ANALYTICS and ANALYTICAL COMPETITORS: SEM in the Age of Prediction and Optimization

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1 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

2 WHAT TO EXPECT DURING THIS SESSION SESSION GOALS 90 minutes 16 slides 1 activity at slides (11:30 AM) 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?

3 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

4 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 factbased 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.

5 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.

6 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

7 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.

8 ANALYTIC EXPERTISE & SKILLS: Cooperation, Collaboration and Coordination Understand nuances of: Optimization Integration Interoperability Compatibility Portability Technology Expertise Data Expertise Pull together data from multiple sources Ensure data quality Monitor, assess and recommend data strategies ANALYSIS Statistical Expertise Content Expertise Build sound predictive models Assess and recommend appropriate models Interpret results via content-specific lenses Determine appropriate actions and ideal timing

9 ANALYTIC ENVIRONMENT: Data is the core, not the foundation Culture Organization People Architecture Evidence-based decisions Performance measurement Data/Information Governance BI/Analytics Governance Producers/Administrators Consumers/Analysts/Advisors Platforms Tools Data

10 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

11 BIG DATA: Harnessing big data to transform decision making 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 Big data neither subjugates or eradicates the need for vision or human insight. 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. McAfee, A. and Erik, B (November, 2012). Big Data: The Management Revolution. Harvard Business Review

12 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 missioncentered. New Brunswick, NJ: Rutgers University Press.

13 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

14 ACADEMIC ANALYTICS: Teaching, Learning and Student Success/Progress Selection & Organization CAPTURE DATA Policies Storage, Granularity & Retention PREDICT MODELS Development Reliability Validity Frequency REPORT Reports Dashboards Scorecards ACT ASSESSMENT Prevention Intervention Consultation Affirmation REFINE IMPROVEMENT Data Processes Actions Campbell, J.P., & and Oblinger, D.G.(2007). Academic Analytics. Educause.

15 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

16 SEM AT YOUR INSTITUTION?!?: EM Development and Analytics 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 Dolence, M. G. (1993). Strategic enrollment management: A primer for campus administrators. Washington, DC: American Association of Collegiate Registrars and Admissions Officers.

17 Descriptive Predictive ANALYTICAL COMPETITORS: Employing descriptive and predictive analytics Level Focus Inquiry 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? 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)

18 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

19 Thank you for your attendance and kind attention! Questions & Comments Session ID 1074 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