CSG Spring 2015 – The Data-Driven University – part 1

DKelly Doney – Changing the Conversation at Georgetown

Getting lots of questions around data not collected in traditional ERP – how many times did you visit your advisor? What volunteer opportunities did you do? Who was your favorite professor?

Advancement needs to follow alumni every step of the way.

Provost asking question – process efficiency, quality of instruction, but also outcomes – what happens to graduates in first five years and beyond, relating those data back to experiences on campus.

Vice Provost for education sponsoring an effort – wants to measure cultural impact of Georgetown on students: learning to learn, well-being, empathy, etc. Creating embedded cultural practices to track that.

Using Enterprise BI + CRM for data analysis

Trying go break down silos of data ownership. Workday enabled some of this as shadow system owners realized they weren’t getting feeds from the new system. Went live with Finance and Student data warehouse this year.

Been partnering with Advancement to bring enterprise CRM to campus. Need to think about other sources too. Just finished first part of playbook project with Deloitte and Salesforce to create a playbook for higher ed institutions that want to take a look at CRM at an enterprise level. Talked to 20 different offices, identified 150 use cases for CRM. Have a high level Salesforce object model. Going to take on a pilot.  Needs to be refined by the community.

Phase 1 – Advancement and Requirements. Phase 2: Advancement and CRM Core. Future phases: CRM and larger engagement.

Salesforce licensing model is cost prohibitive for higher education – they’ve agreed to come to the table to discuss this.

User community always asks for lots of control and flexibility in reporting, but doesn’t make time to learn tools.

Debbie Fulton – VA Tech – Role of BI tool at VT

It’s not how you get there… unless you can’t get there. The perfect BI tool is not the goal and will not create a data-driven university. But if you have no viable tool, your goals may be unattainable.

VT’s journey – Any tool will do (almost). Needed to figure out what mattered to VT. They had Brio since the early 2000s, had a lot of limitations. Licensing, required desktop installation, browser problems, etc. Had a lot of standardized reports that required developers to create. Put out a RFP.

Was important that sponsors realized that getting a tool did not create the data-driven university. Brought in EAB to make recommendations on creating the data-driven university which added credibility.

Goals: Replace soon-to-be obsolete technology; leverage data warehouse (didn’t want to rebuild); position VT for future (unstructured data, mobile access, diversity of data sources); Address issues with current environment (inconsistent distribution and management of information; report development cycle is lengthy and process varies; lack of modern presentation and analytical functionality; inadequate licensing of legacy tools and product obsolescence).

RFP Requirements: Pixel Perfect Enterprise Reporting (not just SQR reports); Ad hoc reporting; analytics, visualization, and predictive modeling; scheduling and distribution; dashboards; mobile implementation; common data model (virtual data model, supporting a common data model regardless of reporting tool used).

Two vendors supported the data model concept: Attivio (search based), and denodo (which actually builds a data model). Both add a layer complexity that would’ve added to the timeline, and expensive. MicroStrategy added ability to build model that other tools could look at. That layer isn’t as robust as the dedicated tools, but was good enough.

Purchased Microstrategy.

Benefits realized and next steps: Site license for Microstrategy including admin and academic usage; have a tool with full functionality to support BIT; opportunity to jumpstart BI dialogue – questions have changed beyond complaining about lack of good tools; BI sponsorship and steering committee; data governance – beyond data stewards; BI leadership and evangelism.

Questions for consideration in achieving a data-driven university: How do we progress with all aspects of a BI implementation (data governance, evangelism, anlytics, etc.) that need to come together? Where does IT fit? could we learn from the evolution of learning systems for how we might create data analytics services, partnerships, and direction between IT and the university?

Business Intelligence Pain Points – Todd HIll, Notre Dame

Finding and acquiring BI talent – can’t pay what industry does. Some places use staff who were gradate assistants. Some use offshore resources, but that presents some challenges. 1 excellent BI person is worth 3 mediocre ones – invest wisely. Build your own BI skills internally. Develop BI competency center.

Tools – Notre Dame historically used Business Objects, but now moving towards Microsoft stack + Tableau. Found that over half of what they built didn’t get used, so needed to change the model. Build Personal BI, Team BI, Enterprise BI. Find what works in a less costly way before moving up the maturity level. Can’t go right from zero to enterprise. 1 month personal BI solutions – 1-2 customers, non refreshing data. Then add data governance, build for the team, then after that build in security at an enterprise level.

Assessment Framework: How well do your customers know what they want? How clean is the data? How clearly defined are the data elements’ How well understood ae data access and security; How technically savvy are your customers?

Create a data steward position; involve constituencies, show a RACI matrix; publish data definitions – BI portal. Notre Dame has a data governance seal of approval for data that’s been defined by the process.

Addressing Organizational Silos – co-locate when possible to promote teaming; have cross-departmental user stories; use sponsors to clear organizational silos. Deans are asking for dashboards that cross those silos – e.g. research, finance, HR.

Sometimes you can take advantage of new ERP implementations to change the model of (for example) data access.

Addressing BI Project Demand – Agile methodologies can help. Partner with app development teams; partner with tech savvy customers; build BI competency center.

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