It’s More than Asking Questions and Getting Answers: The Need for Data Translators in Higher Education
To many people, quantitative reasoning is similar to a foreign language — asking someone without an analytics skillset to interpret regression model results can be as meaningful as asking them to read a novel in Greek. Yet, at the same time, it is equally as foreign to ask those who are trained in analytics to fully comprehend every possible theoretical impact a data model will have.
The reality is that making optimal use of sophisticated data requires the abilities of two different individuals, or teams, or offices to meaningfully communicate with one another — the data producers and the data users. But these two groups speak such different languages with such authority that necessary and meaningful conversations may not be easy to have.
With today’s available data warehousing technologies, data managers and information technology specialists have the ability to collect and present an infinite amount of information about students, offices, faculty and institutions as a whole. Enter a new role in analytics — the data translator, an individual who can bridge the worlds of data analytics and use. In the higher education space, this role plays an even more important role than in other areas of business and industry.
There’s no question that higher education has struggled at large with adopting advanced analytics. Much like other areas of information technology, campuses have at times been a step behind other areas of commerce and industry. An effective data translator should help make even those who are most opposed to advanced analytics feel comfortable with data utilization as a tool and resource for campus-wide efforts.
The right person for the job will know enough about methodology and analysis to fluently speak with those crunching the numbers while understanding specific higher education problems well enough to identify campus-specific use cases. Moreover, they should know how to use results to meaningfully communicate stories both internally and externally. Given that a key focus in sharing data should be on substance over methods, data translators understand the benefit of highlighting the meaning and impact of results before describing the techniques used to examine them.
Having someone with a specific skill set to write reports, design infographics and be the public face of analyses will allow institutional data scientists to have more time for more data work and ensure that data consumers receive results in the most digestible way.
But what else exactly will a data translator do — and what will a good one look like? Data translators can wear multiple hats on campus, but their main imperative should be to actively engage with all campus stakeholders to determine the types of questions to explore, understand both the strengths and weaknesses of the campus data ecosystem, and help analytics professionals ensure models are run that lead to actionable analyses and results.
They should play an active role — if not lead — data governance conversations since they will likely have the best understanding on campus regarding who needs access to what and for what reasons. And they should be empowered to encourage all campus stakeholders to think differently. If the goal is merely to please a supervisor, they will be underutilized and fail to harness the full power of data.
A good data translator will have the ability to interact with all of the various personalities on campus. As anyone working on campus today will confirm, colleges and universities are home to some strong and quirky personalities that require a special type of person with whom they can relate. Moreover, the strongest data translators will not only understand higher education but will also either have knowledge about unique campus characteristics or be able to quickly learn them.
Contextualizing intricacies is invaluable to appropriate interpretation. And these individuals will have enough quantitative knowledge to challenge data analytics professionals on their decisions, execution and results while still being able to truly serve as a bridge and relay substantive information back to the larger community.
While presidents and senior leadership continue to push for greater data sophistication and utilization of available data, they are not yet fully valuing the role an individual positioned between the stakeholders asking questions and those running the analyses. And because of the lack of data translators, campuses are not gaining the true potential value of analytics.
