2015 Session Descriptions

Actionable Intelligence: Big Data for Student Success (Barrie Fitzgerald, Valdosta State University)

In 2009, Complete College America began working towards assisting states with increasing the number of conferred college degrees in the nation; additionally, the State of Georgia in 2012 adopted its own version to help increase the number of degrees conferred. In order to accomplish this at Valdosta State University (VSU), it needed to examine its techniques in helping students succeed. VSU began researching how the public and private sectors use analytics to help students succeed. After the analysis, VSU applied similar techniques to increase the efficiency, reduce attrition, and increase retention and graduation rates. In Fall 2012, an actionable intelligence approach was implemented providing a two-way communication system between faculty, advisors, and academic support centers. Throughout the academic year, students’ needs, strengths, and weaknesses were communicated between faculty and academic support staff to target resources to help student succeed academically. To assist faculty and support staff in determining students’ academic strengths and weaknesses, predictive models for retention and pass rates were developed to identify factors that may affect students’ likelihood of retaining at the university and succeeding in courses. By combining predictive analytics, enterprise data warehousing, and custom applications, our institution was able to provide needed support to students within the first few days of the semester rather than closer to the end. VSU has seen statistically significant improvements in student pass rates and retention among faculty users. Additionally, VSU developed a student portal to display academic resources to aid in students’ success. If a student was having challenges in a course, actionable triggers and alerts display ads for academic support resources in the portal. Providing instant information required a change in business practices and procedures to define action steps when students were identified as at-risk. Two years later, VSU has experienced almost a 4.0% increase in retention and between a 3.5% to 5.0% increase in pass rates of first-time, full-time freshmen. By using actionable intelligence via a two-way communication system and an academic success ad-driven portal, VSU is taking steps in meeting the completion agenda. This session will provide an overview of changes at Valdosta State University and demonstration of the technologies used to facilitate the proactive and predictive engagement of student support before it is too late.

Ask a Guru

An opportunity to ask a panel of experienced HEDW members for advice, insight and BI/DW predictions.

ASU Analytics SharePoint UI Customization (VenkateshMandalapa, Arizona State University)

ASU has many third party tools and applications that provide services to our students, staff and faculty – all 100,000 of them. We strive to provide a seamless and rich user experience among all these tools by implementing our ASU standards and design philosophy where possible. While SharePoint 2013 out of the box looks far better than the previous SharePoint versions, it still leaves much to be desired when it comes to integrating with your own institution. Thankfully SharePoint 2013 provides an easy way to create custom themes. This presentation is about how we branded our SharePoint environment to meet ASU design standards and principles to provide a consistent user experience for ASU Business Intelligence users. We will talk about the technologies behind it, tools used, processes involved with packaging and reusing solutions, and the skills necessary to make this work in your institution.

Automated Business Intelligence Reports Migration OBIEE (Tim Flood, Dynasoft Synergy)

You’ve invested years developing reports in your legacy reporting system. But getting from your legacy reporting system to your new Business Intelligence system (OBIEE 11g) is no easy feat. Dynasoft’s automated report migration service saves higher education institutions the time, cost, and worry of getting to OBIEE 11g. Learn how customers like Stanford used our service to great advantage.

Automating Data Governance and Stewardship in Autonomous and Decentralized University Environments (Pieter De Leenheer, Collibra)

Data Governance and Stewardship requires automation of business semantics management at its nucleus, in order to achieve data trust between business and IT communities in the organization. University divisions operate highly autonomously and decentralized, and are often geographically distributed. Hence, they benefit more from an collaborative and agile approach to Data Governance and Stewardship approach that adapts to its nature.
In this lecture, we start by reviewing ‘C’ in ICT and reflect on the dilemma: what is the most important quality of data being shared: truth or trust? We review the wide spectrum of business semantics. We visit the different phases of growing data pain as an organization expands, and we map each phase on this spectrum of semantics.
Next, we introduce our principles and framework for business semantics management to support Data Governance and Stewardship focusing on the structural (what), processual (how) and organizational (who) components. We illustrate with use cases from Stanford University, George Washington University and Public Science and Innovation Administrations.

The Benefits & Challenges of Cloud-Based Analytics for Higher Education (Ron Cruz, KPI Partners)

Meeting the demands of today’s modern students, faculty, and staff is becoming more challenging as the consumer thirst for instant gratification triggers the advent of innovative new technology.   Delivering social and collaborative experiences, identifying top talent, and increasing productivity across campus through cloud-based technology is now a cost-effective reality.
Join Ron Cruz, Cloud Analytics Guru at KPI Partners, as he takes us on a journey that explores the top benefits & challenges of cloud-based analytics and reporting for Higher Education.

A Better Data Culture: How to have a Clear Path from Question to Answer (Scott Flory, IData)

Successful institutional reporting is based on good reporting processes and data governance. In this presentation, we will share our perspective on the best practices of data management. We will discuss the iterative lifecycle of data requests from questions to answers and we will also introduce the Data Cookbook, the data management tool for higher education. The Data Cookbook provides workflows to manage the process of reporting and to govern the knowledge that is shared through your reports.

BI+IR: Shotgun Wedding, or Marriage Made in Heaven? (Hank Childers, University of Arizona)

The University of Arizona recently decided to combine its BI and IR teams into a single group.  This seems both an attractive and improbable proposition.  What were they thinking?  What are others thinking?  How is it working out?    Part 1 of this presentation will discuss the UA’s situation, starting with the motives for combining the two groups.  It will cover what the organization looked like before, what it looks like now, what issues have been encountered, and what the future looks like.    Part 2 will explore the underlying issues based on conversations with a number of other institutions.  In most institutions BI and IR do not see eye to eye, with atmospheres ranging from fairly peaceful coexistence to outright conflict.  But why is that?  The hypothesis is that there are two basic causes:  1. Structural issues, i.e., organization, reporting lines, responsibilities   2. Cultural issues, i.e., different backgrounds, different values, different career paths  This part of the presentation will cover what’s been learned in these different conversations with other institutions, to see what patterns there might be.    Part 3 will discuss what we might do about it, presuming that we continue to see value in bringing the BI and IR functions much closer to each other.  How do we turn what might be a shotgun wedding into a state of wedded bliss?  Or at least a pleasant discussion around the breakfast table.  With a nice cup of coffee.

Big Ways Universities can Leverage Microsoft Analytics to Gain Insights (Patrick LeBlanc and Jason Morales, Microsoft)

Join us for a walkthrough of how real universities could take their data from multiple data sources to Power BI to drive decisions throughout all areas of their institutions. This demonstration incorporates dashboards and reports as a primary communication and decision making medium, not only for decision-makers business analysts, but also as a vertical integration with students, professors, deans, chancellors and even customers! We will explore, in depth, each usage scenario and learn tips and tricks to maximize the benefit of expedient insights.

Birds of a Feather

Network with conference attendees on topics that interest you.

Building a second generation BI community at Purdue (Aaron Walz, Purdue University)

What if you had a chance to start your BI implementation over, knowing what you know now? Purdue University implemented a successful custom Warehouse in its mainframe days, followed by mixed results with off-the-shelf Warehouse products in the years following two ERP implementations. We’re now in the process of building a “second generation” BI community, taking advantage of the lessons of those past implementations, as well as the experience of a new Director who spent 12 years at another university helping build the BI function there. (Hint: approaching BI as a community is one of those lessons.)     Knowing what we know now, how are we doing it over again? This presentation will cover successful practices as well as pitfalls to avoid, with examples. In addition, we’ll discuss the impact of the increasing push for analytics and how we’re evolving to meet these new demands.

Building a Tableau Training Program (Deenie Esquibel, University of Washington)

The University of Washington’s Enterprise Data & Analytics (EDA) team supports the Enterprise Data Warehouse (EDW). Tableau Desktop is the data visualization platform chosen for use with EDW data, following a multi-month pilot in partnership with 20 academic and administrative units. While Tableau provides on-demand, live online and classroom training, the decision was made to create an internal training program to deliver introductory classes to new and existing Tableau users.    Building and deploying a training program for any organization is exciting and challenging. The instructional designer (ID) needs to incorporate the ADDIE process (Analyze, Design, Develop, Implement, and Evaluate). These five stages of the ADDIE model encompass the entire training development process from the time someone first asks, “What do people need to learn?” all the way to the point where we measure, “Did people learn what they needed?” The ADDIE model describes an ideal-world methodology and assumes the ID will have plenty of time to create a great training program. This is not always the case, particularly in a unit that employs AGILE methodologies for managing projects.    In this presentation, I will describe the process used to create and deliver a training program for Tableau Desktop. I will describe how the training team worked with functional experts and conducted needs assessments to determine course objectives for two levels of Tableau Desktop training. The results of post-class evaluations, as well as feedback the EDA team received about training will be shared. The employment of adult learning principles and a new process and intake flow when creating new training programs will also be covered.

Business Intelligence as a catalyst for cultural change (Dale Amburgey, Embry-Riddle Aeronautical University)

Embry-Riddle Aeronautical University is an institution comprised of two residential campuses and numerous teaching locations around the world.  Two challenges facing University leadership are to create an environment where these numerous entities speak a common data language and to provide a consistent reporting platform.  Information Technology and Institutional Research joined together to provide the vision and resources needed to undertake this challenge.  Utilizing tools such as the Data Cookbook and OBIEE, the framework has been created to move the University towards a more efficient and effective data culture.  This presentation will focus on the lessons learned as we discuss our journey of cultural change.

Cognos Reports without Cognos Connection: A Pain-Free Experience for Data Consumers (Eric Blazek, The University of Oklahoma)

Cognos is a great software tool; but it is a developer’s tool. This presentation will discuss a method of building a user-friendly, web front-end for data consumers so that Cognos reports can be made available to non-developers in a pain-free environment, utilizing the Cognos parameterized URL interface and everyday web development tools.

Connecting Silos: How HR Analytics Helps Win Business & Remove Barriers in Local Research Administration (Joe Frank, Washington University in Saint Louis, School of Medicine)

Over the past ten years, the funding environment for medical research in the U.S. has become increasingly competitive. By leveraging existing datasets and unique analysis capabilities in the Human Resources office, a small team of senior leaders, teamed with an HR data analyst, set out to identify and advocate for organizational change. The question is: How do you build high-performance proposal development teams in a rapidly changing environment? While this work may not win a Nobel Prize nor cure Alzheimer’s Disease, it will help scientists bring in more dollars towards life-saving research.

Cornell’s Journey to the Cloud (Jeff Christen, Cornell University)

With the ever increasing availability of cloud applications, services, and hosting options, it is important for institutions to understand the impacts of moving to “the cloud”.  In the DW/BI area, there are challenges to having globally disparate data sources, as well as benefits to leveraging cloud services for DW/BI. It is important to understand the benefits, as well as the challenges, of cloud and other hosting options.  This presentation focuses on Cornell’s journey to the cloud.  What applications and services have been moved to date, as well as our future plans in this area, and lessons learned.

Cost of Academic Programs (Jahnavi Jilledumudi, SUNY Albany)

In 2012, the then UAlbany Provost set the highest priority for the first BI project: tie expenditures to course credits and tell me how much we’re spending on academics.  When we initially contacted the HEDW forum for ideas on a Cost of Instruction Model, we were advised that it will present a lot of challenges and would be very complex to venture into it. We also researched other institutions and received similar responses. But, since this was THE priority for the Provost, we had no choice but dive in. With a fabricated spreadsheet as a prototype, and using the Delaware model as a basis, IT, Budget, and IR worked hand in hand to develop, test, retest, recode, and test again the Cost for Instruction Model.  This model is in use today and provides very interesting metrics:  Cost per credit, By Instructors, Programs, Department, Schools, Titles, Student Credits, Seat Counts and Student FTE by Instructors, Programs, Department, Schools, Faculty Titles and Title Types. The model also feeds our Course Planning Dashboard (Average Class sizes, Number of Courses) and our Workforce Dashboard (Number of Instructors, Position FTE, and Instructor FTE).  Our Model gained a lot of publicity within the SUNY system. We provided presentations to our sister colleges and to SUNY Administration that included the dashboards, and most importantly, the Data Warehouse structure, sources, and logic required to create these metrics. They were very excited to understand HOW these precious metrics were derived and used.  And equally excited that we are about to quantify Research Activities of faculty. Any institution who has asked this question can benefit from our model, methods & lessons learned. This presentation will convince them not to shy away from trying to tie these metrics together for their own use.

Creating a BI program at a public university and sharing the intellectual property with another university in our State System (Jeffrey Montgomery, Indiana University of Pennsylvania)

Indiana University of Pennsylvania (IUP) is a member of Pennsylvania’s State System of Higher Education.   We will present on our BI Program which started in 2005 and has resulted in solutions and best practices being share with another State System university.  The presentation will cover IT staff development and alignment, engagement and outcomes with a consistently changing user community who challenges are increasing, our data warehouse solutions and reporting solutions and opportunities, sharing our intellectual property with another State System university, and lessons learned from each experience along with our plans and goals for the future.

Data Governance Excellence: Ensuring Standard Definitions and Sustained Data Quality – Part 1 (Laura Whitaker, EAB)
Data Governance Best Practices from the 2014 research study on the Data-Driven Enterprise from Education Advisory Board (EAB) IT Forum – Part 2 (Kevin Danchisko, EAB)

When EAB’s IT Forum asked its member CIOs what their most pressing challenges were for 2014-2015, “building an analytics capability” topped the list. CIOs were getting requests for data from all over campus and beyond – academic leaders requesting help to make program prioritization decisions, deans trying to increase enrollment with new RCM budget models, boards of trustees wanting to assess progress on institutional goals, etc. The task to become a “data-driven university” often fell to IT, and CIOs needed help getting there. When seeking the root cause problems preventing this transformation, EAB found it wasn’t a lack of technology or data, but rather minimal data governance and poor data quality that holds institutions back. EAB’s research identifies best practices for setting up data governance for sustainability and improving data quality in source systems. This session will include a presentation highlighting best practices in higher education followed by an extended Q&A session for attendees to ask questions of EAB researchers.

Data Governance – Lessons Learned During Deployment (Augie Freda, University of Notre Dame)

This presentation will illustrate experiences and lessons learned upon moving from initial data governance efforts to deployment of governed data in a data driven decision making environment using business intelligence tools.  The presentation will cover deployment of metadata, revision needs and security/stewardship attributes of moving from team-based BI environments to enterprise-wide deployment.

Designing a Federated Business Intelligence Development Strategy (Michael Wonderlich, University of Illinois)

Your centralized Business Intelligence team has created some very successful BI products for your university and your constituents are using your tools to create great BI products to share with their colleagues.  Now they want to incorporate their localized BI content into your centralized BI delivery.  Finding a way to empower the divisional BI developers to publish their BI products with as few obstacles as possible, yet maintaining the integrity and security of your enterprise BI platform can be a challenge.  This presentation will explore the possibilities of partnering the divisional and central BI teams with the goal of creating a highly successful, federated BI strategy by creating a partnerships with defined roles and objectives.

Through the use of Relationship Managers a partnership is established between the central BI team and the divisional BI teams.  These groups work to support each other and eliminate redundant work between divisional and central BI development.  This organization supports a strong central infrastructure while meeting the BI needs of the university at all levels.  It places the BI development as close as possible to the audience for which it is intended to benefit.  The result is a BI culture that permeates the university leading to better information sharing and better decisions.

Developing Data Definitions that ADVANCE Effectiveness (Stacey Randall, Waubonsee Community College)

During the last two years, Waubonsee has embarked on a data stewardship project that focused on that facilitated systematic planning including the centralization of data requests; the evaluation of current systems used to access and analyze data; and the development of a system of data governance, definition and security procedures. This session will explain the implementation of this model and lessons learned in the process.

Feeding Scholarship Repositories in Quasi-Real Time (Mike Rohlinger and Matt Moericke, Academic Analytics)

The Academic Analytics database presents metrics on the primary areas of scholarly accomplishment in Ph.D. education; journal publication, conference proceeding publication, citations, book publication, federal grants and professional honors and awards. Join this session to learn more about how Academic analytics is partnering with universities to deliver the data in a range of formats, including: MS SqlServer, Oracle, My SQL. The data can be ingested into open source Faculty record systems such as VIVO, RNS Profiles, an existing on-campus dashboard or used to populate an institutional repository. Further, data on individual faculty research activity can be ported from the Academic Analytics database to ORCID through an ORCID API.

Herding Effective-dated Cats: Challenges for a Student-Term Data Model in a Decentralized Business Environment (Jonathan Havey, SUNY Buffalo)

The University at Buffalo (UB) implemented PeopleSoft Campus Solutions in 2011. As part of this effort, UB also implemented iStrategy’s HigherEd Analytics (now BlackBoard Analytics) in a Data Access and Reporting initiative that went live at the same time.  The first phase of the Data Access and Reporting initiative was intended to provide data to reporting super users and to create a suite of basic, web-accessible operational reports for use by departmental administrators. To this end, the pre-existing data-mart (branded “Info Source”) was initially populated using BlackBoard Analytics data models, and a pre-existing OBIEE instance containing human resource and financial reports was leveraged to deliver operational reports in the student realm. The rollout, following an extremely aggressive schedule for implementing Campus Solutions (which we branded as “HUB”), was successful in the sense that there was no interruption of service in providing data to the university community. Departmental administrators were able to run basic reports on majors, classes, enrollment, student groups, and service indicators (i.e., holds), and reporting super users were able to start rebuilding their local reporting systems. As time went on, however, certain gaps emerged that were difficult to reconcile with the data in the HUB. All of these challenges originated in the attempt to generate a student-term data model using effective-dated data that lacked term dimensionality. This presentation will discuss how the University at Buffalo responded to these gaps through the following: Customizing views, constructing supplemental views to fill the gaps, reviewing business process in assigning effective dates, and updating the OBIEE .rpd file to capture some of the lost data.

The Holy Trinity of Higher Education: Point-In-Time, Price Versus Press on Graduation Rates and Seat Melt! (Christina Rouse, Incisive Analytics)

In 60 minutes come hear about three new ideas in measuring outcomes and efficiency in higher education.  Your deans will be glad you did!  Point-In-Time (PIT) highlights a 95-point (hey, it worked for Martin Luther in 1517) in time analysis for enrollment with more slicing and dicing than a Ronco Veg-O-Matic.  Price Versus Press on Graduation Rates takes a very different look at what it means to finish in four years and how much money was spent on a degree.  Lastly, Seat Melt is killing efficiency.  We’ll take a look at calculating seat melt over time and across all type of classes and student profiles.  Is the section losing seats to drops or withdraws and when and what is the fill rate?

How to Build a Data Warehouse (Lucas Jones, Weber State University)

Weber State University has just completed their data warehouse project, more than fifteen years in the making. This presentation will cover what worked, what didn’t, and what ultimately led to their success.

If you build it, they will come – Creating web sites your users will love (Pieter Visser, University of Washington)

Do you dream of building Web sites for your B.I. products that people can’t stop oohing and ahhing about? Then this is the session you’ve been looking for.  In this demo heavy session I will show different ways that you can link to reports and visualizations to make your imaginations run wild. I will be using Tableau, JavaScript, and other tools to show what’s possible, but knowledge of HTML or JavaScript will be not be required.  Will then show how we used these techniques to create the portals that our end users love (BI Portal and new and improved UW Profiles Portal). You will also hear about ideas to achieve end-user Nirvana with the Ultimate Collaborative, Self Service B.I.Portal (that does not exist yet).

Implementation of a Position Based Data Access Security Model at OSU (Michael Hansen, Oregon State University)

When OSU deployed the OSU CORE BI solution the traditional security administration models did not scale when faced with administering user access to over 6,500 employees.   Unable to fund additional FTE to administer user access OSU was forced to look outside the box for a better security administration solution.  The result was that OSU developed an automated “Position/Classification Based Data Access” model which leveraged job/position classifications to assign security access.  This model automates user access rights based on the “Principal of Least Privilege” according to data classification level and employee position/classification.

Increase User Adoption: Consolidated Data Marts and Watson Promise (Ashley Silverburg, Phytorion)

Despite the use of dimensional data models and BI tools that aim to be intuitive, BI adoption remains low.  Reporting across multiple data marts is hard even with conformed dimensions.  And on the front end, analytics continue to suffer from a balkanization of capabilities that forces users to employ multiple tools to get their answers.
In this presentation, we will first review Consolidated Data Marts, which bring together data from multiple ERP modules into single stars, in order to analyze Faculty Load, Discount Rates, Net Tuition Revenue, and Retention and Graduation. We will then demonstrate Watson Analytics, a new IBM cloud-based tool that uses a single interface for data discovery, visualization, refinement, and prediction and which, over time, learns from the user.

Intentional Retention Analytics (Martha Taimuty, The New School)

When The New School was designing a warehouse to support enrollment management analyses they decided to build an entire room devoted to retention analytics.  Join us to learn how having a well-crafted, flexible foundation has resulted in on-demand retention and graduation analytics, sophisticated enrollment projection modeling, and more.

Introducing Visualization to a Static Reporting Culture (Jeffrey Meteyer, University of Rochester)

The University of Rochester has been using a single BI tool ( IBM Cognos) for over 7 seven years to support the reporting requirements of the data warehouse, reaching over 7 data marts. The report development cycle and the static appearance of data has reached a point where the audience now wants to “see” the data and its surrounding effects in order to foster better decision making. What started out as proof of concept and has eventually morphed to production readiness is repurposing the existing data sources and enabling visualization tools to be introduced. The goal of dashboards to help explain what trends are being seen in short and long term periods is being realized. the back end changes needed to complete this transformation include the re-purposing of data elements and development of views that lend to easier development of visualizations. I will discuss how we have leveraged the existing Cognos data models and how the Tableau visualizations are being used to support new reporting requests and how Institutional Research is taking advantage of a rapid development environment, while still benefiting from existing reporting structures.

Making dashboards with static data: Designing UC Berkeley’s suite of curriculum dashboards (Jenna Allen, University of California, Berkeley)

It can be challenging to develop dashboards with data that only gets updated once a semester or once in an academic year. At UC Berkeley we have tried a number of approaches to getting the data in Cal Answers, the campus enterprise data warehouse, out to the campus and into the hands of decision makers. In this presentation, you will see examples of the curriculum dashboard reports that we have rolled out to campus over the last 2 years. These reports include general access dashboards featuring aggregated curriculum data with our most current release designed for use by Department Chairs to support curriculum management and planning.

Making Governance Work: Lessons Learned at UC Berkeley (Max Michel, U.C. Berkeley)

This presentation reviews UC Berkeley’s Governance model, and how it has served as a powerful mechanism for managing priorities and delivering value for data consumers across the UC Berkeley campus.  Topics will include:  –    The role of executive sponsorship:  creating a shared Governance vision across business and technical teams, making adjustments to the Governance model as DW/BI programs grow and mature, balancing competing data requests from different groups and departments, defining Roles, teams, processes and technologies to manage data security, data quality, and business and technical metadata.

Member Program Showcase (1 & 2)

HEDW members describe the operations and services at their institution.  This program offers convenient comparisons of a data warehouse, business intelligence, or institutional research operation at several institutions.  There are 2 Showcase programs. The format is the same for each program and the slate of institutions is different.

Miami University 6 Year Retrospective:  What we wish we had known at the beginning (Mary Brooks, Miami University)

Miami University initiated a BI program 6 years ago. The program has evolved into a more agile, value driven, iterative, and collaborative delivery.  The Miami U director of IR, director of BI and BI project manager will share our experiences (and sometime conflicting perspectives)  by discussing what we wished we knew early on and how we addressed our hardest issues.  Topics will span from staffing issues to strong client representation to going agile.

Moving Beyond Business Intelligence: Using Blackboard Analytics’ Student Management Suite as the Foundation for Machine Learning Models to Understand and Predict Student Retention (Sara Collins, Illinois Central College)

In the modern higher education environment, a great deal of data is generated that allows institutions to analyze and understand several facets of their students’ academic experiences. The structure provided in Blackboard Analytics’ Student Management Suite builds a solid foundation for schools to not only report and present that data through Business Intelligence and dashboards, but also use advanced analytical techniques from the emerging field of Data Science to build powerful machine learning models. These models can help institutions analyze how to optimally serve their students, determine which factors are most important in helping students learn and achieve success, and forecast retention and other critical success measures. Illinois Central College is currently using their Blackboard Analytics data warehouse data as the basis for machine learning models in Python to predict and better understand student retention, and use information from those models to guide decision-making in working toward their strategic boldly important goal of becoming nationally recognized for student success by 2020.

Moving UMUC Data Warehouse to the Cloud: A Case Study (Murthy Ravikanti, University of Maryland University College)

Cloud computing has proven to be a disruptive technology in many industries, but will it work for higher education and can it support data warehouse and business intelligence platforms?  UMUC has taken the plunge with a fully   cloud based-solution and completed the migration in less than six months.    Learn about the decision criteria, migration process, and overall results that have transformed   our tools and how do business.

A New Business Analytics Definition: Performance Architects Clarifies BI & Data Discovery, Storage, and Integration Confusion (Kirby Lunger and John McGale, Performance Architects)

Ever feel like a hamster on a treadmill, trying to keep up with new business analytics terms?  Trying to understand data storage technologies (Hadoop, NoSQL); what data discovery actually is beyond BI; or what ELT is?  Frustrated with trying to figure out how this fits into your organization’s business analytics roadmap?  Learn our perspective on what constitutes a “best practice” business analytics architecture in Higher Ed today, and what other institutions are doing to address these challenges and opportunities.

One Ring to Rule Them All – Managing Security in a Complex Business Intelligence Realm (William DeMoville, The University of Texas at Austin)

In a large, complex enterprise with various authorization authorities, plethora of databases, and myriad business intelligence tools (Cognos and Tableau) laid on top of those systems, managing user security can be a time-consuming, hair-pulling experience.  The University of Texas at Austin has begun work on a system aimed at simplifying the administrator experience, increasing administrator productivity, and ensuring users have a consistent and pleasant security experience across all platforms currently in use.  Join us to learn about how we modernized our lightweight directory access protocol (LDAP), connected our Business Intelligence tools and databases to it, and created a web-based service on top of it all to provide visibility and user-friendly management.

Project Portfolio Management and Team Capacity (Troy Hogan, University of Washington)

Balancing project portfolio demands with your team’s capacity is a priority in every organization.  This presentation will provide an overview of processes and tools used on the Enterprise Data and Analytics team at the University of Washington, along with highlighting the benefits and lessons learned.

The Right Brain University Administrator – Financial Analytics for Insight and Action in Higher Education (RK Paleru, The George Washington University)

Since the dawn of this millennium, we have seen a confluence of strategic headwinds impacting higher education. As a result, CFOs and other University Administrators are being asked step out of their comfort zone to help enhance revenue, contain or reduce costs, mitigate business risks, and sustain cash flows. GW invested in a dedicated organization – Systems Analytics & Insights Group (SAIG), which developed Financial Analytics solutions to help administrative and academic power users / decision makers at GW make business decisions with error free analytical information / insight. These automated analytical solutions, ready within hours of each monthly financial close (versus weeks in the past), help optimize a variety of business decisions at GW. In this session, university administrators, systems analysts, data scientists  and IT analysts will be able to learn about Financial Analytics solutions being leveraged at GW, using tools such as Tableau / Cognos, and also take away a template for effective self service using Business Intelligence.

Self-service dashboards for the business user community (Jon Salmon, iDashboards)

Institutions have struggled with reporting and data discovery tools meant for data analysts. This presentation demonstrates iDashboards’ unique capability on creating dashboards for the non-It/non-analyst crowd from databases, spreadsheets or other sources.

Secrets behind building a world class higher-ed Business Intelligence (Business Insight) program (Rainbow Di Benedetto, The University of Texas at Austin)

This presentation will share key factors (tips and techniques) that were effectively handled in deploying a successful enterprise Business Intelligence (BI) solution at UT Austin. We will also review how our BI initiative called Information Quest (IQ) laid a trusted foundation which is helping us now to soar into the next generation analytics platform including big-data.  Our organization is highly decentralized and this presentation discusses how to shorten time-to-market to exceed customer analytical needs and to delight them. Selling a project in current technology trend is the easy part but making it part of the daily routine is harder. This presentation will highlight what we learned, the framework, and the approach we took at the University of Texas (UT) and critical success factors. This presentation will also discuss both the tangible and intangible Return-On-Investment (ROI) gained through BI innovation and BI Center of Excellence.

Signs of a Successful Business Intelligence (Tableau) Deployment (Darin Mattke, The University of Texas at Austin)

What do you do once you have an established Business Intelligence (BI) environment?  You roll out another one of course!  At The University of Texas at Austin, we are in the process of implementing another toolset for customers to consumer their data: Tableau.  Attempting to duplicate the success of our first rollout of BI tools (Cognos), we quickly realized that Tableau is not “your granddad’s BI software”!  A number of issues were the same as our first deployment, but now we encountered a number of new, unforeseen topics to be addressed.  As they say in the movies, “this time, it’s personal!” Because we have established governance, trust, and experience with BI tools themselves, we were able to put that knowledge to use to assist in implementing performance management, capacity planning, and security strategy for all of our BI systems.  In this presentation, we will discuss what we found different from our initial deployment, demonstrate our monitoring and security solution, talk about pain points and what should be avoided, best practices and solicit interactive discussions about how to handle security, Tableau projects, data sources, workbooks, and visuals.  Personal mental health therapy and hair replacement strategies will not be covered.

6 Things to Consider When Building an Institutional Data Warehouse Foundation (Todd Nash and Jerry Edwards, CBIG Consulting)

CBIG consulting’s Todd Nash and Jerry Edwards will discuss best-practices for building an institutional data warehouse foundation including 6 key focus areas and lessons learned from successful use cases. If you’re building or enhancing a data warehouse / data intelligence platform for your institution’s reporting and analytics needs, this session lays the groundwork to support your goals.

Starting a Business Intelligence Initiative on a Budget Using Microsoft (Miriam Clark, Kansas State University)

This is the story of a startup. It’s a story about change and acceptance. Like many universities Kansas State is facing the challenges of budget reduction with higher expectations of supporting our students and increasing retention and graduation rates. In order to meet the challenges facing us, we need to move to data driven decision making. Our current culture is based on reporting from transactional systems where much of the information is difficult to access. We need increased transparency into our business and academic data across multiple subject areas and faster more agile reporting in order to be a more effective organization. We need analytical tools to enable data driven decision making.    This presentation will discuss our implementation of Microsoft as our business intelligence toolset and our roadmap to implementation. We built a data warehouse for Pre-Awards Research and plan to use SharePoint as our presentation environment. We will describe our history, the challenges we had to overcome and are still currently overcoming, what we built and how, how many people use it, how much data is in it, and our training and communication plans. Also covered will be our short and long term plans, timelines, use cases and the order that we plan to implement the various subject areas.

Supporting User Defined Custom Hierarchies In Central Datamarts (Neil Belcher, Cornell University)

Centrally defined hierarchies often do not meet the special needs of campus units. Units often spend a lot of time and effort downloading central data to local systems so they can add their own hierarchies, groupings and calculations. What if you could provide users with the capability of defining their own custom hierarchies/groupings within the central datamart? Would they still need to download data? This presentation describes one approach that gives individual users just that kind of power. We will discuss the user experience in terms of creating and maintaining custom hierarchies, how to use the hierarchies in reports,  as well the behind the scenes data structures used.

Tableau for the enterprise: Making the right design decisions (Devdutta Bhosale, University of Maryland University College)

A new class of lightweight BI tools focused on enhanced visualization features is gaining significant traction.  UMUC implemented Tableau to be used for both ad-hoc analysis and more formalized interactive dashboards.  Along the way we tackled significant design considerations around metadata management, security, dashboard performance, and user experience.  This session will share our design and implementation approach which has resulted in a robust, flexible, and sustainable Tableau environment that compliments the rest of our BI stack.

The University of Washington’s Strategic Plan for Enterprise Data & Analytics: A Year in the Making (Rob McDade, University of Washington)

In this presentation, Rob McDade will focus on the process developed and applied within the University of Washington’s Enterprise Data and Analytics team to implement a strategic roadmap that created a shared focus and vision for a large BI team. We will show how the process helped to grow the strategic thinking acumen of the team’s leadership, created alignment across disparate teams, and allowed the team to increase productivity and credibility in a political and fast changing environment. The talk will provide example exercises and artifacts that were produced to craft and document the team’s strategic roadmap.

Using the data warehouse to power IR’s University Factbook (Insiyah Jamal, Drexel University)

Come to this session to learn about how Institutional Research worked with IT to leverage the data warehouse to produce the University Factbook. Learn about extensions required of the DWH as well as the tool used to deliver the data to the end users. The session will also demo reports, dashboards and books deployed using Pyramid Analytics.

Visualizing the Data Warehouse: Metrics and Dashboards for the University of Missouri System Enterprise Data Warehouse and Reporting Team (Adam Morris, University of Missouri System)

Managing a data warehouse is no small task.  Large quantities of data, complex data transformations, and institutional demand for reports create a challenging environment.  One way to help manage the complexity is to keep a finger on the pulse of the day to day operations of the warehouse, and to gather data to measure how operations change over time – whether for better or for worse. This presentation will discuss tools and metrics the University of Missouri System Enterprise Data Warehouse and Reporting Team uses to aid in the day to day monitoring and future planning of Warehouse operations.  While the UM System environment uses specific tools for reporting and data transformation, this presentation will demonstrate visualization examples that could be done in any data warehouse environment. Topics covered in the presentation will include: Visualization goals, Specific metrics identified, Operational vs tactical metrics, Strategic Metrics: Looking at The Big Picture.

Weber State Uses Automation and Agility to Enable Fast Data Warehouse Development (Lucas Jones, Weber State and Michael Tantrum, Director, Field Programs, WhereScape)

Weber State’s data warehouse was 15 years in the making, the result of several false starts and a “build it ourselves” mentality which never delivered a fully functioning enterprise solution.  Over time the University ended up with multiple disparate systems with inconsistent data housed in data silos that proliferated throughout campus.  The University wanted the ability to enhance data consistency and sharing of data for reporting and analysis.
Attend this session to learn how Weber State employed data warehouse automation and agility to architect, develop and deploy a fully functioning enterprise data warehouse in nine months with just two staff members.  The data warehouse greatly enhances data consistency, data quality and data access for Weber State’s hundreds of users and is enabling analysts to ask and answer questions that were not possible due to the University’s previous data management infrastructure.

What? Our delivered warehouse product isn’t perfect! (Sharon Price, University of Colorado)

How confident are you that your warehouse data accurately represents all changes, additions and deletes from your transactional system? The University of Colorado (CU) Information Resource Management (IRM) team has been actively working to improve the quality and content of our student data warehouse for the past four years, upon our initial implementation of Oracle’s Campus Solutions Warehouse product.  The team has fully analyzed the delivered product, and identified and implemented a variety of improvements and enhancements to meet the expectations of the CU reporting community.  Such enhancements include:   comprehensive handling of transactional deletes in all warehouse reporting tables;  thorough analysis of all delivered ETL jobs to ensure all transactional changes are represented in all reporting objects; creation of robust reporting tables to serve cross-functional reporting needs.

Your Data in Motion:Turning Data Into Information (Matthew Pickus, University of Michigan)

Are you tired of searching Excel lists to narrow down the right list of prospects to visit? Wish there was a better way to determine the most likely respondents to a marketing effort? Want to improve your data decision making skills? Visual metrics – charts and graphics – help turn your data into actionable information that is easier to read. This  presentation will explore some industry tried and true best practices, some of the  pitfalls, and show you some of the work that is being done in Tableau at the University of Michigan.