Knowledge, Skills, and Abilities for Entry‐Level Business Analytics Positions: A Multi‐Method Study

In recent years, analytics has gained considerable momentum in both industry as well as academia. This study aims to develop an analytics curriculum for undergraduate and graduate programs by identifying skill‐based gaps between industry and academia and then clustering them based on methodological and semantic similarities among other criteria. Specifically, we compare industry requirements and related skills for analytics jobs to three types of analytics domains, that is, descriptive, predictive, and prescriptive analytics. We also address several sought‐after intangible student characteristics, such as communication skills, intellectual curiosity, and business acumen. We then cluster these skills by similarity based upon methodology, skill type, and semantics to develop courses based on these clusters. Our study thus provides a comprehensive analysis linking specific industry needs to specific coursework that allows any university to create a well‐rounded analytics program at both the undergraduate and graduate level. Finally, realizing the significant difference between undergraduate and graduate students in terms of expectations and maturity, we use personality‐job fit theory to recommend strategies to better promote the field to undergraduate students. Regarding the latter, we recommend best practices that graduate analytics program can employ to be easy to market and sell to prospective students.

Section: Literature Reviewmentioning

confidence: 99%

Analytics Curriculum for Undergraduate and Graduate Students

Paul

MacDonald

2020

“…We use the research study developed by DeVeaux et al. (2018) and Cegielski and Jones‐Farmer () to determine the key competencies of analytics (KCA) and potential CSKT that corresponds with each KCA as shown in Table .…”

Section: Industry Needs—indeedcom Web Scraping and Focus Group Analysismentioning

confidence: 99%

Exploratory Analysis to Identify Concepts, Skills, Knowledge, and Tools to Educate Business Analytics Practitioners

Johnson

Albizri

Jain

2020

Recent technological advancements in data storage and processing have changed how companies conduct their business. An increasing number of firms have started putting their efforts in extracting information from their databases to improve profitability and reduce costs using quantitative approaches. Thus, the job market has been experiencing a rapidly growing demand for business analytics (BA) practitioners, and universities across the globe are increasingly responding to this newly emerged field by offering both undergraduate and graduate level degrees as well as certificate programs. Thus, this research aims to provide a framework for academic institutions to develop a state‐of‐the‐art master's in business analytics (MSBA) curriculum by identifying concepts, skills, knowledge, and tools (CSKT) that industry seeks in BA practitioners. Our data‐driven methodology utilizes peer institution analysis, indeed.com web scraping, and focus group analysis with mid‐ and senior‐level analytics leaders from major companies. Our contribution to the literature and recommendations to institutions developing MSBA programs are offered at the end.

“…There is also considerable buzz about statistically derived insights from big data (16,17) with related interest in the programs and techniques employed to address them (DevX, ). Although not directly referenced in Figure , data visualization is also experiencing a rising profile among data‐focused organizations (Cegielski & Jones‐Farmer, ; de Jager & Brown, ; Wixom, et al., ; Acito & Khatri, ). Visualization capabilities are embedded in software platforms like SAS (4) and R (13).…”

Section: Demand For Analyticsmentioning

confidence: 99%

Planning for Low End Analytics Disruptions in Business School Curricula

Rienzo

Chen

2018

Analytics is getting a great deal of attention in both industrial and academic venues. Organizations of all types are becoming more serious about transforming data from a variety of sources into insight, and analytics is the key to that transformation. Academic institutions are rapidly responding to the demand for analytics talent, with hundreds of offerings aimed at producing a broad range of analytical graduates from data scientists to data‐savvy managers and functional specialists. Curricula generally provides best practice methods of tackling descriptive, predictive, and prescriptive analytics; but there has been little discussion about the disruptive nature of increasingly robust analytical tools in the academic space. The net effect of astounding tool capability is empowerment of less technically trained people to address analytical complexities heretofore only comprehensible to data scientists with in‐depth knowledge of mathematics, programming, and statistics. This article examines skills needed for analytics in industry, academic response, and evolving analytic needs through the lens of disruption theories of Clayton Christensen. We offer a direction of curriculum development by supplementing the disruption theory with three interactive components of problem space mapping: processes, tools, and techniques. The challenge for academicians is a dynamic, adaptable curricula addressing multiple levels of data analyses.