2020 Presentation Descriptions

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This is an alphabetical listing of the member presentations due to be given at the 2029 HEDW Conference in Ogden, Utah. The formal Conference Agenda, listing specific times and locations for each presentation, will be available as the conference draws nearer.

Member Presentation Descriptions




A Student Touch-Point Universe Revisited
Mick Haney, Nevada State College

This presentation will expand on the framework presented at HEDW2019 for integrating student data from admissions to graduation and everything in between. We will briefly revisit the framework levels while introducing new concepts of supporting Header and Trailer years, and going down a level to illustrate how we support Student Program and Plan information over time. We will technically illustrate how to populate the framework. We will then technically discuss how the framework drives the population and enhancement of an integrated student database. The presentation will close with a technical discussion (and SQL Samples) on how the framework design is utilized to support retention and graduation rate analysis for any identifiable COHORT. This presentation will be of interest to Data Architects, Persons interested in creating a Data Driven Environment, and database developers.



Accelerating Analytics Across the Enterprise
Pramod Kunju, University of California – Irvine

Analytics across disparate data repositories has been an ongoing challenge in every organization. This usually involves moving data from multiple applications to a single data stores – which is costly from a data movement, and modeling perspective.    There are several innovative solutions to this problem. One of the effective solutions is using Data virtualization. The session will cover use cases where this solution is appropriate, including implementation plan, and best practices.    According to Gartner, “By 2020 Organizations Utilizing Data Virtualization Will Spend 45% Budget, Less Than Those Who Do Not, on Building and Managing Data Integration Processes for Connecting Data Assets.”    Attendees will learn many techniques for Data analytics across functional areas, including:  1) Quick and efficient proto-typing without the need for expensive ETL  2) Creating virtual single source of truth for the whole enterprise  3) Strategies to address short-term ad hoc information needs



Admission Application Weekly and Daily Snapshots
Hairong Liu, University of Missouri System

The project is to do trend analysis in various levels and categories regarding applications, admits, accepts, and enrollment. A flat table has been created to capture application details on a daily basis, appended on a weekly basis at exact same time.  The president’s office and the IR office uses this table heavily through hundreds of reports.  There are trend analysis from week to week on application/admission/accept counts, trend analysis compared to past years, counts between Residency and Non Resident students, counts in a more detailed level such as First Time College, transfers, graduate, and professionals, details on each individual applicant and application.  An aggregated table has been created for reports to improve performance.  Campus specific WEEKLY/DAILY/CENSUS_DATE views have been created for access control among different roles.  The tables are accessible on an Oracle database, and also loaded to Cognos for users to generate and run their own customized reports.  The table includes but not limited to term info, application info, applicant demographic data and financial aid info, applied academic data, test scores.  Those data points can be used for aggregated reports.



Analyzing the Costs of Your Programs: It’s All About the Model
Adam J Raab, Embry-Riddle Aeronautical University

Revenue. Expenses. Margin. These terms are often considered anathema to the academy, but in today’s hyper-competitive market for students and funding, they are more important than ever. At the center of this issue is the cost of teaching. Over ten years ago, Embry-Riddle Aeronautical University endeavored to discover just what their academic programs were costing them. It was determined that a model was needed to combine student and financial data in a way that would result in actionable unit cost of production information, usable by both academic and administrative leadership.   This presentation will introduce participants to this model and explain how it functions. This will be a technical presentation, and will cover topics ranging from data sourcing to computational methods. Context will be provided in regards to the models that are available commercially and how they compare to the in-house model being presented. The intention of this presentation is to give participants something to work from, and to set them on the right track towards their own model. This presentation is intended for intermediate practitioners who are knowledgeable about their data and how to work with it, but may not understand program costing methodologies.



And that’s a RAP……How the University of Washington is transforming a 1,000+ Workday report inventory in short order!
Karen Matheson, University of Washington

In 2017, the University of Washington for the first time implemented a SaaS based enterprise system by replacing its 35-year-old legacy HR Payroll System with Workday. During our first year with Workday, we quickly learned about Workday’s special brand of technical report and system configuration tools. This provided a backdrop to come up with report development and maintenance best practices. However, we discovered significant technical debt from before go-live, which contributed to user frustration and lack of campus report utilization in general. This session will share the highlights of our Reporting Adoption Project (RAP) strategy, which aims to address the technical debt, as well as engage report stakeholders in working groups and testing. The end result of this project will culminate in a complete revision of the 1,000+ report inventory.    During the presentation we will cover our methodology for engaging campus stakeholders through surveying, focus group and report testing, revising our Workday security model, creating a training and outreach plan, and technical report inventory revision execution.



API Directory adoption at University of Michigan
Kranthi Bandaru, University of Michigan

This presentation will cover the adoption of the API Directory at University of Michigan.   We will cover the following topics:  – Business and technical needs, along with use cases for implementing APIs.  – Advantages of converting traditional applications to APIs.  – An overview of the University of Michigan’s API directory implementation.  – Additional features such as subscription levels, throttling rates, auditing, and data governance.  – How to overcome resistance from data owners and make them a part of the API journey.  – What’s in the future for the API Directory.  – How APIs can help your cloud migration strategy.



Building a Data Warehouse for Workday Student
Mike Salisbury, University of Rochester

The University of Rochester has recently become the 1st R1 tier institution to go live with Workday Student (branded UR Student). As part of our UR Student program, we have also replaced our current student data warehouse with a redesigned UR Student data warehouse with integrations to Workday Student. This presentation will discuss our strategy, evaluation of Workday tools, implementation approach, and architecture as well as our hard-earned insights into the challenges, critical success factors and best practices. If your institution is planning to transition to Workday Student in the future, come and learn from our experiences.



Building a SAS Admissions data model including dashboards, reports and data dictionary
Nadia Mankins, University of North Texas

Admissions can be a complex subject as it covers many areas such as test scores, high school and college information, events attended, housing application, orientation, different application processes between INTL, UGRD and GRAD office, etc. My goal with this presentation is to provide you with useful tools and examples, so you can complete a successful SAS data model within a year, all these including data validation, dashboards/reports creation and data governance documentation. I will share my experience on how helpful it is to have an organized and comprehensive worksheet with all the tables and fields/indexes mappings, fields length, calculated fields, etc., this will save a lot of time when identifying the fields for the data dictionary and the Data Governance team. I will also suggest what fields could be part of the daily snapshots. In addition, this presentation will show you the importance of having multiple resources available in one model, so users can filter the data based on their needs. Examples of dashboards will also show what areas could use some attention as missing data can affect reliable outputs, such as missing zip codes, etc.



Creating Better User Groups with ACM and Grouper
Julie Parmenter, Indiana University

Everyone creates user groups but few of us do it well. Those of us working in the Business Intelligence space create thousands of user groups for access and authorization. Groups are manually created and managed on numerous different platforms, including some with very complex user interfaces, with no standards, consistency nor audits. Many of these groups contain users who should no longer have access due to termination, retirement, job change or expired compliance credentials. This presents a significant security risk. The loss of productivity due to the number of people manually managing groups on multiple platforms in many different ways is also quite high.   Access Control Management (ACM) is software developed at Indiana University that allows for the creation and management of user groups and serves as the easy-to-use interface to the role-based management tool, Grouper. ACM has security features such as the automatic removal of terminated users, built-in compliance checking and alerting on users with job changes. ACM was originally developed as a method to automate security for Tableau workbooks. However it was quickly realized that the use of this tool could be expanded to be a global group management system. In this presentation, we will demo the software and highlight all of the types of user groups that we are building from local Tableau groups to Active Directory groups to authorization of cloud-based applications.



Data Cleansing using the Google Places API
Charles Rosenberg, PhD, University of Rochester

Students applying for admission to the University of Rochester are required to enter the schools they have attended previously along with their GPA and degrees received.  These data, along with city, state/province and country, are entered as free-text and so data quality is a major issue. The goal of this project was to try to find the best match to a list of approximately 50,000 schools sourced from the College Board. Using the Jaro-Winkler similarity score based on the school name, matches were found for only about 20% of the 110,000 schools entered and many of the matches were poor quality. An alternative approach was explored in which schools (school name, city, state/province, country) were looked up using the Google Places API. The resulting unique identifiers (place_ids) were compared to those for the College Board schools. The resulting performance was about 85%, which is probably near the theoretical limit given the quality of the data. Run time also scales only linearly versus exponentially using similarity matching.  This talk will present the architecture of the system and its implementation in Oracle Data Integrator.



“Data-Driven” Student Success for BI Teams Big and Small
Nate Rochester, Portland State University

“Data-driven” has become a modern mantra of higher education administrators at all levels, and is referenced often as an ideal to strive towards in matters of enrollment management, program design, and other organizational decision making. It has also become a standard expectation that student success initiatives should be “data-driven” in their development and implementation. This presentation explores some of the main challenges and obstacles to achieving this ideal for student success efforts, as well as strategies to overcome them.     I begin by briefly discussing the challenge of defining “data driven” in the context of student success initiatives. Rather than prescribing a single definition, I describe the flexibility of this term to be defined within the scope of a given initiative and the data and resources available. Data should not amount to mere anecdotes, but neither does it have to reach the level of predictive analytics. Suggestions and examples are provided.    I also discuss the challenge of separation (organizationally and in terms of expertise) between the IT/IR professionals who manage institutional data and develop reporting tools, and the administrators, academic professionals, and faculty members intended as their audience. Several key strategies are presented as ways to address this separation and related challenges in providing data resources for student success. These strategies fall into three broad categories: (1) Bridging the divide between academic units, advisors, and IT; (2) Building appropriate tools for particular audiences and applications; and (3) developing system integrations that allow reporting tools to engage with academic requirements.



Data Science: A Journey Not A Destination
Craig Rudick, Director of Product Strategy and Data Scientist, HelioCampus

Data science is the art and practice of answering questions using data. Effective data science programs employ a wide range of tools from the humble bar chart to advanced analytics algorithms. While predictive modeling and machine learning are powerful tools, their major strength is also their most important limitation: they answer very specific questions very precisely. In order to use their results effectively, they need to be put in a broader context, informed by a more holistic data analysis. We have often found that the insights gleaned from initial exploratory analyses are ultimately the most impactful, and they are absolutely crucial for laying the foundation that allows predictive modeling results to be utilized effectively. We will discuss techniques, both technical and practical, for designing and implementing a data science program that uses the full range of available tools, and creates the broad and deep knowledge base upon which true data-informed decision making is grounded.



Data Science: Data Warehouse & Data Visualization for Banner, What’s Next?
David Pacific, Ryan Snyder, and Itzik Maoz, Converge Technical Solutions

We continue our exciting journey with Banner based institutions, while expanding the ability to deliver quality and trusted dashboards and data visualizations while building out a team of data stewards and champions. We will showcase the benefit and value of a phased approach to data warehousing while allowing for the development and adoption to start small and iterate more easily over time. Gaining insight into the cycle of applications, admits, and enrollments is no easy feat and the roller coaster through development, enablement, and implementation can be met with stakeholder resistance at times but lessons were learned and growth opportunities realized. In this session, we will examine a combination of technologies and concepts, highlighting the end-to-end data flow and efficiencies from data extraction, data transformation, standardization, and finally, interactive presentation. The unique insights collected through this process, help institutions understand important key metrics and in turn result in the optimization and confident predictions in areas such as retention, admissions, and enrollments. All of these are prototypical needs of growing universities and colleges with the thirst for data and exercising their insights.



Data Virtualizations Role in Higher Education Digital Revolution
Saptarshi Sengupta and Emma Stein, Denodo 

Technology is changing higher education in a way it has never before. Who would have thought that working professionals could get certified from some of the most coveted and accredited institutions of the world without ever stepping foot on campus? But that’s a reality
now. Even the traditional higher education institutions are making their online presence better than ever. Attend this session to learn:

What is data virtualization technology

Why data virtualization is a critical piece in higher educations digital revolution

How some of the best higher education organizations are using data virtualization



Diving Into the Deep End: How We Built a Digital Education Dashboard Using Cloud Technologies and Agile Methodology
Roland Hall and Katerina Stepanova, Brown University

The project began in mid-May 2019 with the goal of creating a data lake using cloud technology. Our new CIO charged us to execute the project quickly, incorporating agile/SCRUM methodology, which was new to the project team. We were learning the agile methodology while simultaneously getting up to speed with new tools and data concepts.    The project team successfully developed dashboards that provide information on built-in tool usage in Canvas. We developed our data lake infrastructure on Google Cloud Platform, storing data in Google BigQuery and Google Cloud Storage. We used Talend and Python to ingest data. We built our dashboards in Tableau.    We learned to adopt a more modern and agile methodology for building curated data. The power of the cloud lets us deliver speedy performance even when using complex views that integrate data from disparate sources. We can quickly create usable prototypes and rework them as needed. These views can be used to build the data pipeline and dynamically create tables that can later be reverse engineered into a logical model.    We ran into challenges and learning opportunities every step of the way, from using Canvas data, to working in the cloud to developing effective visualizations. We will describe our process and talk about our lessons learned and where we are now.



Doing Regression When Your Dependent Variables Aren’t Well Behaved
Abby Kaplan, Salt Lake Community College

We will review the three types of regression described and apply each one to a sample dataset of LMS assignment submissions: we will use beta regression to predict grade (expressed as a percent), ordered logistic regression to predict letter grade, and multinomial logistic regression to predict how the student submitted the assignment (text entry, file upload, URL, etc.).  The sample dataset and the R code for each regression will be available for participants to download.


EASY Data Warehousing using Software Robots and AI with Pyramart
Dan Bruns, Senior Data Architect, Pyramart

Pyramart is a revolution in Data Warehousing (www.pyramart.com). Our patented Intelligence Engine builds and maintains your agile, powerful, scalable data warehouse for you – automatically. Just tell Pyramart how you want to bring your data together from any location and Pyramart will build and maintain the rest for you, using data warehousing best practices. Time to Market shrinks from months to days. Come watch us build a fully functional data warehouse in only 15 minutes!



Education Data Hub: Data Management to Power Strategic Analytics
Danielle Yardy, PhD, Director, EAB

Education Data Hub (EDH) organizes data from across your campus using a system- and vendor-agnostic data model designed for higher education that democratizes access with common-language data definitions and a user-friendly interface. In this session, we will demonstrate the capabilities of EDH, including data model configuration and ad-hoc reporting, and explore how the platform enables integration and better data governance. We’ll also share how our university partners are using EDH to power their analytics and solve complex problems in IT, IR, and beyond.



ETL for Power Users?  Yes, and we will show you how
Kristin Kennedy, Arizona State University

In the history of Analytics and Business Intelligence, the process of Extracting, Transforming and Loading (ETL) has been owned by centralized IT or Institutional Research or some rogue department.  As the world of analytics continues to evolve it is becoming harder and harder for these groups to keep up with the demand.  Between an abundance of new data sources from disparate systems, a departure from traditional databases for applications as well as an increased demand, it is becoming impossible to meet the demands of customers.  At Arizona State University, we have a large group of sophisticated power users who have been meeting the needs of the business in creative and agile ways for many years.  We decided to help them do what they have been doing, better and easier.  As a result, we brought in a series of tools such as Alteryx and Aurora PostgreSQL to come up with a way that these subject matter experts can be our partner in getting data out to the business in an enterprise, sustainable way.  This session will go over how we did this, as well as sharing all of the victories and challenges of its implementation.  We will share with you our architecture as well as some ideas of costs and skills needed, from both IT and the power users.



From Big Data to Free Computer Rentals: Transforming Data to Enable Student Success
Corinne Briggs and Russell Youngberg, Brigham Young University

In order to have more proactive interventions leading to student success, we needed student-centric data. Learn how Brigham Young University took application-centric data from multiple sources, transformed it into student-centric tables to be consumed by machine learning modeling, and then transformed the input and output data into a format consumable by end users.  We are still working through challenges of combining multiple data sources, but we have built a reliable and resilient Early Alert system to allow proactive solutions to individual student challenges in just a year.  The data structure we created is also scalable to be useful to other projects now moving forward and to allow additions of new data sources.



From Statistical Significance to Practical Application: Communicating Predictive Results between Analysts and Stakeholders
Heather Chapman, Weber State University

As predictive analytics and machine learning become more and more popular across higher education, the importance of translating findings from these complicated analyses is critical. Typically, those requesting the analyses and those conducting them come from two different worlds, and have very different expertise. Stakeholders often have a very good understanding of the messy issues that exist, but often have a hard time translating that into questions for a data scientist to answer. On the other hand, data scientists may have the technical expertise to run the analyses but often lack the real-world application experience to translate findings into a language the stakeholder can understand. The full power of predictive analytics results cannot be reached without addressing this mismatch in expectations and expertise. This presentation provides practical tips for both stakeholders and analysts to bridge the gap when reporting results.     Specifically, this presentation will attempt to provide answers to important questions such as: What makes a good research question? What are some of the most common issues associated with variables used in higher education research? What are the critical components that should be provided when reporting on predictive analytics?      As part of the session, participants will receive a step-by-step guide for both requesting analyses as well as providing results.



Growing analytics capabilities at a small liberal arts college for a data informed future
Dobby Spencer, Meghal Parikh, and Bay Rodriguez, Rollins College

Traditionally, institutional data in higher ed were derived from a small number of guarded sources. This data was ETLed into data-marts or relational databases for reporting. Data stewards could refine the data upfront supplying limited data elements for analytics and data governance emphasized on data definition management mostly on reporting side. Today, institutional data is distributed across multiple systems, is unstructured and alignment with Enterprise Data Warehouse (EDW) is complex. When data is sourced from multiple sources and is continually refined at every stage to analytics, governance becomes a business process driven exercise entailing what we call as data lifecycle management rather than data definition management. Data stewardship involves managing the usability data and increasing the value of the data domain. Data monitoring responsibilities shift from stewards to technology managers who become the caretakers of data quality.    In 2017, data analytics became a strategic priority to inform complex, timely decision making at a small college. The strategic goals included modernizing the data technology services, deploying an integrated data warehouse, developing a self-service BI architecture and improving data management. Execution of these goals hinges on a modernized, distributed data governance framework with re-imagined business processes, support for multiple systems and new institutional reporting standards; all disruptive to the existing conventional data culture at the institution. This presentation will share the progress towards a modern vision for institutional analytics leveraging external consultants, a revitalized data technology portfolio, and strategic hiring of dedicated data professionals.



How do we maintain applicability in a new analytics environment and capitalize on citizen data scientists across the university to meet advanced analytics needs?
Ashley Hallock and Dimuthu Tilakaratne, University of Illinois

The University of Illinois System is actively working toward meeting our changing needs in data and analytics. Through a use case gathering mission, consultations with other Big Ten Universities, previous HEDW presentations, and discussions with Gartner Analytics we prepared a recommendation and foundation for a System-wide Advanced Analytics service. We would like to share our plan forward and discuss our successes and opportunities.      The first of our efforts is to allocate resources to maintain the existing Data Warehouse and BI/Data Visualization solutions, while modernizing our tools, storage and processes. Success in this effort includes re-engaging our current community of reporters and analysts who have built extensive reporting and visualization solutions using our platform.  The second effort is to strategically delve into advanced analytics using an agile framework created by our Innovation team. This is done with virtual teams created around projects to answer the next generation of from our use case list. We are engaging our end users in an effort to create an environment of Community Data Scientists who can discuss everything from university data to software to the next big model.



How UCF use Power BI to monitor our IT services
Priscilla Camp, University of Central Florida 

Curious how to monitor your IT Services using Power BI? Come see how UCF tracks our IT services to ensure our staff, students, and faculty are meeting their teaching, learning, research, and service objectives. Utilizing ITIL (Information Technology Infrastructure Library) practices, ServiceNow data, and Power BI we are able to take quantitative information and turn it into graphical visualizations to aid leadership and our customers in making informed decisions.



Just In Time Data Management with Data Cookbook
Brian Parish and Brenda Reeb, iData, Inc. 

IData recommends a “just in time” approach to maintain momentum in your data governance initiatives. We find this approach is successful for organizations with any maturity level. Using our Data Cookbook product, you can focus participation in your governance activities without a lot of bureaucracy. With each successive problem solved, you maintain momentum and gradually achieve success. This session shows you how to use Data Cookbook to manage a business glossary, a report catalog, and a quality rule catalog. You will also learn how to automate assessment of data against quality rules; present highly technical metadata to technical and business users; and control changes to valid values and schemas. The Data Cookbook is a collaborative tool that can support all of your data management needs. This collaboration gets everyone on the same page quickly, so that you can get on with making better informed data-driven decisions.



Modularizing Data with dbt
Jacob Mastel, Oregon State University 

My plan is to give a high level overview of our new data pipeline in AWS and Snowflake, and how dbt fits within that pipeline. Then, I plan to go over the principles of what dbt is. Once the audience has an understanding of what dbt is and its general features, I’ll go through a real world example that we are actively using including small code samples.



Python for Data Science: How to leverage your EDW
Ken Nagle and Shelin Mathews, University of Notre Dame

1) Introduction to Python for Data Science  2) Introduction to Jupyter Notebooks  3) Demonstration of how to connect to an EDW and execute a SQL query.  4) Demonstration of how to perform Exploratory Data Analysis with Python.  5) Demonstration of how perform a variety of modeling techniques (Possibilities include linear regression, neural nets, etc).  I’ll figure this out as I put together the presentation to ensure that its both a powerful and  accessible demonstration.



The Dream House that Data Governance Built
Barry Goldstein, Washington University in St. Louis

Data Governance can take on a lot different paths, options and capabilities.  Similar to when you are building a house the options make most people’s heads hurt.  You want everything – but can only do so much.  In this presentation we will take a look at the major steps needed to build your house and how those parallel the Data Governance path.  Along the way we discuss helpful tools and tips.  We will cover People, Process and Tools involved through DG construction.    Topics will include    • Funding / Resources – Executive investment / commitment / approval?  • Vision / End State – What is success?  What are the milestones?     • Find a Building Site – Where will we build?  Does it matter?  • Architecture / Blueprints – Your plan and timeline?  • Prepare construction site and pour foundation – Your Stewardship Structure and foundational pieces.  Your DG Framework and Stewardship Structure.  • Construct rough framing – The Policy / Standards / Guidelines and guardrails.  • Complete rough plumbing, electrical and HVAC – The processes – how will still stuff work.  How data will flow through its lifecycle….  How is data created, stored, used, disposed of….  • Finish Interior and Exterior – What Tools do we need?  Can we customize and adjust?  • Walkthrough of finished House – How will we maintain and sustain DG?  • Host an Open House – Show it off / show the value you add.  Communication and marketing  • Enhancements / Future Projects – What are the next phases?



To know is to learn what you have
Lance Tucker, Boston College

Boston College, like many schools, has hundreds of applications and databases used to manage information. Less sophisticated electronic data stores (e.g. Google Sheets) may also serve as systems of record for some business processes. Preferred solutions are often chosen and managed by multiple groups or departments within the University.  In 2017, we began cataloguing our information sources.  To date, we have developed metadata for over 600 items across nine vice presidential areas.  An application was developed to complement this collection that both houses and manages the records, as well as allowing a user to visualize or report.    This presentation will cover how we identified the metadata, the process used to collect the information, and how information can be utilized.  While it has taken considerable time to research and collect the data, managing the information is fairly easy and provides a good tool for IT managers to collaborate with their customers.  The presentation also covers how we accomplished this effort with minimal staff resources and cost.  We are now in the process of demonstrating the value this information can provide.    Like many of you, when we began our information governance program, we dove right into terms and data dictionaries.  This approach proved to be a sizable challenge. It was difficult to develop the business process and garner participation to support and sustain the effort.  We learned that starting on higher ground with an information catalog is a better approach to indoctrinate staff on governance practices and the value of curated management.



Upgrade or Trade-in: Navigating your data warehouse through a major change
Ravindra Harve, Boston College

For the past several years, Boston College has been converting a mainframe-based student system that serviced the University for nearly forty years. The new system is a service-oriented architecture with a code foundation supplied by the Kuali Consortium. The approach taken has been to implement this conversion iteratively module by module, student accounts, financial aid, course catalog, enrollments, and other subject areas. Implementing the new transactional system has been a challenging task for the University as the data dependencies between the student system and other ERP and departmental applications are considerable, as well as dependencies within the student system itself.    Data Warehouse developers challenged how to keep historical reporting relevant when the new source system is very different. The analysis involved going through thousand’s of tables, new column names, new business logic. This presentation will explore the process used for developing a strategy to accommodate the significant change. We will present the approach the EDW team used to Discuss with stakeholders, negotiations with application developers, and how to build timelines and propose solutions.  Besides, how to use information governance tools to assist with developing solutions and help navigate through technical details requiring the adoption of a new environment. This effort is still a work in progress, but we will share what has worked to date and what has been problematic. After trial and error, a discussion around approaches used to open developer communication channels, working with multiple project managers, and vying for resources and funding are to be covered by this presentation.



Using Data Virtualization to break down data silos and solve other data problems
Richard Hanks and Roger Tervort, Brigham Young University

When I arrived at BYU, the decentralized storage and use of data was a challenge.  There were many data silos, a lot of replicated data, challenges in securing and delivering data outside of applications, multiple database platforms, the need for customers to use and update their own data in reporting environments.  By introducing data virtualization, we have made in-roads in solving these problems.  Some of the data silos now use DV to access their own data and when they have approve Data Sharing Agreements, the requested data is simply given to them by changing access permissions.  This has saved many time consuming ETL processes, additional replication of data, and now provides a more central point for campus to access their own and enterprise data.  My team was spending a lot of their ETL development time copying tables from one database platform to another.  DV has eliminated a lot of the light ETL work and data replication and has allowed users to access data at its source.  Our security team receives JSON and csv files from our sister campuses and through DV, both of these types of data files can be used as tables for SQL queries and enhance their architecture.  Campus departments with specific faculty roll-ups can load their own look up files or roll-ups into DV and can use them with other reporting data.  The Library has anonymized some of their patron data and through DV has made it accessible to other areas of campus for analysis.  The library uses the faculty data to fine tune the databases they subscribe to in order to make sure they include publications and databases where the faculty publish.  These are just a few of the examples of where data virtualization can assist distributed environments and customers get the data that they need.



We Don’t Have Suites: tackling master data in an institution of competing business priorities
Cynthia Carlton, University of Rochester

In 2017, when several enterprise projects surfaced the need to have consistent data for leased and owned buildings, the University of Rochester sailed headfirst into uncharted territory of master data. We brought together a working group made up of individuals representing data domains and technology systems across the institution. We gathered input from anyone that would talk to us. We benchmarked industry standards. We figured out what we needed, who we needed, and recommended how we could accomplish the goal. “This won’t be that difficult!”, we naively declared to ourselves. This presentation will tell the story of our master data journey with location data. From the initial working group, to the implementation, and the inevitable course corrections. We’ll share the places we’ve been successful, what we learned from the challenges that we encountered and where we’re headed next. While this voyage hasn’t been smooth sailing, it’s certainly an adventure you don’t want to miss.



What a long strange trip it’s been: Tales of a First Chief Data Officer
San Cannon, University of Rochester

Data governance is an often discussed but not often understood practice.  While common to many industries, most of the applications and guidance available for starting and sustaining data governance programs is better suited to profit driven corporations rather than mission driven universities. This presentation will cover the drivers that lead to the hiring of a chief data officer as well and the progress and challenges faced when setting up a more formal data governance program within a higher education culture.



What do you mean by that?
Cynthia Carlton, University of Rochester

Is everyone speaking the same language around the table? It can feel silly to ask what something means; especially when that thing seems so simple and fundamental. “Everyone knows what this is!” you think to yourself. But do they? And is that meaning the same for everyone? In this session, we will discuss when and why you should consider defining terms, how to deal with competing contexts, the differences between a business glossary and a data dictionary and best practices you can take away and start using right away on your teams and projects.



When it comes to BI Tools, Is More Better?  A panel discussion on the pros and cons of using multiple BI tools within an institution
Dave Pecora, Rochester Institute of Technology

We will begin the panel discussion with a brief introduction of the topic (described above), followed by an introduction of each participant and their respective university.      Some of the questions we will explore in this panel discussion include:     1) Does your school use multiple BI tools?  What are the advantages and disadvantages of having multiple BI environments?    2) Are other schools ditching big-box BI vendors for tools like Tableau?  What are the driving factors for this?  What are the potential pitfalls?    3) Multiple environments are more costly to maintain.  When is this worth the cost?  When is it not?     4) Do multiple environments make BI and self-service analytics easier to adopt by allowing choices, or more difficult to adopt by making the environment more complex?    What other approaches are being used?  Reporting portals, other hybrid approaches, etc.    Ample time will be allowed to take questions from the audience, and to allow schools in attendance to participate in the discussion.