Purpose The assessment of the achievement of the goal is data driven and uses analytical modeling of the forecasted demand and the projected electrical power generated capacities. The analysis is based on compiled granular observations and is compared to reported aggregated information. The model takes into account changes in demand patterns such as the increase in the use of electric cars, and decreased demand because of improving efficiencies and behind-the-meter generation. Design/methodology/approach This case study is designed to examine one of the major goals of New York State (NYS) Governor Andrew Cuomo’s energy plan, namely, that 50% of all electric generation will come from renewable energy resources by the year 2030. The aim is to compare the aspiration of the political policy with the reality of its implementation. Findings The analyses describe a measurable gap between the achievement of the stated goal and the projected reality. The paper includes discussions on the nature of this gap and factors that could potentially further increase this deficit. Practical implications In addition, the paper highlights the need to recognize the complexities of projecting the future and difficulty of developing aggressive contingencies given practical and political constraints. Originality/value This paper provides a data-driven independent assessment of the NYS’ current energy plan and highlights important issues for consideration if the political promise is ever to become a reality.

Over the past few years, academics have undertaken initiatives to bridge the gap between theory and practice in the ever-growing field of business analytics, including implementing real-life student projects in all shapes and forms. Every year since 2015, Manhattan College has invited student teams from across North America and elsewhere in the world to its campus in order to participate in an intercollegiate business analytics competition (BAC@MC). This well-received event and the objectives behind it are described in this article. The program is shown to serve as an effective experiential learning adventure for the undergraduate students as it hones their data analytic skills in the context of an engaging real-world business problem. The roles various stakeholders play in this high-impact practice are highlighted. Furthermore, an example of a recent competition question is presented (along with a summary of the analytical approaches attempted) by the student teams. Descriptive visualizations, regression, and cluster algorithms implemented using python, R, Excel, or Tableau are among the typical analyses utilized by participating students. As witnessed by the students, faculty advisors, and the industry practitioners who attended the event, competitions such as BAC@MC can be rewarding, community-building, and transformative experiences for undergraduate students who will soon become tomorrow's business analysts.