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Data technology providers on analytics usage barriers

Industry leaders discuss the biggest roadblock to effective use of data analytics tools as they relate to student success
University Business, June 2017

What is the biggest roadblock to effective use of data analytics tools as they relate to student success?

“Many view data as a punitive measure to highlight performance flaws when, in reality, analytics tools help identify trends and answer questions about the student experience. Institutions must first address the campus culture surrounding data and demonstrate the benefits of analytics as a means to foster change across the organization.” —Robert Dolan, Jr., market segment director for public sector, Tableau

“Lack of visibility into the connections between financial, enrollment and student outcome data. Many institutions look at their data in silos and don’t have the ability to efficiently and quickly view a comprehensive picture of their students, much of which exists outside of the student success realm.” —Jack Neill, VP of client services, HelioCampus

“So much investment is being made to get to the nth-level data point. But the real power of the data is how it’s interpreted and acted upon. The insights must become the basis for action - proactive, persistent and personalized engagement that measurably improves results.”—Matthew Schnittman, CEO, Helix Education

“Data management continues to be a major challenge for universities needing to deploy a data visualization and analytics platform. Even when disparate departments agree to share their data, universities struggle with extracting, integrating and cleaning that disparate data, then preparing it in a structured way for reporting and analytics.” —Georgia Mariani, product marketing manager for education, SAS

“Universities have a lot of data. Yet, when we talk to those open to innovation we find that the data required to evaluate a solution has to be cleaned and made consistent across the institution. And, often the data needed to implement the solution must be collaboratively developed from scratch.” —Elena M. Cox, founder and CEO, vibeffect

“Inconsistency and the quality of the business processes that generate the data are big challenges for campuses trying to use analytics to drive student success. When data is generated differently across multiple systems it creates noise and undermines confidence in the analytics themselves, slowing adoption.” —Chris Chumley, Chief Operating Officer, CampusLogic

“The effectiveness of data analytics tools in driving student success is stymied because the student perspective is an afterthought—administrators are often the primary audience. What could be accomplished with a students-first approach—a new crucial ally in the fight, armed with increased agency and accountability for their academic success.” —Margo Wright, CEO, Yenko

“The intricacy of the higher education hierarchy can create complexity around who is responsible for managing data analytics tools. This can sometimes make it difficult to determine which departments will train employees and provide access to the important information gleaned from data analytics – information that ultimately will help students.” —David Doucette, director of higher education, CDW-G

“When purchasing data analytics tools, institutions often view technology as the solution. In reality, improving student success hinges on having the people and processes in place to effectively leverage the information these tools provide. Failing to recognize this fact is the biggest roadblock to using data analytics tools effectively.” —Michael Taft, ZogoTech CEO

“In my observations, the biggest roadblock is organizational. That is, given reliable student success data, whose job is it to reach out and interact with the student? While this seems basic, things like organizational lines, expected responsibilities, and accountability make this a challenging question to answer.” —Mike Sharkey, vice president of analytics, Blackboard

“Often people struggle with understanding where the data lives in different systems on campus and how to access it. Once that hurdle is overcome then it can be a challenge deciding on what variables to include for predictive models. Having an easy way to identify what variables are potentially predictive in your own data is very important.” —Jon MacMillan, senior analyst, Rapid Insight

“A proactive mindset. Most schools are not used to offering support to students before they struggle. So having the right people in the right position to lead the charge is a big change for them.” —Kimberley Munzo, president/CEO, AspireEDU

“Research by Erik Brynjolsson at MIT shows the largest productivity gains in IT are realized by firms that couple technology investments with changes in their organizational structure and business processes. The lesson for data analytics is that improvements in student success require, not only technology investments, but also re-doubled commitment by institutions and instructors to transform teaching and learning practices.” —Alfred Essa, vice president, analytics and R&D, McGraw-Hill Education


Dawn Papandrea is a Staten Island, New York-based writer and frequent contributor to UB.

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