SEM XXII Orlando, FL November 5, 2012
ANALYTICS and ANALYTICAL COMPETITORS: SEM in the Age of Prediction and Optimization
Monique L. Snowden, Ph.D. Associate Provost for Academic & Enrollment Services
Fielding Graduate University
Session ID 1074
SEM XXII Orlando, FL November 5, 2012
WHAT TO EXPECT DURING THIS SESSION
SESSION GOALS
1. Present a high-level overview of analytics in the context of higher education, in general, and strategic enrollment management specifically
2. Explore what it means to “compete on analytics” and being a “analytical competitor”
KEY QUESTIONS
1. What are analytics and the relationship
between analytics, data, information, and business intelligence ?
2. What constitutes enacting an analytical decision-making process?
3. How can analytics can be used to gain/sustain a competitive advantage for your university, college, school, and /or program?
90 minutes 16 slides 1 activity at slides 14 – 15 (11:30 AM)
SEM XXII Orlando, FL November 5, 2012
THE ANALYTICS (R)EVOLUTION: Managing uncertainty, improving performance
Executive/Decision/Student Information Systems Enterprise Systems Data Warehouses OLAP (Online Analytical Processing) Data Mining Business Intelligence Key Performance Indicators (KPIs) Data Visualization – e.g., Dashboards, Scorecards, Predictive Modeling Performance Optimization
Data Information Knowledge Intelligence Analytics
SEM XXII Orlando, FL November 5, 2012
ANALYTICS: Connecting data |process| performance
In their book Competing on Analytics, Davenport and Harris, note:
Analytics are the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions.
Davenport, T. H., & Harris, J. G. (2007). Competing on analytics: The new science of winning. Boston, MA; Harvard Business School Press.
SEM XXII Orlando, FL November 5, 2012
ANALYTICS in CONTEXT: Focus on the questions and actions, not the tools
1. Analytics is the use of data, statistical and quantitative methods, and explanatory and predictive models to allow organizations and individuals to gain insights into and act on complex issues.
2. In colleges and universities, analytics is used to improve operational efficiency and student success.
3. The use of analytics to improve administrative functions is often called business intelligence; similarly, academic analytics is used to help run the business of the higher education institution.
4. More specifically, learning analytics focuses specifically on students and their learning behaviors (e.g., gathering data from course management and student information systems in order to improve student success).
5. Overall the term analytics refers to an approach that can be used to explore a broad range of questions. The emphasis should be on the question to be addressed rather than the tool.
Oblinger, D.G. (July/November 2012). Let’s talk analytics. Educause Review. van Barneveld, A., Arnold, K.E., & Campbell, J.P. (2012). Analytics in Higher Education: Establishing a Common Language. ELI Paper.
SEM XXII Orlando, FL November 5, 2012
SENIOR ANALYTICS DIRECTOR: Searching for Analytic Talent
Duties: • Identifies problem areas, such as admission patterns, fiscal and management analysis,
and sources of financial support in order to develop research procedures. • Provide critically important reporting, assessment, benchmarking, planning and public
information services to support and respond to institution needs. • Assists the institutions by providing reliable, relevant, and quality data and
information to facilitate planning, budgeting, marketing & outreach analyses, accountability, program evaluation, and development of policy decisions.
Qualifications: • Minimum of 8 years of work experience with at least 5 years in any of the following
areas: 1) Marketing or management consulting , 2) Database marketing, 3) Analytics, 4) Marketing analysis, or 5) Financial analysis
• MBA/advanced degree and previous business/IT consulting experience are both desirable
• Strong analytical skills with foundation in statistical techniques or similar skills in strategic business management
SEM XXII Orlando, FL November 5, 2012
THE NEW NORMAL: Prediction and optimization
Why listen to Matt Cutts’ Advice?: Matt is an engineer at Google, who specializes in search optimization. He's a friendly and public face for helping webmasters understand how Google's search actually works, making hundreds of videos that answer questions about SEO. He's an advocate for cutting down on poor practice such as link spam. He also wrote the first version of SafeSearch, Google’s family filter. http://www.ted.com/speakers/matt_cutts.html
SEM XXII Orlando, FL November 5, 2012
ANALYTIC EXPERTISE & SKILLS: Cooperation, Collaboration and Coordination
• Interpret results via content-specific lenses
• Determine appropriate actions and ideal timing
• Build sound predictive models
• Assess and recommend appropriate models
• Pull together data from multiple sources
• Ensure data quality
• Monitor, assess and recommend data
strategies
• Understand nuances of:
• Optimization
• Integration
• Interoperability
• Compatibility
• Portability Technology
Expertise
Data Expertise
Content Expertise
Statistical Expertise
ANALYSIS
SEM XXII Orlando, FL November 5, 2012
ANALYTIC ENVIRONMENT: Data is the core, not the foundation
Culture
Organization
People
Architecture
Data
• Evidence-based decisions
• Performance measurement
• Data/Information Governance
• BI/Analytics Governance
• Producers/Administrators
• Consumers/Analysts/Advisors
• Platforms
• Tools
SEM XXII Orlando, FL November 5, 2012
DATA IMPERATIVE: Big Decisions and Insufficient Data
Input: Multiple data sources, internal and external to your organization Processes: Data selection, collection, organization, and rationalization Output: Timely, reliable , relevant and sufficient data = Quality Data
SEM XXII Orlando, FL November 5, 2012
BIG DATA: Harnessing big data to transform decision making
We need leaders who can spot a great opportunity, understand how a market is developing, think creatively and propose novel offerings, articulate a compelling vision, persuade people to embrace it and work hard to realize it, and deal effectively with various constituents.
The term big data is often used interchangeably with analytics, but big data describes that uses massive amounts of data. For example, Walmart collects more than 2.5 petabytes of data every hour from its customer transactions. A petabyte is the equivalent of about 20 million filing cabinets’ worth of text. Rapid insights can yield competitive advantages
McAfee, A. and Erik , B (November, 2012). Big Data: The Management Revolution. Harvard Business Review
Big data neither subjugates or eradicates the need for vision or human insight.
SEM XXII Orlando, FL November 5, 2012
SUSTAINABLE SEM: The triple bottom line matters!
We’re a mission-driven organization, and in a mission-driven organization, no margin no mission. It’s very simple. You have to figure out how to generate a margin so you can deliver the mission. Education, research, and service are all activities that intrinsically lose money.
~ President Emeritus William Brody, Johns Hopkins
A juxtaposition of institutional and economic theory converges toward a central question: Can universities choose to be places of public purpose?
Zemsky and colleagues argue that the preservation and enactment of public purpose, framed as
mission-centered, is a function of an institution’s capacity to be market-smart.
Zemsky, R., Wegner, G. R., & Massy, W. F. (2005). Remaking the American university: Market-smart and mission-centered. New Brunswick, NJ: Rutgers University Press.
SEM XXII Orlando, FL November 5, 2012
SEM ANALYTIC PROJECTS: Relevant domains within analytics
Web Analytics - end-user visibility, organizational effectiveness, click-to-conversion rates, search engine optimization and marketing (SEO/SEM) Marketing Analytics – preference measurement, program design, positioning and brand equity assessment Pricing Analytics –price sensitivity, discounting on margins, effectiveness of pricing promotions, and program profitability/subsidy Text Analytics - word frequency distributions, pattern recognition, link and associational analysis and visualization, content analytics from unstructured data including blogs, news articles, on-line forums, video, audio Risk Analytics - causal modeling, optimization, portfolio analysis
Academic Analytics
SEM XXII Orlando, FL November 5, 2012
ACADEMIC ANALYTICS: Teaching, Learning and Student Success/Progress
REFINE IMPROVEMENT
Data Processes Actions
ACT ASSESSMENT
Prevention Intervention Consultation Affirmation
REPORT
Reports Dashboards Scorecards
PREDICT MODELS
Development Reliability Validity Frequency
CAPTURE DATA
Selection & Organization Policies Storage, Granularity &
Retention
Campbell, J.P., & and Oblinger, D.G.(2007). Academic Analytics. Educause.
SEM XXII Orlando, FL November 5, 2012
THE ANALYTICAL IMPERATIVE: Discerning and leveraging reliable and valid analytics
Opportunities: Impactful, authentic, and trustworthy interpretations (distinct and nuanced insights) Challenges: Appropriateness, frequency, expertise, risk aversion, acceptance
SEM XXII Orlando, FL November 5, 2012
SEM AT YOUR INSTITUTION?!?: EM Development and Analytics
Dolence, M. G. (1993). Strategic enrollment management: A primer for campus administrators. Washington, DC: American Association of Collegiate Registrars and Admissions Officers.
Hypothesis: The level of (strategic) enrollment management development signifies levels of institutional analytics enacted at a particular college or university.
Level 1, Nominal – EM perceived as a panacea for enrollment problems Level 2, Structural – primacy on organizational structure, efficiencies, & effectiveness Level 3, Tactical – the organization sees itself as a component of a larger system, and therefore collaboration and environmental scanning are promoted and performed; EM evolves from a structural concept to a comprehensive process Level 4, Strategic – EM is situated within the academic context, and thus is entrenched in the institutional and strategic planning of the academic enterprise
SEM XXII Orlando, FL November 5, 2012
ANALYTICAL COMPETITORS: Employing descriptive and predictive analytics
Level Focus Inquiry
Pre
dic
tive
8 Optimization What’s the best that can happen?
7 Predictive Modeling What will happen next?
6 Forecasting/Extrapolation What if these trends continue?
5 Statistical Analysis What actions are needed?
De
scri
pti
ve 4 Alerts What exactly is the problem?
3 Query/Drill Down Why is this happening?
2 Ad Hoc Reports How many, who, how often, where?
1 Standard Reports What happened?
Adapted from Davenport & Harris (2007)
SEM XXII Orlando, FL November 5, 2012
THE PERFORMANCE IMPERATIVE: Analytics stimulate action & impact performance
Opportunities: Forecasts, predictions, optimizations and actions Challenges: Institutional culture, capacity, and capability - people, programs, products, policies, processes, and practices
SEM XXII Orlando, FL November 5, 2012
Analytics and Analytical Competitors: SEM in the Age of Prediction and Optimization
Monique L. Snowden, Ph.D. Associate Provost for Academic & Enrollment Services
Fielding Graduate University 805.989.4154
Questions & Comments
Thank you for your attendance and kind attention!
Session ID 1074
