as well as those that are practically oriented yet have the potential to make significant contributions to the research community.

Relevant topics for the minitrack include, but are not limited to, the following:


  *   Innovative Big Data, data science and analytics governance approaches
  *   Chief Data Officer and Chief Analytics Officer roles and responsibilities
  *   Data, analytical model and algorithm asset management
  *   Analytics workflow management
  *   Analytic model life-cycle management
  *   Model management platform design
  *   Model compliance management
  *   Model documentation
  *   Ethics of data analytics
  *   Analytics regulatory risks and risk mitigation
  *   Data and model transparency
  *   Business value of analytics governance
  *   Platform economics and strategy
  *   Coordinate IT, analytic and client teams
  *   Analytics documentation and metadata design
  *   Legal implications of analytics governance policies
  *   Organizational implications of advances in the field of legal analytics
  *   Managing and deploying champion and challenger models
  *   Campaign documentation and model reuse
  *   Data and model ownership and contracts
Minitrack Co-Chairs:
Michael Goul (Primary Contact)
Arizona State University
Michael.Goul at asu.edu<mailto:Michael.Goul at asu.edu>
Zhongju Zhang
Arizona State University
Zhongju.Zhang at asu.edu<mailto:Zhongju.Zhang at asu.edu>
Jeffrey Saltz
Syracuse University
jsaltz at syr.edu<mailto:jsaltz at syr.edu>


Michael Goul
Associate Dean for Faculty & Research
W. P. Carey School of Business
Arizona State University
Michael.Goul at asu.edu<mailto:Michael.Goul at asu.edu>  p.480.727-6031  BAC 600

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