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Analytics Short Discussion

May 16, 2014. Analytics Short Discussion. ECAR Analytics M aturity I ndex. 5 – Transforming 4 – Implementing 3 – Launching 2 – Visioning 1 – Starting. The Maturity Index is freely available for you to take to learn your own maturity levels.

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Analytics Short Discussion

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  1. May 16, 2014 Analytics Short Discussion

  2. ECAR Analytics Maturity Index 5 – Transforming 4 – Implementing 3 – Launching 2 – Visioning 1– Starting The Maturity Index is freely available for you to take to learn your own maturity levels. Source: ECAR Analytics Maturity Index, 2012. http://www.educause.edu/ecar/research-publications/ecar-analytics-maturity-index-higher-education

  3. BI Maturity – Challenges/Opportunities • Organizational Structure(s) • Cornell’s Office of Data Architecture and Analytics • Central IT, IR, other? • Data Governance – • Getting common set of data definitions/measures • Data access/sharing • Data Reporting/Tools • Managing BI Assets • Investment Levels • Expertise • BI Skill Acquisition • Culture • Coordination and cooperation across campus • Process • Working with customers

  4. Projections for analytics technologies • Technologies estimated to be deployed in approximately half or more of institutions by 2016/17: • Data warehouse • BI reporting dashboards • Learning analytics: degree advising • Administrative/business performance analytics • …and in about one in three or four institutions: • Learning analytics: course level • Big data • Predictive analytics Source: Higher Education’s Top-10 Strategic Technologies for 2014, Susan Grajek, ECAR, February 2014

  5. Appendix

  6. Analytics Maturity Index content Dimension 2: Governance/Infrastructure • Our information security policies and practices are sufficiently robust to safeguard uses of data for analytics. • We have policies that specify rights and privileges regarding access to institutional and individual data. • Our Institutional Review Board (IRB) has policies and practices for handling proposals involving analytics data collection procedures. • We have sufficient capacity to store, manage, and analyze increasingly large volumes of data. • Our data are “siloed”; we have pockets of individuals who protect their data. • We have IT professionals who know how to support analytics. Dimension 1: Data/Reporting/Tools • Our data are of the right quality/are clean. • We have the right kinds of data. • Our data are standardized to support comparisons across areas within the institution. • Our data are accessible to those who need it. • Our data are collected for a purpose. • Our data, reports, and processes are repeatable; we don’t have to reinvent the wheel to address questions and problems that come up regularly. • Reports are in the right format and show the right data to inform decisions. • We have a process for eliminating, phasing out, or updating reports that are no longer used or of value. • We have the right tools/software for analytics.

  7. Analytics Maturity Index content, continued Dimension 5: Culture • Our senior leaders are publicly committed to the use of analytics and data-driven decision-making. • Our administration largely accepts the use of analytics. • We have a culture that accepts the use of data to make decisions. • Our faculty largely accept the use of analytics for institutional decision-making. Dimension 6: Process • There is effective communication between our IT and IR departments. • Our senior-most institutional research leader is involved in the planning process for addressing high- level strategic initiatives or questions. • We have identified the key institutional outcomes we are trying to improve with better use of data. • Use of data is part of our strategic plan. • We have a process from moving from what the data say to making changes/decisions. • We have demonstrated with at least one high-profile “win” that analytics can lead to improved decision-making, planning, or outcomes. Dimension 3: Investment • Our funding level for analytics is sufficient to meet our current needs. • Funding for analytics is viewed as an investment, rather than an expense. • We have an appropriate number of data analysts. • We invest in analytics training. • Our analysts are too overwhelmed with routine reporting demands to use analytics to address strategic initiatives. (scored opposite)  Dimension 4: Expertise • We have a sufficient number of professionals who know how to support analytics . • We have dedicated professionals who have specialized analytics training . • We have business professionals who know how to apply analytics to their areas . • Our analysts know how to present processes and findings to stakeholders and to the broader institutional community in a way that is visually intuitive and understandable .

  8. BI Business Drivers – Weighted Scores

  9. BI Business Challenges – Weighted Scores Others Top Lack of BI tools; currently in an RFP process to acquire BI system Top Freeing up staff from other projects to work on BI Fourth Some data we need doesn’t exist in our systems Fifth Data access - silos don't want other silos to see their data

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