How can institutions best demonstrate the value of analytics to answer important questions so that they can make better information-based decisions and better align their efforts with strategies, goals, and objectives? Here are three value propositions we discuss with our customers and partners.
Let's First Start at the Beginning
Higher education institutions face many dilemmas today from growing enrollment to lowering costs to improving student outcomes to deciding which programs to expand and which to retire. Analytics can provide answers to these difficult questions, but too few institutions have a strategy and a culture to use all institutional data in the moments that matter. It's well documented that when well-directed analytics can improve student retention, provide key insights on program viability, and predict applicant conversion, enrollment, and endowment.
So why is it that many institutions fail to realize this potential? I see three primary reasons. First, data and systems are siloed across the institution creating tension between IT and administration. Second, the analytical tools are too limited in scope and are not connected to the daily work at hand. And third, the technology is too difficult to use broadly across the institution. But above all, failure to use data strategically is a void in leadership.
Provosts, presidents and senior leadership must ask more difficult questions breaking down the data silos in the institution. Many leaders stop asking difficult questions because they recognize the constraints of the data silos that exist in the institution. Shifting focus from what happened to why some outcome didn't meet expectations is essential. In my experience, most administrative leaders spend 90 percent of their time collecting facts about what happened, and 10 percent of their time understanding the cause and effect element. Furthermore, leaders must let the data do the talking and stop looking for data to support a hypothesis or a political agenda. At Oracle, we call this AI for the Why.
AI for the Why is a unique opportunity to leverage embedded machine-learning techniques to de-bias data analysis to find the true explanation on why enrollment didn't meet expectations or why middle-income, first-time students didn't convert in Chicago. When data does the talking, profound change can occur, and leaders need to inspire this approach. Variables that seem irrelevant can and often are connected with an outcome but go undiscovered because most analysts keep analyzing the same data to explain an outcome when what's needed is a capability to find patterns automatically and brings those insights into the decision at hand. Gartner calls this capability Augmented Analytics where embedded AI can help augment the cognitive decision making of humans and deepen the rigor and viability of the analysis.
Develop Successful Collaboration
The rapid rate of technology change requires different collaboration model between solution providers and higher education leaders. The greatest collaboration opportunity is in the area of academic program development. The demand for data-savvy business professionals is profound, and the supply from higher education continues to fall short. Every solution provider should, at a minimum, offer to participate in the classroom on analytic best practices and ideally partner with the academic leadership team to guide the development or evolution of analytic programs. Ideally, the solution provider should offer to provide real world industry use cases and data for students to practice their new skill. At Oracle, we have partnered with California Polytechnic State University, San Luis Obispo, Orfalea College of Business to advance their program, and the best collaboration was the data science competition.
Above and beyond the academic collaboration, great partnerships focus on best practice sharing and community development to promote excellence in analytics. Workshops, special interest groups, and online forums are a great way to advance your experience with analytic technologies.
Build Cross-Institutional Communications
Analytics is a team sport and requires many unique skills and coaches. To develop a data culture, it's vital that when teams or individuals win with analytics that their success is promoted internally. One way to do this is by building a virtual 'data club.' The role of the data club is to be advocates for success in analytics. Leading organizations make it fun, agile, and outcome based. Data clubs are made up of cross-functional analytic leaders, and members are positioned as the thought leaders in their field. Data clubs aren't like the old-skool competency center as they aren't focused on technology standards but promoting outcomes reached through the strategic use of data. By linking analytic initiatives to the mission of the institution, it will become more apparent to non-data savvy managers that they need to modernize their skills and explore more data.
Aim For Analytics Maturity
In working with many institutions over the years, I have found at least five common attributes among high analytic performers. First, great analytic leaders embrace new technologies and let AI reduce bias in decision making. When embedded in analytic processes, AI can bring many insights which would otherwise go unnoticed. Second, the formation of a data lab for experimentation and discovery is essential to growing the analytical capabilities of the institution. Third is the aspect of the use of storytelling and visualization. Beautifying insights for greater comprehension is a core competency in leading institutions. Fourth is the ability to connect plans with shared assumptions. Too often, analysis and planning are separate and not connected, and great institutions have linked these processes to improve operational efficiency. Finally, and most important is the creation of a talent development plan for data literacy. Analytic skills in the workforce need continuous nurturing, and those who have a plan to build their competencies in analytics will create better student and institutional outcomes.
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