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Saturday, May 18, 2019

Big Data Science: Establishing Data-Driven Institutions through Advanced Analytics | Editors' Picks - EDUCAUSE Review

Data analytics can drive decision-making, but to optimize those decisions, stakeholders must couple effective methods with a shared understanding of both the domain and the institutional goals.

Photo: Cecilia Earls
From improving student success to forming optimal strategies that can maximize corporate and foundational relationships, data analytics is now higher education's divining rod. Faculty and administration alike make daily decisions that impact the future of our institutions and our students, recommends Cecilia Earls, Data Scientist in Information Technologies at Cornell University.


Departments establish curriculums; labs invest in new technologies; we admit students, hire faculty, monitor meal plans, and define security protocols. How can we optimize those decisions over the coming years? How do we know if we are meeting our goals? Can we use our data to make better decisions?

I think the answer is yes, but only if we couple the use of state-of-the-art analytical methods with a focused approach to how and when we engage our data to make decisions. Our data strategy must reflect not only our institutional goals, but also the novel ways in which we can now collect and analyze data to attain those goals. Part of my role as a data scientist at Cornell University  is to help guide this strategy by establishing a common understanding of, and vocabulary around, the data-driven decision-making process. 

A Team Effort
Simply hiring a data scientist does not create a data-driven organization. Identifying and realizing relevant and measurable goals through a well thought-out data strategy does, and this requires collaboration. It is essential that data scientists' partner with four types of stakeholders:
  • Visionaries. These are the leaders with a vision of our organization's future. They can identify the areas in which, when influenced by informed decision-making, would result in the greatest impact toward achieving our institutional goals. Bottom line: they know what our "big questions" really should be.
  • Subject experts. Members of our community who deeply understand the area chosen for analysis. We rely on them to identify which variables are important. Subject experts can help guide the analysis because they understand the types of change that are truly possible. If we offer a "solution" that cannot be implemented, it is the wrong solution.
  • Data experts and archivists. These individuals know where the relevant data are stored and how they can be accessed. This group also includes experts on data quality and how the data have been collected.
  • Technology experts. Setting up a secure data ecosystem requires substantial computer expertise and resources. Many data scientists do not have this expertise and need support from those who do.
While this list is not exhaustive, it makes a key point clear: data-driven decision-making is a team effort. To have the desired impact, data solutions in higher education depend on the collective knowledge of visionaries and experts in subject matter, technology, data, and data science who work collaboratively to ensure that our goals are well defined and that our approach is practical. To this end, all engaged team members must have a common understanding of our framing themes, terms, and processes...

The Analysis: Machine Learning and Statistical Inference  
At their core, supervised and unsupervised machine learning and statistical analysis are simply sets of algorithms used to extract useful information from data. While you can expect your data scientist to choose which algorithm to use, everyone on the team should have a basic understanding of what these algorithms do.
Both supervised machine learning algorithms and traditional statistical inference depend on historical data for either:
  • prediction—accurately estimating future outcomes; or
  • estimation—determining which variables are related to the outcome, and how and to what degree they are related to the outcome.
For a given question, decision makers may be interested in prediction, estimation, or both; in any case, this interest must be established prior to the analysis...

Conclusion 
Big data science is taking purchase in higher education, and our diverse institutions provide an exceptionally fertile ground for impactful data-driven decision-making. We are not corporations; we are small, vibrant communities that make decisions every day regarding critical issues such as safety, facilities management, risk management, housing, recruitment, admissions, research support, academic freedom, instruction, campus life, alumni relations, athletics, career services, support services, and healthcare. Each of these components creates independent data stores that, when analyzed collectively, can offer valuable insights for the institution as a whole.

To realize this potential, however, requires that the entire community of decision makers, data and subject experts, technological experts, and analysts work collaboratively and communicate effectively.
Read more...

Source: EDUCAUSE Review