Learning from Analytics Best Practices in Other Sectors

Donald M. Norris

Higher education leaders are grappling with the challenges of using analytics in reimagining and reinventing their processes and practices, in response to changing conditions and “The New Normal.” In the process they are turning to many sources for insight and inspiration.

Some sources are obvious, such as institutions that are successful in using analytics to enhance student access, affordability and success and to focus on outcomes and value. Many for-profit institutions such as Capella University and American Public University System embed predictive analytics in most academic and administrative processes. They use them to manage and demonstrate individual performance and success in achieving targeted outcomes.

Other exemplary enterprises are similar in the nature of their challenges, such as health care organizations that are reinventing medicine around teams and working to improve and demonstrate the “value” of good health care, as reflected in effective results at reasonable costs. Kaiser Permanente, the Mayo Clinic, and the Cleveland Clinic are often cited and written about as delivering great outcomes, based on scrupulous analysis of most efficacious care and acute attention to managing costs. They deliver indisputable value.
And some best practices enterprises are dramatically different in context and organizational culture but still useful. These examples include commercial enterprises that use analytics to better and continuously understand their customer’s needs, measure satisfaction with their performance, and optimize enterprise performance, over time, in the face of changing conditions and withering competition. The best of these performers focus their attention on analytics relating to innovation that will enhance their long-term competitive standing.

Even if the circumstances are different, however, what these best practice applications from other sectors illustrate a stark fact: The prevailing analytics applications, practices, and culture in traditional higher education are positively primitive compared to best practices in for-profit education, leading health care entities, and top-performing commercial enterprises. The other sectors embed analytics in all processes and practices, use them more intensively, and are dedicated to enhancing performance.

Competing on Analytics

Despite the differences between higher education and business, many higher education leaders and practitioners have found Thomas Davenport and Jeanne Harris’s Competing on Analytics to be very useful in higher education, even though it was based primarily on examples from the business world. This book provides several very helpful typologies that address the full range of analytics starting with standard reports and ad hoc queries and ranging through predictive analytics and optimization, through which the enterprise identifies and strives to achieve an optimal strategy in the face of changing conditions and competition. Many institutional practitioners are using these tools to advance and leverage the use of analytics in their organizations.

The work of Thomas Davenport and Jeanne Harris is further showcased this month in their article in The Harvard Business Review on “Competing on Talent Analytics.” In this piece they reveal how top performing organizations are using analytics to get the most out of their people and empower them to become top performers. Building on the same typologies used in Competing on Analytics, Davenport and Harris describe how enterprises move beyond simple data on individual performance and demographics to applications that enable manager to optimize employee performance and satisfaction, manage the enterprise’s talent pool in a manner that maximizes retention and boost performance, and project how the workforce must change to deal with changes in the business environment. In the top-performing commercial enterprise of today, yesterday’s “human resource management” has evolved into “strategic talent management” that relies heavily on the continuous, embedded use of analytics to shape day-to-day and strategic decision making.

Insights from the New Intelligent Enterprise Survey

In an article in the September issue of the MIT Sloan Management Review, Michael S. Hopkins, Steve LaValle and Fred Balboni present the initial findings of a survey by “The New Intelligent Enterprise” Initiative. This survey addressed the question: How do you win with data? It surveyed global executives about turning the data deluge and analytics into competitive advantage. This report offers an early snapshot of how managers are answering the most important questions organizations face. Future reports and findings will be part of “The New Intelligent Enterprise” Initiative.

Through surveys of business leaders and experts the authors of this report by Sloan Management Review have come up with the top ten insights and questions facing organizations as they incorporate the use of analytics in order to make their businesses more efficient and competitive.

• Analytics and the ways they are used in an organization must be made new and innovative. Enterprises are focusing analytics on understanding and leveraging innovation that will position them for long-term success. Top performers place an even greater premium on focusing analytics on innovation than lower performing enterprises.

• There is a high correlation between the use of analytics and top performing enterprises. The survey showed that top performing organizations were three times as likely to be sophisticated users of analytics.

• For analytics to be of impact, business culture must also be modified. The use of sophisticated analytics requires changes both in practices and the culture of decision making behavior. The most successful practices involve centralized development of data and analytics infrastructures and decentralized decision making, innovation, and experimentation, measured and tuned through analytics.

• There is a gap in the opportunities presented by analytics and the number of talented, analytics-driven managers needed to implement them. There is a serious talent gap, created by the peculiar requirements of the ideal analytics-driven managers: a combination of expertise in statistics, experiment design and interpretation and analytics with fundamental business knowledge and acumen. These analysts need the ability to ask the right questions and pose the right hypotheses. They need to know how to get data to tell them the things that matter (and not the things that don’t).

• Where in an organization should analytics be used? While IT is important to the support of analytics, the real development of analytics applications starts at the front-line point of need. From there, needs float upward to stimulate business units and centralized analytics applications.

• How are leaders in analytics using them in their businesses? Leaders and top performers are highly motivated to learn even more and push the envelope of application.

• Leaders are striving to improve data visualization in analytics to make information real to their users. Data visualizations and simulations are being used extensively to make representations of data more real and engaging. This is critical if the use of analytics is to be embedded in all processes and activities involving front-line workers and managers,

• An experimental approach to analytics is more useful than setting a plan in stone. Analytics need to be expeditionary. Respondents to the survey used the terms “test and learn” and “sense and respond” to describe the sense of experimentation needed for analytics to succeed.

• Analytics can be beneficial to any and all industries, not just tech savvy, digitally driven businesses. The survey showed conclusively that enterprises in all industries were using analytics in a competitive manner.

• The best is yet to come. Even as many executives believe their organizations are doing fairly well at integrating analytics into their businesses, experts see great potential for improvement in the sophistication of how businesses utilize analytics.

Implications for Analytics in Higher Education

Erik Brynjolfsson, director of the MIT Center for Digital Business, has made the following observation:

“What we’re going to see in the coming decade are companies whose whole culture is based on continuous improvement and experimentation — not just of specific processes, but of the entire way the company runs. I think this revolution can be fairly compared to the scientific revolution that happened centuries ago. Great revolutions in science have almost always been preceded by great revolutions in measurement.”

If Brynjolfsson is correct the future belongs to analytics-savvy organizations that can create new levels of performance and value. Higher education enterprises that are not able to achieve to this level of performance will lose ground to those that do meet the rising expectations of consumers who will encounter the new standards of performance in their dealing with enterprises from other sectors and in analytics-savvy organizations in higher education. New providers that provide lower-cost, good-value learning options will likely thrive.

True, some medallion institutions may be shielded by their aura of “quality” and the networking-for-life value that they provide to their graduates. But that number is relatively small. Most institutions will find that the performance-outcomes-and-value mantra that is driving all other sectors of the economy will also become pervasive in higher education. Community colleges, comprehensive universities, and for-profit higher education will all feel this pressure. They can all learn a great deal from” The New Intelligent Enterprise” which will continue to provide fresh insight.