2016 Session Descriptions

These presentations are scheduled for the 2016 HEDW Conference in Rochester, NY.

The agenda, listing the times and locations for each presentation, will be available as the conference draws near.  Programs will begin at 8:00 a.m. on Monday, April 4 and conclude at 2:30 p.m. on Wednesday, April 6.

2015 HEDW Member Survey – Top 10 Issues (Hank Childers, University of Arizona and Aaron Walz, Purdue University)

Presentation and analysis of the results from the 2015 survey of HEDW members asking for the top 10 issues members are facing. What message(s) can we take from the top 10 selections? What do the comments say? Are there patterns related to geography, sector, or size? Are there clusters of responses that say something about the issues we are facing? How do these results fit with the 2014 maturity model survey results?

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.

A Practical Data-driven Approach to Achieving University Strategic Objectives (Greg Siino, California State University, Sacramento)

How do we utilize dashboards and data visualizations to drive goal attainment? In this session, a straightforward method for developing dashboards will be presented that focuses on measuring and achieving results rather than simply on reporting historical trends and demographics. The model includes a method for linking activity to results and breaking long-term goals into short-term strategies that can be measured over shorter durations. It also includes an approach to developing dashboards with cascading levels of detail and visualizations tailored to different roles within the organization. The session will include a short Tableau dashboard demo to illustrate the proposed approach.

Agile Data Warehousing in an Educational Context, Building a Data Warehouse with a Small Team (Michael Tantrum, WhereScape and Kevin McNamara, Colorado Christian University)

In this session learn how data warehouse automation through WhereScape enables small teams to build big data warehouses and deliver value back to your business users significantly quicker than traditional methods.  Colorado Christian University will share their results after selecting WhereScape to be a key technology in their new BI stack.  The speakers will discuss how automation through WhereScape embraces and facilitates agile development as well as highlight its deployment and documentation capabilities.

BI Adoption Strategies (Michael Hansen, Oregon State University)

Adoption rates of Enterprise Business Intelligence solutions vary by institution. Some early BI adopters experienced lower than anticipated adoption rates due to reporting system complexities, competition from parallel departmental BI implementations, or the inability to meet the users reporting needs. Oregon State University is in the third and final year of its BI Solution implementation. Throughout the 3-year implementation phase, OSU has experienced sustained growth in employee adoption of its BI Solution, from 25% to 35%. The continued growth is due in part to the user-centric development philosophy that places priority on providing value to the user.

Business Intelligence and Data Governance: Where do we go from here? (Dale Amburgey and Jessica Steinmann, Embry-Riddle Aeronautical University)

The dramatic increase of data requirements in today’s higher education landscape has led to the topic of Business Intelligence (BI) reaching critical mass in the Institutional Research profession. As many institutions are finding out, even the best BI efforts can be thwarted by a weak or non-existent data governance infrastructure. Follow the journey of one private institution as they journey towards enhancing data-driven decision-making by introducing the tenets of data governance and business intelligence in their organization.

Change Your BI Strategy: Deliver value sooner by increasing your agility (Mary Brooks, Miami University)

Is this your BI story? You had a plan worked out with your clients, you had all the requirements documented, you had a good development and testing team, and you used a top-notch BI tool. If so, why wasn’t the client thrilled with the results?, how did you miss so many data elements?, why was the team exhausted?, where did things go wrong? Find out how you can change your story and your strategy by increasing your agility project by project.

Come ABOR’d with Your Data (Tamara Noecker, University of Arizona)

The University of Arizona implemented a new business intelligence model for the Arizona Board of Regents (ABOR) which governs the three public universities. The project was designed around twelve primary metrics with targets set through the year 2025. Project goals included improvement of data visualizations, high availability, quick response time, more data at a granular level for internal comprehension, and transparency of key performance indicators to the citizens of Arizona and beyond.

Crowdsourcing Enrollment – How Watson and Social Media Can Help Us Detect Retention Trends (Yiorgos Marathias, Phytorion)

In this session we will evaluate and visualize social data from multiple channels to gauge student sentiment; we will then integrate this unstructured data with the data warehouse to better predict which students are in danger of dropping out.

Data Discovery to Drive Enrollment and Student Retention, John Grieco, Bryant & Stratton College

For more than 160 years, Bryant & Stratton College has helped students and working adults move up to a brighter future with a quality college education.  As a private, personalized college, Bryant & Stratton focuses on the success of the individual student and provides a supportive, friendly environment for higher learning.  Bryant & Stratton College will present how they use Qlik to increase their visibility into the student lifecycle for new student enrollment and retention.  Qlik has enabled the Bryant & Stratton team to better manage their efforts, properly allocate resources, and therefore better serve their students.  Bryant & Stratton leverage Qlik’s powerful analytics platform to discover data from the Banner Student Information System.

Data Driven Decision Dashboards (Diana Lindsley and Julius Moreland, Oregon State University)

Oregon State University has embraced data as a strategic planning tool. In support of the data driven decision making process at OSU, the Business Intelligence Center developed high level interactive dashboards designed specifically to support university administration decisions. The design basis for the Dashboards was to maximize the user experience through touchscreen technologies allowing user interaction for data discovery. The presentation will demonstrate the dashboards and examples of how the data and visualizations are being used for decision support. The second half of the presentation will dive into the weeds and discuss how the dashboards were designed and built.

Data Governance – Access and Security Challenges (August Freda, University of Notre Dame)

A key element of Data Governance is establishing agreement on requirements for security and access of information. In a BI and Enterprise Data Warehouse (EDW) environment, the concepts and process for security and access require a completely different approach. In traditional transaction systems, access for specific domains (student, faculty, staff) is typically for specific purposes and managed individually by the data stewards overseeing those domains. In the EDW, access to information may be for quite abstract needs and will generally span multiple domains under the oversight of multiple stewards. This presentation will outline an approach to these challenges.

Data Science for Student Success (Dev Nambi, University of Washington)

In this presentation, Dev Nambi will show how data warehousing teams can create predictions to further campus goals. You will learn how to discover patterns in student data, turn them into accurate predictions, and explain the results using data visualization. We will cover topics as varied as student retention, course demand, and enrollment predictions. This talk will focus on using machine learning, statistics, and data science to understand student behavior. Finally, we will show how the University of Washington’s Enterprise Data and Analytics team collaborates with campus groups to increase awareness and understanding of advanced analytics and its uses.

Data Virtualization for the Logical Data Warehouse at Indiana University (Julie Parmenter and Kyle Quass, Indiana University)

Logical Data Warehouses (LDW) are a new architecture for analytics which combines the strengths of traditional warehouses with alternative data access strategies. Data Virtualization is a critical component of the LDW architecture enabling queries to be federated across multiple data sources from both traditional data sources, such as data warehouses to less traditional sources, such as Web Services, while still appearing as a single ‘logical’ source. LDWs also become the central repository for security, business logic and metadata. Join us as we discuss our project to implement Denodo, a Data Virtualization specialist, as part of our newest BI architecture.

Data Visualization — Communication or Discovery? (Hank Childers, The University of Arizona)

Visualization of data has moved into prime time with the advent of newer tools and a changing sensibility about data presentation. Much of the accumulating body of practice is around the topic of effective and fair communication. But it is also an critically important tool for discovery. This presentation looks at its application in both modes with a number of examples, including those that focus on predictive analytics for student success.

Data Visualization in the Cloud or On-Premise: Performance Architects AHEAD Higher Education QuickStart Program for Oracle BI Cloud Service (BICS / OBI 12c) (Kirby Lunger, Performance Architects)

Oracle Business Intelligence (OBI) 12c is available both in the cloud and on-premise. Join the Performance Architects teams for a discussion and demonstration to learn more about data visualization capabilities in OBI 12c, and how you can jumpstart the use of these in your business through the launch of our “A Higher Education Analytics Dashboard” (AHEAD) Higher Education QuickStart Program for Oracle Business Intelligence Cloud Service (BICS).”  AHEAD includes rapid training, knowledge transfer and implementation of a predefined BICS analytic environment that provides government data from National Center for Education Statistics (NCES) and Council for Aid to Education (CAE).  This fast deployment offering takes advantage of the Performance Architects team’s experience with hundreds of Oracle implementations in the higher education industry both on-premise and in a hosted or cloud environment.

Deans and Dashboards: Guts or Glory? (Jeff Meteyer, University of Rochester)

With the increase in development and usage of dashboard technologies, everyone is interested in having their data displayed in an intuitive format in order to help facilitate decisions. But what do you do when the data to be presented does not meet the “expectations” of the owner? Hear the “dark side” of an honest attempt that went slightly south of target, and the individuals and their processes impacted.

Designing Data Structures for Analytics (Craig Rudick, University of Kentucky)

At the University of Kentucky, we are building data structures in our data warehouse that are specifically designed to answer key analytics questions. These data models typically re-structure and aggregate data in ways that facilitate analytics and ensure the generalizability and reproducibility of results produced by analysts across our campus data community. Audience members will better understand how we carry out open, transparent analytics; learn how we design data structures in the data warehouse to facilitate these analyses; and see examples of how this approach has led to better decision-making at UK.

Determining the Profitability of Academics, the Basis of Performance-Based Funding (Steve Letzring and Theresa Sherwood, Bowling Green State University)

How do you determine the true financial contribution of a section, department or college? BGSU sought to answer this question in support of its new performance based funding initiative. Working in concert with University Finance and Human Resources, the data warehouse team developed a subject area within the data warehouse that provides an analysis of gross and net revenue on a per unit basis for every class section offering as well as faculty salary and wages as they are applied to faculty course work on a per unit basis. This has enable the university to determine which sections, departments, and programs are paying for themselves and which are not.

Developing a Tableau Dashboard for Faculty Activity: Lessons in Collaborative Processes from Brown University (Rebecca Blum, Roland Hall, Kate Stepanova, Mary Heather Smith, Brown University)

In 2015, a small team of analysts located in different offices at Brown University were tasked with creating a data dashboard to track faculty activity. The intended audience was department chairs and University leadership. The metrics to be displayed combined data from appointments, demographics, instruction, sponsored research, facilities, and external benchmarking services. Over the course of many months, the dashboard team worked with the Business Intelligence team to bring together these siloed data sources for integrated analysis using Tableau Server. The presentation will focus on our approach, obstacles encountered, and lessons learned.

Dimension Extensions – Part 2, 2016 – Supporting User Defined Custom Hierarchies In Central Datamarts –  (Neil Belcher and Tim Pollard, Cornell University)

Part 1 in this series was presented at the 2015 HEDW conference.  Centrally defined hierarchies often do not meet the needs of college units. Units often spend a lot of effort downloading central data to local systems so they can add their own hierarchies. 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 the Dimension Extension approach that gives individual users 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 as the technical underpinnings.

Disruptive Trends Changing the Status Quo (Darren Catalano and Jack Neill, HelioCampus)

As technology and organizations evolve, data warehousing and business intelligence teams are changing to keep pace, take advantage of new technologies, and grow into greater roles within their organizations.  Data visualization, cloud computing, and evolving organizational structures are three major trends that are changing how we deliver insight and provide value to our organizations.  Explore these trends and more during this informative and interactive session

Education Analytics for Data Driven Decision Making (Dustin Ryan, Microsoft)

Data is everywhere. It’s coming from every device, web page, application and system in your organization. If your organization is like most organizations, drawing value from this wide variety of data sources to improve decision making is often a challenge.

  • How do you scale your applications to meet the data needs across your entire organization?
  • How do you integrate disparate, disconnected data sources?
  • How do you share the data with end users that need to access it?
  • How do you keep the data secure?
  • How do you use your institution’s data to improve teaching and learning?

In this upcoming session, we’ll address all of these questions, and show you how Microsoft’s data platform and reporting technologies can help your educational institution begin taking a data-driven approach to improve education analytics.

Evolution of data warehouses into Reporting-as–a-Service (RaaS): Implications for IR and IT collaboration (Jack Suess, University of Maryland, Baltimore County)

Data warehouses will continue to evolve into Reporting-as-a-Service (RaaS) and this will require closer collaboration of IT and IR. Increasingly, new entries, such as EAB, CIVITAS, PAR and Helio Campus, among others, are leveraging cloud technology to expedite modeling by transitioning data and reporting to the cloud. This talk will describe RaaS and discuss why this calls for new approaches to campus reporting and decision making, whether institutions outsource, insource, or use a combination in implementing this solution

Evolution of The University of Texas System Dashboard (Jeff White, University of Texas System)

Released in 2011, The University of Texas System Dashboard was recognized as a model for transparency and accountability of annual data. However, three years and multiple new requests later, the Dashboard began looking like a dinosaur… it was time to ‘evolve or die’. In 2014, the System worked with a Dashboard Advisory Group composed of UT Campuses and System Leadership on a year-long process to simplify the user experience while easing access to key data points. The new UT System Dashboard highlights these key metrics while providing context to help the user understand their relevance.

Getting Off the Ground: A Panel Discussion on Starting DW/BI Initiatives (Peter Wiley, Bowdoin College; Lisa Newman, Welleley College; Vanessa Brown, Ithaca College; Kevin McNamara, Colorado Christian University)

How does a college move from talking about a data warehousing/business intelligence initiative to actually implementing one? Hear from panelists at four institutions in the early stages of delivering a DW/BI solution share their experiences in launching these unwieldly, high-risk, and politically fraught projects. Our goal is to show different approaches as well as common themes to getting off the ground with these programs. Expect to hear about the successes and the pitfalls that could help others who are also young in their DW/BI efforts or just beginning to plan for such an initiative.

How do you report on double majors?: The process towards counting “hidden” students (Katarina Durasova and Michelle Stine, The Pennsylvania State University)

At Penn State, enrollment reporting has historically involved only the student’s first major of record. However, because students can have more than one major, colleges do not get a full picture of their supported students if they only use the first major of record. The presentation will focus on the history, process, and challenges of providing more complete enrollment and degree information. Access to this information will aid in better decision-making at the college and department levels. Examples of dashboards containing overviews of student, major and degree counts at the university and college levels will be included.

Implementing a System-Wide Data Warehouse with 16 Schools and 1 Annoyingly Stubborn Individual (Rachel Serrano, Appalachian State University and Elizabeth Reilley, University of North Carolina)

In 2014, the UNC system began implementing a central data warehouse to programmatically collect and store conformed data from each of its member institutions. This presentation will address both the central office experience and the experience of one of the member campuses.

Introduction of a Data Modeling Discipline for Data Warehouse design at the University of Minnesota (Mark Skweres, University of Minnesota)

In January of 2015 the University of Minnesota’s data warehouse had limited data definitions and a tedious, manual method for designing new data structures. This presentation describes the journey we’ve begun towards a model-driven methodology to build up an integrated repository of data definitions based upon approved business term glossaries. We’ll describe the benefits, challenges, tools and processes involved in this transition along with how we are weaving Data Governance processes into the modeling methodology.

Limitless Data in a Data Driven World: Implementing a Data Warehouse (Cassandra Elizondo, University of Utah)

As the focus of the University of Utah moved toward data driven decision-making, the availability of impactful data became an area of great interest and discussion, which led to the creation of the Student Data Warehouse. This session will discuss our road to implementation, including defining the stakeholders, the governance and guidelines, the training, and the reports created and future reports coming. As with any implementation, there were a few setbacks, but regardless of these deterrents, the system has moved forward, with much success.

Map Your Campus: How to Master Maps and Polygons in Tableau (Scott Lloyd, Brown University)

One of Tableau’s great strengths is the ability to generate graphic reports in geographic spaces, particularly cities, states, and countries. In Higher Ed, we also want to study our campus spaces. Unfortunately, mapping your campus in Tableau is a do-it-yourself affair with many pieces that are poorly documented. At Brown University, they have been successful in integrating ArcGIS building shape files with dynamic map servers to create a campus graphic reporting system using Tableau. In this presentation, facilities data guru Scott Lloyd will share the steps for creating your own campus map.

OLPP: Where the Warehouse Ends (David Vintinner, New York University)

Over the past two decades in Arts & Science at NYU, we built a web based information system (OASIS) around planning functions ranging from multiyear faculty recruitment to semesterly schedule planning. Our OASIS is not simply a shadow system, it has grown into a full-fledged On-Line Planning Processing System (OLPP), with its own data, security, and workflow. This presentation will introduce our theory of OLPP systems that reside between OLAP and OLTP technologies. It will demonstrate differences in OLPP from OLAP, and will be followed by discussion of situations when analytics might be preferable in one system over the other.

Project Management 101 for Data warehousing Projects (Insiyah Jamal, Drexel University)

Attend this session to learn about Project Management tools and techniques used to manage data warehousing projects at Drexel. This is a key but often over-looked aspect. This session use a few types of projects to go over project management methodology, templates used to track and communicate various aspects of the project, as well as discuss the common gotchas encountered. Working templates will be made available for download by attendees. The goal is for all participants to come away from the session armed with a good grounding in project management and a toolset to draw on help keep project elements on track.

Purdue’s Journey to Build a New Data Mart Environment (Amy Keene, Purdue University)

This session is Part 1 of 2 about Purdue’s journey to build a new data mart environment at the University that has generated excitement not only from the user community but all the way to the executive vice president level. This session is intended for anyone interested in how to approach a major BI initiative or simply pick up tips that can be incorporated into BI projects, major or minor.

Purdue’s Data Mart Environment – Part 2 – a Technical Perspecitive (Amy Keene, Purdue University)

This session is Part 2 of 2 about Purdue’s implementation of a new, highly-successful, data-mart-based BI environment as part of our increased focus on moving up the BI maturity scale. This session will delve into the technical details of the new data mart environment. This session is intended for anyone interested in how a major data mart environment is developed and operated and would like to glean out tips, tricks, or best practices that could be incorporated into your own solution.

Running a BI Shop: Magic, Start-Up or Joint Venture? (Anja Canfield-Budde, University of Washington and Aaron Walz, Purdue University)

Whether you are new to the field or a seasoned BI professional, there are some basics (and not so basics) you need to practice to run a successful analytics organization. In this talk, we will cover key concepts that will help you to build, manage and sustain your BI team. Taking the University of Washington and Purdue University as examples, this collaborative talk will share practical tips, from establishing your program, strategic planning, and building the organization to managing the work and marketing your products. The presentation will go beyond the experience of two colleges, by referencing industry best practice recommendations we have found helpful. After the talk, two separate working sessions will offer hands-on interactive opportunity to engage with selected topics covered in the presentation in more detail.

Self Service Business Intelligence: Panacea or Pandora’s Box? (Kristin Kennedy and Jennifer Wilken, Arizona State University)

Self-service business intelligence is perceived as the wave of the future. In this presentation we will reflect on the evolution of self-service business intelligence at Arizona State University. From the IT perspective, what does it mean and what fears and possibilities does it illicit? From a business user standpoint, what does it provoke and promise? Which fears have we found well founded, which have turned out to be myths, and which are we still deciding? We will share specific examples of perceptions and realities of self-service from a wide spectrum of constituencies, both technical and operational.

Self Service Reporting at Georgia State University (Kathy Bryant-Bonds, Georgia State University)

Georgia State University has deployed a robust enterprise data warehouse that contains most of the datasets needed by the university community. But what good is it to capture all of this data if the information is inaccessible to the end users? Come see one of the reasons that Georgia State has been able to create a culture where the numbers matter, by delivering data that matters into the hands of decision makers.

Snapshot in the Dark: Adding Dimensionality to Census Data after the Fact (Jonathan Havey, SUNY Buffalo)

Snapshot data tied to census dates is critical to offices of institutional research. Without it, volatile transactional data becomes a source of endless disagreement over numbers. However, snapshot/census data can be limiting as well. Oftentimes, there is rich dimensionality in our transactional systems that is not captured in census snapshots because it is not needed by the intended recipients. Given the many priorities being juggled when a new ERP is being developed, producing a serviceable census dataset represents a considerable challenge by itself. This presentation will cover various techniques employed at SUNY Buffalo for adding synchronized dimensionality to our official reporting data set.

So You’re Replacing a 30 Year Old Payroll System? – A Communication Strategy for Navigating Major System Change (Peter Visser, University of Washington)

The University of Washington is in the process of replacing its 30+ year-old HR/Payroll system with Workday, a modern human capital management system delivered via software-as-a-service. This multi-year effort touches every person and department at the University. UW’s Enterprise Data & Analytics unit has employed several communication techniques and processes to assist the EDW’s user population through the migration. This talk will spotlight how we leverage a new metadata tool, brown bag sessions, wiki pages, videos, one-on-one meetings, communication templates and more to help keep users informed and engaged through all phases of the migration process.

The Chief Data Officer role at Cornell University (Gregory Menzenski, Cornell University)

The Chief Data Officer is a new position in industry and higher ed. At Cornell, the CDO leads the Office of Data Architecture and Analytics team in the development of project implementation BI solutions and the support and maintenance of existing solutions and is charged with developing ODAA to become the recognized leader for university data analytics. In addition, ODAA will help leverage university data assets, solve unit data needs, formulate and promulgate best practices and develop a university wide data analyst community while also taking an active role in data governance and policy development. This presentation will describe the goals, objectives and associated challenges and to-date successes of the CDO role at Cornell University.

The Student Lifecycle – a powerful analytical model for IR, Admissions, Financial Aid, Student Affairs and Alumni Relations (Kurt Rosenfeld, Corporate Technologies)

Modeling the Student Lifecycle – from prospective applicant to eventual alumni – yields a fresh and powerful analytical solution for several critical areas of University administration.   In this session you’ll gain an understanding of a model that moves us away from base reporting (such as demographics) to look closer at student behaviors impacting admissions, progress to degree, student affairs, alumni relations and donations that fundamentally impact how the student experience is managed.  You’ll also learn about the approaches for integrating data across the different systems involved throughout the process (SIS, Advance, Slate, CareerLink, Qualtrics, Linkedin, etc), and see example Tableau dashboards.

Transforming Decision-Making: Point/Counterpoint (Joanne Wilhelm and Kyle Quass, Indiana University)

Join us and watch a scrum master and a data architect square off about the logical data warehouse, data virtualization, role-based access, a new portal, and the use of agile methods in Indiana University’s Decision Support Initiative (DSI). This ambitious initiative launched in mid-2015 to transform decision-making through the delivery of timely, relevant, and accurate information to decision makers. We’ll provide an update on progress in reinventing analytics and BI at Indiana University through both technology and culture changes, and we’ll challenge assumptions about what’s working and what’s not.

Using Statistics and Machine Learning to Predict Student Success (Monal Patel, Ian Pytlarz and Xi Zhang (Cecelia), Purdue University)

Most higher education institutions have implemented student success initiatives towards helping students succeed through their college career. Higher education institutions also are faced with limited resources for implementing student success initiatives. At Purdue, we are using statistical modelling and machine learning techniques to predict student outcomes before they happen, allowing Purdue to target student success resources at students that are most likely to perform poorly. Our presentation will focus on the methodology we used for building our models, the features of the data which ended up being most important to student success, how we plan to continue improving our models, and finally how Purdue is operationalizing these learnings.

Using Tableau to present the Data Digest (Monal Patel and Ian Pytlarz, Purdue University)

Many higher education institutions have some version of information about the institution’s enrollment, tuition, financial aid, faculty staff headcounts and more. At Purdue, this type of aggregate data was presented in pdf, excel sheets or HTML tables on various central department websites. With the purchase of Tableau server, Purdue has consolidated information into one concise website called the Data Digest (www.purdue.edu/datadigest). The various dashboards are presented on the first landing page as thumbnail images of the dashboard. This gives the user an instant visual depiction of the information. By clicking on the thumbnail, the user can explore certain information in greater detail.

When Harry met Sally or How to integrate Cognos and Tableau on your campus (Ted Bross, Princeton University)

This is a case study, describing how we integrated an enterprise version of Tableau with our traditional Cognos BI environment. The focus will be on defining the audiences and requirements and making sure that the right tool is being used to match the need. Issues related to security, data sources and user levels will be discussed.

When Institutional Research Leads the Data Warehouse: the Good, the Bad, and the Thorny (Libby Barlow, Syracuse University)

This presentation will make the case that institutional research (IR) must be the “owners” of their own data warehouse and go on to outline the sensitive points of contact between information technology (IT) and IR. Points of discussion include skills and tools needed to design and maintain a data warehouse that is useful for IR, whether each of those skills should reside in IR or IT, data governance, and shared resource models.

When you need help, Who do you call? (Steven Chang, Pima Community College and John Van Weeren and Kevin Meldorf, ASR Analytics, LLC)

Many Business Intelligence and Data Warehouse projects are led by internal institutional staff, often new to the field and the skills necessary for success. This can lead to a slow start while ramping up with training and project organization. It is also easier to fall into common traps that can lead to limited success or failure. Furthermore, when there are recommendations to be made, especially those that require significant resources, reorganization, or funding, the phrase “A prophet is not accepted in his own hometown” is never more  true. It can be difficult to overcome the inertia and campus politics that are inevitably encountered during these types of projects.

Learn how Pima CC went to RFP to find the expertise to accelerate their project and bring external perspective that added credibility to senior leadership and those approving resources. Find out what approaches and skills are best left to the experts at early stages of a BI and DW project to make progress and see quick results. Pima will share their rationale and process for going outside the institution as well as the differentiators that led to the selection of ASR Analytics to assist them with their large, multi-year data warehouse project.  Additionally, ASR will share their “Full-Circle BI” methodology and “Institutional Intelligence Framework” which contain the key components of People, Process, and Technology necessary to ensure project success.

Workday: Impact on EAP Architecture and Analytics (Phyllis Wykoff, Miami University)

Workday is becoming a part of the ERP Architecture at many schools. It’s  impact on BI and analytics is significant. This
session will be an opportunity for  institutions to share issues, solutions and to make connections with other schools facing the same challenges.