2021 HEDW Conference Schedule

Color Legend

General Sessions
Technical Track
AI and Data Science Track
Student-Centered Track
Breaks
Vendor Presentations
Birds of a Feather
Social Sessions

 

Wednesday, April 14, 2021

11:30 am – 11:45 am EDT

Conference Welcome  

Dale Amburgey

2021 Virtual Conference Chair

11:45 am – 12:45 pm EDT 

Technical Insights Track

The Best of Both: Cornell’s Hybrid Data Warehouse / Data Lake Architecture

Jeff Christen
Cornell University

11:45 am – 12:45 pm EDT

AI and Data Science Track

Education Analytics Value Chain

Josh Magdziarz
West Coast University

11:45 am – 12:45 pm EDT

Student-Centered Track

Combining the Streams:  University-wide Admissions Analytics

Michael Salisbury
University of Rochester

12:45 pm – 1:00 pm EDT

BREAK/SPONSOR VISITS

1:00 pm – 2:00 pm EDT

How Data Virtualization is providing new opportunities to organizations like Indiana University

2:00 pm – 2:30 pm EDT

BREAK/SPONSOR VISITS

2:30 pm – 3:30 pm EDT

Birds of a Feather Discussion

Operating in a Global Pandemic

Group Discussion

3:30 pm – 4:30 pm EDT

Technical Insights Track

From Data Governance to Data Strategy: The Evolution of the Institutional Data Governance Program at the University of Toronto

Kiren Handa
Jeff Waldman
University of Toronto

3:30 pm – 4:30 pm EDT

AI and Data Science Track 

Lessons from Deploying In-house Predictive Retention Analytics

Jeremy Anderson
Rich Silva
Bay Path University

3:30 pm – 4:30 pm EDT

Student-CenteredTrack

Data Driven to Data Aware – Our Journey towards Data Utopia

Anthony Spagnuolo
Michael Arabitg
County College of Morris

4:30 pm – 5:00 pm EDT

BREAK/SPONSOR VISITS

5:00 pm – 6:00 pm EDT

Virtual Socialization or Vendor Conversations



Thursday, April 15, 2021

11:30 am – 11:45 am EDT

Updates for the Day 

Dale Amburgey

2021 Virtual Conference Chair

11:45 am – 12:45 pm EDT

Technical Insights Track

Best Practices for Reporting Snapshot Design and Implementation

Mark Ray
University of Arizona

11:45 am – 12:45 pm EDT

AI and Data Science Track

From Data Visualization to Data Science: Moving from Descriptive to Predictive

Heather Chapman
Weber State University

11:45 am – 12:45 pm EDT

Student-Centered Track

Dashboards for Student Success and Achievement

Sri Sitharaman
Columbus State University

12:45 pm – 1:00 pm EDT

BREAK/SPONSOR VISITS

1:00 pm – 2:00 pm EDT

Setting Goals and Creating Your Data Governance/Data Intelligence Roadmap

Brian Parish, CEO and Founder
IData

2:00 pm – 2:30 pm EDT

BREAK/SPONSOR VISITS

2:30 pm – 3:30 pm EDT

Birds of a Feather Discussion

Data Visualizations

Group Discussion

3:30 pm – 4:30 pm EDT

Technical Insights Track

DIY Data Applications – Self Service at its most basic level

Kristin Kennedy
Arizona State University

3:30 pm – 4:30 pm EDT

AI and Data Science Track

DataOps and Advanced Analytics at the University of Illinois System

Ashley Hallock
University of Illinois

3:30 pm – 4:30 pm EDT

Student-Centered Track

Yale’s Journey in Building a Student Operational Data Store

Jennifer Kirby
Kate Hathaway
Venkat Veeramneni
Yale University

4:30 pm – 5:00 pm EDT

BREAK/SPONSOR VISITS

5:00 pm – 6:00 pm EDT

BEACH BREAK



Monday, April 19, 2021

11:00 am – 12:15 pm EDT

HEDW Annual Business Meeting

HEDW Executive Board

12:15 pm – 12:30 pm EDT

BREAK/SPONSOR VISITS

12:30 pm – 1:30 pm EDT

Technical Insights Track

What the Learning Sciences Have to Say about Data Education & Training

Benjamin Wiles
Clemson University

12:30 pm – 1:30 pm EDT

AI and Data Science Track

When Regression Isn’t Enough: Using Simulation to Make Predictions

Abby Kaplan
Salt Lake Community College

12:30 pm – 1:30 pm EDT

Student-Centered Track

Applying the Power of Predictive Analytics to Student Success

Jessica Lemon
Connie Grove
University of Tennessee, 
Knoxville

1:30 pm – 1:45 pm EDT

BREAK/SPONSOR VISITS

1:45 pm – 2:45 pm EDT

Unlock your data to unleash your institution’s potential

1:45 pm – 2:45 pm EDT

 

Modernize Higher Education Analytics With a Cloud Lakehouse

.

2:45 pm – 3:00 pm EDT

BREAK/SPONSOR VISITS

3:00 pm – 4:00 pm EDT

Birds of a Feather Discussion

Data Governance

Group Discussion

4:00 pm – 4:15 pm EDT

BREAK/SPONSOR VISITS

4:15 pm – 5:15 pm EDT

Technical Insights Track

Analyzing the Cost of Your Programs: Data Matters!

 

Adam Raab
Dale Amburgey
Embry-Riddle 
Aeronautical University

4:15 pm – 5:15 pm EDT

AI and Data Science Track

Using Design Thinking to Enhance Data Literacy and Empower Campus Decision Makers 

Ashley Hurand
Jessica Gerlach
Lauren Isely
Nick Letson
University of Arizona

4:15 pm – 5:15 pm EDT

Student-Centered Track

Tips and Tricks with Tableau for Troubleshooting

Hillary Lincourt
Jeff Meteyer
University of Rochester

5:15 pm – 5:45 pm EDT

Virtual Socialization or Vendor Conversations



Tuesday, April 20, 2021

11:00 am – 12:15 pm EDT

HEDW General Session
Top 10 Survey Results
Membership Recognition and Conference Announcements
HEDW Executive Board
Heather Chapman, HEDW Research Chair

12:15 pm – 12:30 pm EDT

BREAK/SPONSOR VISITS

12:30 pm – 1:30 pm EDT

Technical Insights Track

Data Viz Style Guide – What is it, Why You Need it and How to Create One

Nadya Balabanova
Loyola Marymount University

12:30 pm – 1:30 pm EDT

AI and Data Science Track

Integrating R and Oracle for Better Insights and Analytics

Jenn Schilling
Trevor Kvaran
University of Arizona

1:30 pm – 1:45 pm EDT

BREAK/SPONSOR VISITS

1:45 pm – 2:45 pm EDT

Data and Analytics for All

2:45 pm – 3:45 pm EDT

Technical Insights Track

I Want to Do Something New and Different with My University’s Data. There Has to be a Better Way To Get It Through the System.

Don Willison
University of Toronto

2:45 pm – 3:45 pm EDT

Student-Centered Track

Roadmap to OBIA 12c and Oracle Cloud

David Xie
McMaster University

3:45 pm – 4:00 pm EDT

BREAK/SPONSOR VISITS

4:00 pm – 5:00 pm EDT

Birds of a Feather Discussion

Data Quality

Group Discussion

5:00 pm EDT

Conference Concludes

The Best of Both: Cornell’s Hybrid Data Warehouse / Data Lake Architecture

Over the last few years, many new technologies, terminology, and concepts have been introduced in the data space. Do new concepts, such as Data Lakes, replace our Data Warehouses we have invested so heavily in? What are the differences between Data Lakes and Data Warehouses? How do our customers’ data needs map to the various technology and methodology options? What data architecture will meet our current data reporting and analytic needs and enable us to meet future demands? This presentation covers Cornell’s journey to answer these questions and come up with a next generation data architecture.

Welcome and Announcements

Dale Amburgey, 2021 Conference Chair, welcomes you to the 18th HEDW Annual Conference and will provide information on:

 

How to enjoy the conference
Housekeeping tips
Conference etiquette

Education Analytics Value Chain

Many educational analytics initiatives are plagued by a plethora of barriers preventing advanced success. These barriers include multiple sources of truth, unreliable data, lack of ownership and other undesirable features resulting in stalled projects, depleted resources, and an unaware customer base. Organizations today have an opportunity to create an agile data analytics program that is prepared to handle the demands of a rapidly changing environment by defining structure and building value. The path to breaking through these barriers is accomplished by leveraging the Education Analytics Value Chain through navigating a business-driven approach via a Mission Driven Analytics Architecture.

Combining the streams: University-wide Admissions Analytics

We have developed a reporting and analytics solution that integrates Institution-wide Admissions data from multiple admissions systems including multiple instances of Slate, Hobsons/Campus Management, and internal systems as well as with existing Financial Aid and Student data. This solution provides Cognos and Tableau reports and dashboards to School Deans, Financial Aid, Admissions, IR, Office of Graduate Programs, and the Office of Global Engagement. We will discuss the needs of these user groups, the data models and integration strategy developed to unify the undergraduate and graduate admissions data across 6 schools, and demonstrate some of the dashboards and reports built.

How Data Virtualization is providing new opportunities to organizations like Indiana University

Digital Transformation extends everywhere in modern society and no more so than education. In the last year we’ve seen organizations have to pivot to online learning/interaction and normal is now a thing of the past. Discover how Data Virtualization can help organizations exploit Digital Transformation to provide new opportunities in a new era of education.

From Data Governance to Data Strategy: The Evolution of the Institutional Data Governance Program at the University of Toronto

The University of Toronto (UofT) is establishing its institutional data governance program. The institution is large, complex and highly distributed, presenting unique challenges to successfully engaging its divisions to implement the program. This presentation will introduce the approaches taken by UofT to date, including establishing the institution’s vision, guiding principles and implementation framework for the program. Based on the preliminary work completed to date, the institution is now moving forward with four ‘flagship’ initiatives that will set the direction for the people, process, technology and policy transformations required by the institution to realize its data governance vision.

Lessons from Deploying In-house Predictive Retention Analytics

Bay Path University developed an in-house predictive analytics infrastructure following the CRISP-DM framework. During this process, the analytics and IR teams learned a number of lessons for establishing clear business needs, wrangling data from various sources, cleaning and preparing data, deriving insights, and deploying findings to the staff responsible for student outreach. Besides providing a deep dive into technical considerations and future steps, a strong emphasis will be placed on collaborative approaches to engaging stakeholders to ensure applicability and application of analytics products. Attendees should leave with ideas and next steps for a predictive project at their institutions.

Data Driven to Data Aware - Our Journey towards Data Utopia

Come one, come all to the greatest presentation about data you’ll ever witness! You’ll be left speechless and wanting for more numbers, theories, and charts…ok, maybe not, but you will enjoy our presentation on how County College of Morris implemented a data warehouse that incorporates student enrollment information into one comprehensive data model. Since implementation, we have incorporated Admissions data, Guided Pathways KPIs, Tutoring Center information, and a cloud-based analytics tool for institutional level dashboards. We will lead you through our journey from data aware to data savvy and where we need to go to become a data driven institution.

Best practices for reporting snapshot design and implementation

Designing and implementing efficient, usable, and robust reporting snapshots can be a complicated task given the various competing needs and significant constraints involved. In this presentation we will provide you with general information about how to best evaluate your reporting needs and the appropriate snapshot methodology to support them and several best practices that we currently use to maximize the value of data that is available both for internal facing university evaluation and for use in external surveys and federal reporting.

From Data Visualization to Data Science: Moving from Descriptive to Predictive

Using data to make decisions is an important part of the landscape of institutions. Often, those requesting information don’t know the best approach to answering a question. Data meant to be used descriptively is often used in a predictive manner, with conclusions made that do not match the purpose of the data. This presentation provides practical tips for both stakeholders and analysts moving from descriptive presentation to predictive analytics. As part of the session, participants will receive a comparison of the two approaches and a flowchart that could be used to help make decisions about when to use each.

Dashboards for Student Success and Achievement

Columbus State University (CSU) recently adopted Microsoft Power BI as a reporting tool. CSU has now developed dashboards for student success and student achievement. Dashboards for non-productive grade distribution in all core courses and programs will assist faculty in redesigning courses and address inequalities in non-productive grade distributions across gender, race, low-income, first-generation, and age. Enrollment, Degrees Awarded, Retention and Graduation rate dashboards by major will help program coordinators and department chairs analyze student success and implement programmatic changes.

Setting Goals and Creating Your Data Governance/Data Intelligence Roadmap

Data governance (or what is more broadly called Data Intelligence) is often identified as a key initiative at a higher education institution. However, many efforts to implement data governance encounter challenges in starting or maintaining momentum. Data governance success is often built from having a good assessment of your institutions’ strengths, having clear actionable goals, and a pragmatic roadmap for implementation.

Covered in this session:
* Current-state data governance assessment strategies
* Developing data governance recommendations
* Defining the scope and prioritizing which recommendations to implement
* Defining the scope(s) of your data governance initiative with prioritization
* Building an actionable data governance plan and moving from planning to doing
* Maintaining momentum and buy-in
* Measuring success and return-on-investment

By attending you have the ability to receive a sample assessment workbook. Hope you can join us on this important topic.

DIY Data Applications – Self Service at its most basic level

Do it yourself Data is now easier than ever. There are many powerful tools that users use every day that if used properly could allow them to truly be self sufficient with their data needs. By learning a few best practices around data both in collecting it and storing it, you will create partners in your institution that will truly allow your institution to become a data informed institution. This presentation will show you ways to help your users learn how to do this and how to be successful.

DataOps and Advanced Analytics at the University of Illinois System

About a year ago, the University System began a new analytics adventure. We would like to talk about how we have grown with a very small footprint using DataOps principles, even throughout a pandemic!

Yale’s Journey in Building a Student Operational Data Store

We will discuss the major drivers leading to the decision to build a Student Operational Data Store and not jumping right into a data warehouse implementation. We will focus on the implementation journey , technical design, and change management, and lessons learned.

What the Learning Sciences Have to Say about Data Education & Training

Data literacy, statistical reasoning, data science education, data fluency, information literacy, quantitative reasoning, etc. have become focal points for organizational data strategy. A great deal of pragmatic theories for learning and motivation with broad application has been developed over several decades and have been utilized in collegiate STEM education and technical disciplines. This presentation will highlight big ideas from the learning sciences to build a framework for holistic conversations around data educational programming in the workplace.

When Regression Isn't Enough: Using Simulation to Make Predictions

Regression models are useful when there is a straightforward relationship between the predictors and the outcomes we are interested in. However, when we are trying to model a complex set of connected systems, regression is not always an appropriate tool. In these cases, simulation may allow us to explore how small changes in one part of the system affect the system as a whole. Moreover, simulation allows us to expand beyond the limited data available to us by running pseudo-experiments that would not be possible in the real world. This talk presents a case study of a project in which we used simulation to predict how changing the design of a placement test would affect what courses students take and how well they perform.

Applying the Power of Predictive Analytics to Student Success

Can how a student engages in nonacademic activities on campus affect academic outcome? This presentation illustrates the use of predictive analytics to gain insight into how levels of student engagement affect GPA, persistence, retention, and graduation. We’ll review our approach used for analysis of available data sources, evaluation of machine learning tools, development of the statistical model, architecting and building the web application, securing the application, and managing the flow of data from end to end. We’ll conclude by discussing the impact of variations in learning modalities on the application of the models.

Unlock your data to unleash your institution's potential

The variability of data sources, products and technologies in use across educational institutions means that most organizations have been forced to take a bespoke approach to building an institutional data warehouse. But this very flexibility means that each institution is on its own path, making it difficult to share data with third parties for things like collaboration, comparison and quality assurance. In this session, we will explain how Blackboard’s standards-based approach to data warehousing using best-in-class infrastructure retains flexibility but unlocks collaboration, at the same time removing barriers to entry for smaller institutions.

Modernize Higher Education Analytics With a Cloud Lakehouse

Universities today are trying to tap into advanced analytics to improve the student experience, optimize fundraising, and enhance operations. Legacy investments in proprietary data warehouses make it hard to chart a path forward. Many universities grapple with data silos, the expense of maintaining complicated data architectures, reduced time-to-insight, and limited support for predictive analytics.

These needs can be better addressed with “lakehouse,” a modern data architecture that combines the best elements of a data warehouse with the low cost, flexibility and scale of a cloud data lake. This new, simplified architecture enables organizations to bring together all their data — structured and unstructured — in a single, high-performance platform for both traditional analytics and data science. Now, teams can get all the insights they need to deliver on their goals in real-time.

Join our session to learn:

  • The state of data analytics and AI in higher education
  • How to modernize your organization’s data strategy with a lakehouse in the cloud
  • How to analyze and visualize all your data in real-time with SQL Analytics 
  • Live demo

Analyzing the Cost of Your Programs: Data Matters!

Building upon the program cost modelling foundations presented last year, this session looks to provide attendees with a deep understanding of the types of data that may be required for this type of analysis. Speakers will provide real world examples of the different variables that can be incorporated. Focus will be on the integration of data from different sources, including financial, human resource, and student data systems. Best practices will be shared, as will pitfalls and trouble points. A worksheet will be provided that will help attendees. recognize and gather the right kinds of data for their own model.

Using Design Thinking to Enhance Data Literacy and Empower Campus Decision Makers

University Analytics & Institutional Research (UAIR) strives to give the University of Arizona an institutional advantage through outstanding data services built on accessibility and accuracy. Over the past several years, UAIR has worked to improve data availability through continual development of the Enterprise Data Warehouse and accompanying self-service business intelligence tool. However, UAIR has recognized that data can be difficult to consume, even when readily available. This presentation will cover design thinking and data literacy concepts, as well as four examples of how UAIR has used design thinking to improve data literacy, with the ultimate goal campus decision making capacity.

Tips and Tricks with Tableau for Troubleshooting

In this session, various tips and QA functionality will be demonstrated as a user is troubleshooting a visualization from the data up thru the visual using Tableau

Setting Goals and Creating Your Data Governance/Data Intelligence Roadmap

Data governance (or what is more broadly called Data Intelligence) is often identified as a key initiative at a higher education institution. However, many efforts to implement data governance encounter challenges in starting or maintaining momentum. Data governance success is often built from having a good assessment of your institutions’ strengths, having clear actionable goals, and a pragmatic roadmap for implementation.

Covered in this session:
* Current-state data governance assessment strategies
* Developing data governance recommendations
* Defining the scope and prioritizing which recommendations to implement
* Defining the scope(s) of your data governance initiative with prioritization
* Building an actionable data governance plan and moving from planning to doing
* Maintaining momentum and buy-in
* Measuring success and return-on-investment

By attending you have the ability to receive a sample assessment workbook. Hope you can join us on this important topic.

Integrating R and Oracle for Better Insights and Analytics

In this session, we will present two approaches to integrating the statistical computing software R and an Oracle-based Enterprise Data Warehouse that have successfully been deployed by the University Analytics & Institutional Research department at the University of Arizona. In the first approach, a server-based instance of R is maintained and called on a nightly scheduled basis to update enrollment predictions. In the second approach, a live connection to R is called from an Oracle Business Intelligence dashboard, allowing senior decision-makers to conduct advanced what-if analyses. We will explain best-practices and tips for how to avoid common problems.

Data and Analytics for All

In years past, only larger enterprises had the means to create data solutions and deploy business intelligence to satisfy information needs. Thanks to the changes brought on by cloud solution vendors with consumption pricing and elastic architectures, the opportunity exists for all enterprises to leverage data to understand their enterprise and drive decision making. Performance Architects has partnered with leading data pipeline solution provider, Fivetran, to help enterprises gather and deliver transformed data to cloud databases for analysis and reporting. In this session, Performance Architects and Fivetran will review the changes that occurred to get to this point and explain how the solution can work for any enterprise.

I Want to Do Something New and Different with My University’s Data. There Has to be a Better Way To Get It Through the System.

University of Toronto had an ad-hoc process for authorizing access to institutional data for “non-routine” purposes, which was perceived by data users to be opaque and inconsistent. We describe the development and early roll-out of a common review process designed to improve the transparency, equity, and efficiency of access. Key features include: a single point of contact; a risk-screening tool to triage the level of review; a systematic process to gain necessary perspectives and approvals; a registry of projects to provide audit capacity; and post-approval assistance with assembly of datasets, metadata, and analytics as required.

Roadmap to OBIA 12c and Oracle Cloud

As an OBIA customer, we have several options going to Cloud. We would like to share our experiences to peers on what we are now(OBIA 11.1.1.10.2), what our goal is (OBIA 12c Cloud), how we will reach that goal (SaaS, PaaS, DBaaS, IaaS).