Category: Technology

Presenters

• Michael Kubit, VP and CIO, The Pennsylvania State University
• Jennifer Stedelin, Senior Director for Strategic Operations, The Pennsylvania State University

 

Resources

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The Case(s) for Change

Changing Landscape: students of the future, financial trends (reduction/elimination of state funding), faculty trends (retirements), research funding trends, future of learning.

1. Penn State deserves a world-class IT org commensurate with its status as a leading R1 institution.
2. IT must reinvent its funding model and business processes to adapt to financial and demographic realities.
3. The speed of change in the current Fourth Industrial Revolution is disrupting every industry.
4. The IT workforce must grow and transform to keep pace with changes in society, education and society
5. Penn State data and research are more vulnerable than ever, due to the frequency and scope of cyber threats.
6. We have a fiduciary responsibility to fully optimize and invest in a cohesive strategy for IT services and support.

Key Issue: Innovation in face of growing financial strain

Cannot fund innovation with new dollars, so we need to think about ways to use the university’s funding more wisely. One way we do that is to think about moving IT from a provider to an enabler. We had a faculty member come in to share what IT means to her as a professor: she uses 35 apps in her classroom that the university does not provide or support. IT needs to have a new set of (mostly) soft skills that we need to bring to the fore to support our staff, faculty and students.

Aligning to Institutional Mission

The majority of our work is about changing lives and making the world a better place. How do we at IT professionals share how we contribute to these goals? We’re a differentiator and allow the institution do things with IT other organizations cannot do yet. Penn State has nearly 2,000 IT pros across 80 IT orgs. As a result of this size and spread, we’re over-invested in commodity IT. We had a large number of focus groups which found that a) people liked their local tech staff and b) recognized the value of being connected with central IT, and c) discovered that we have no consistent approach on funding of IT across the institution.

“Bright Spots” are areas of the organization that are doing things very well, that we’d like to see spread and become a part of how we do things day in and day out. Our rollout of O365 allowed us to consolidate 36 e-mail systems into one. This project had three components which led to success: executive support; advisory groups consisting of faculty, staff, and leadership; and a community of IT pros engaged in the project throughout…including a number of volunteers from across the university that came together as a community to migrate their departments. Those 36 email systems were run by the equivalent of 32 FTEs, and the new system is run by 5 FTEs, at an avoidance cost savings of $600K/year to the university.

Reimagining

You may have noticed we are avoiding the issue of centralization; our efforts are more about using institutional resources more effectively (i.e. running commodity hardware is not a strategic benefit to the university).

Vision: align, optimize, create.

How do we become an IT organization that’s all about people? We’ve tried to think about the people that we serve; we’re trying to embrace a culture of service to people; and that we’re all in this together…everyone has a role, everyone has a share in the responsibility. It’s a journey, not a destination.

This plan needs to be navigated through the university relationship by relationship, to help folk understand what changes mean to them. Quick wins can demonstrate what’s possible; an IT summit shared what we’re planning with our stakeholders across the university and we asked for their feedback. We’ve committed to open communication as much as possible: I conduct a Zoom seminar every month that allows participants to ask questions directly.

We want to leapfrog rather than remediate wherever possible.

Question: what are the top initiatives? We’re building out a planning process to go in-depth with some of our units (volunteer units first). We want to discover the services they deliver, the faculty and staff they serve, their spend, etc. We’re writing up discovery methodologies to help facilitate this process. Identification of commodity IT services is a big part of this.

Question: how does central IT staff respond to this process? We’re not centralizing, but rationalizing processes across the organization. Many de-centralized organizations are in fact doing some processes better than our central IT unit. We want to emphasize that these changes are going to impact the entire organization.

This is the first session of the last day of the conference at 8:00 AM, so if anything below is unclear or muddled, it was NOT the presenter, but me!

Presenter

• Mojgan Amini, Director Process Management & Continuous Improvement, IT Services, University of California, San Diego

Resources

• http://becop.ucsd.edu
• http://leanbench.ucsd.edu

Process Improvement Movement Engulfs UCSD

Backstory: Goal #5 of UCSD’s strategic plan is: “Creating an agile, sustainable, and supportive infrastructure by ensuring a dedication to service, people, and financial stewardship.” Various forms of the word “efficient” are used 11 times to further explain the goal.

• Translation: be nimble, do more with less.
• That means any savings in time or money gets directly reinvested back to the university’s core mission.

Continuous Improvement Mindset

• Lean Six Sigma (LSS) is all about eliminating waste and reducing defects.
• Process driven, data centric and people oriented approach to achieving business goals.
• Over 1,600 LSS practitioners at UCSD who have earned Yellow, Green and Black belts.

Every new employee in IT gets registered for LSS training.

Momentum was Building

• Community to bring together change management, continuous improvement and project management.
• Major ERP effort with process optimization at its core.
• Investment in LSS training and significant outcomes of LSS projects was increasing.

Harness the Collective Knowledge

We had the skillset, the interest and the need. All that was left was a way to bring LSS practitioners together. So we created an event called “Process Palooza,” a three-in-one event which included:

• Process improvement competition – aka The Great LSS Race
• LSS Learning tracks for everyone
• Networking and community building

We weren’t quite sure what to expect, but we ended up with 500 people show up to this event! Whiteboards and collaboration, no laptops. Opportunities for networking and making connections included networking lounges and booths for departments and workgroups to showcase their projects, provide info, or ask for help

Real Processes, Real Results

The competition element of Process Palooza realized amazing results:

• AS travel request process: $96K savings, 41% reduction in processing time, 50% reduction of rework, 50% reduction of advising time, increased customer satisfaction by 80%, increased process understanding by 100%.
• Transportation Services new employee commuted: By implementing only a small number of improvements, achieved $105,000 in labor savings, 30% reduction of in-person procession, Full implementation of improvements slated for Fall 2018.

Business Excellence Community of Practice

A vehicle to carry forward the movement and continue to grow a continuous improvement mindset and our professionals. Benefits to university include: COPs can help you scale; create a support network for members, uniting change management, project management, continuous improvement and more.

• Started in Fall 2017
• Executive Board and Committes
• Over 170 Members
• Monthly events
• Mailing list and Slack channel
• http://becop.ucsd.edu

Enterprise System Renewal

Our LSS process is reshaping the way UCSD does business. This includes: space management, financials, document management, admissions, identity management, HR/payroll, faculty review, data & analytics, research admin, student information, degree progress.

Lean Bench: software matters, but processes take center stage. We crowd sourced process improvement talent; a select set of campus expert practitioners of LSS methodology who engage and lead crucial efforts around process improvement, operational excellence, and business efficiency.

We track and measure all implementations and store this information in one place.

 

I attended this session because I knew it would include a lot of useful and practical information, and the presenters are really good at what they do 🙂 As I type this, people are filtering into the session…it doesn’t look like it’ll be a standing room only session, but it will be well-attended. The speakers moved through their materials quickly, and conversation was pretty lively…any errors or omissions are entirely my own!

Presenters

• Timothy Chester, VP for IT, University of Georgia
• Jonathan Gagliardi, Associate Director, American Council on Education (ACE)
• Gina Johnson, Assistant Executive Director for Partnerships & Membership, Association for Institutional Research (AIR)

 

 

 

 

 

 

Gina framed the session from the perspective of collaborations between IT and IR departments and breaking down silos between different units. She also challenged us to stick around and not bolt for the door, because the session will include collaborating with neighbors 😉 She then talked a bit about collaborations between AIR and EDUCAUSE.

Agenda

• Areas for collaboration
• Tools for developing collaboration
• Discussion & Q&A
• Final Thoughts

The higher ed community needs to get on the same page about using data effectively! An American College Presidents and AACRAO/ACE study showed that:

• Very few college presidents identified the use of IR/evidence as an area of future importance
• People from the same campus held different perspectives on data access, quality, and future use
• Yet, areas considered to be important all require a strong research and evidence base

Jonathan Gagliardi on his role & perspectives about using data

Higher ed in the US is facing unprecedented challenges to improve access and attainment despite stagnant or diminished funding. We can use data to uncover and fix structural issues we have.

• Things are getting more expensive, and students are footing more of the bill.
• Not a lot of huge gains in graduation rates (based on Federal numbers)

These forces have eroded the public confidence in higher education.

Many campuses face challenges related to data use: data quality, accessibility, accuracy; data use not as widespread as it should be; data are siloed; lack of resources for training and professional development; resource limitations.

Spectrum of analytics

The following aspects of the spectrum of analytics should be done continuously and collaboratively:

Descriptive > Diagnostic > Real Time > Predictive > Prescriptive

Data need to be timely, accurate, integrated, and relevant regardless of the nature of the analytics being used. One of these dimensions may be prioritized over the other, depending on the insight needed.

“Analytics is a translation function”

Maturation of analytics functions requires an elevated cross-divisional model that takes into consideration many important factors:

• Culture & politics
• Technical infrastructure
• Resources
• The IR/IT nexus

We need a strong relationship with IT to make all this happen! IR and IT should strategically collaborate.

Timothy Chester on Areas of Collaboration

• 30% is technical, 70% is about people!
• IT vs. IR legacy silos and structures need to go

What do we need to do when we get a seat at the table?

From Build to Buy to Buy AND Build

IT focus historically (1960s – 1980s) was to buy and build systems, but over time those systems were highly layered. Later, (1990s – 2010s) our focus was to buy systems from vendors that we’d have to “bolt on to.” Latest phase (2010s – present) is composing systems, i.e. we buy pieces that we need from vendors and then spend time integrating them. IT and IR are in a great place now to coordinate.

Business Process Management: Discover, Document, Improve, Implement. BPM depends on using data effectively. Bringing units together and reducing redundancy will help your internal processes.

Data governance is a big piece of the puzzle as well. Discover > Define > Apply > Measure & Monitor. This process is an ongoing effort.

Having a seat at the table is having an opportunity to influence decision-making. Trust, credibility, respect and personal relationships are more important than hierarchical influence!

The Credibility Cycle

Rinse & repeat the following:

1. Initial Credibility
2. Resources & Expectations
3. Outcomes
4. Results

Get poor results, then you get into

1. Reduced Credibility
2. Cycle of Overcommitment & Underperformance
3. Diminished Authority

The more and better you are at “blocking and tackling,” the more credible you’re going to be with building effective relationships and being successful.

The Best Teams are Diverse Teams

Every good team needs people who are good at the following:

• Generate ideas
• Promote ideas
• Fulfill ideas
• Validate ideas

IT/IR are becoming business organizations…we need to grow beyond just technical expertise and into a strategic advocate for change. Your job is more than to just drop data off at the table, you need to provide candid advice on the issues of the day. Senior leaders want advocacy based on analysis.

Questions for Discussion

1. Using the duties & functions of IR to orient yourself to the focus of IR – What are some areas of crossover in work between iT and IR (Common KSAs)
2. What are some areas of clear delineation between the work of IT and IR (unique KSAs)?

The session broke up into groups to discuss these questions and then shared with the larger group.

Arrived about 20 minutes early to Thursday’s keynote. The Bellco theater is filling up more slowly than yesterday…maybe folk were out late last night? It’s about 7:59 to the presentation as I type these words, so I guess it’s just gonna be fewer people in the hall, which will make leaving a little easier. I’m keen to hear this presentation, as the technology we use is NOT a neutral force in the world.

Presenter

• Teddy Benson, Director of Data Integration, Walt Disney World, Parks and Resorts

Started with a quick informal poll of audience members who are studying or working in the field of AI (it was not a huge number of folk). Question: How many think that bias, in general, is bad? A mix of half-asleep hands go in the air, most attendees abstaining.

What is AI?

To Teddy (as a youngster), AI was exemplified by C-3PO and R2-D2 from the Star Wars movie. It’s borrowing characteristics from human intelligence and applying them as algorithms in a computer friendly way. machine learning is a subset of AI, Deep Learning is a subset of machine learning. AI started in 1952 with a study by the Navy to build a neural-net computer made by Frank Rosenblatt. Neural nets are layered which allow for nuanced creative problem-solving. AI has become popular in the last 10 years because of theoretical algorithms made cheaply available by cloud computing such as Azure and AWS.

Today what we use for AI is known as narrow or weak – AI designed to perform a narrow task (e.g. facial recognition, internet searches, driving a car).

In the future we may see the creation of AGI – Artificial General Intelligence. Designed to successfully perform any intellectual task that a human can do.

Passive AI: search auto-fill, delivery of targeted ads, entertainment recommendations, product recommendations, etc.

Active AI: IBM’s Watson info retrieval, knowledge representation, automated reasoning; Salesforce’s Einstein smart CRM; Amazon’s Alexa virtual assistant; Microsoft’s Cortana personal digital assistant; etc.

AI Types

Symbolic AI: rules and knowledge has to be hand-coded and human-readable.

Non-Symbolic AI: lets the pre-generated model to perform calculations on it’s own. Downside: it’s a black box…hard to understand what’s happening inside the AI and/or the model.

Where are we in the evolution?

• Type 1: Reactive machines (chess playing)
• Type 2: Limited memory (self driving cars)
• Type 3: Theory of mind (understanding the world)
• Type 4: Self-awareness (understanding consciousness)

What is Bias?

Attaching of positive or negative meaning to elements in our environment based on personal or societal influence that shape our thinking.

Conscious bias is to be aware, intentional and responsive.

Unconscious bias, on the other hand, refers to being unaware of bias being introduced into the system.

What is AI Bias?

When a machine is biased, it is unable or less able to adapt to various training models, preferring one route as a primary mechanism.

The makes the developed AI algorithm rigid and inflexible, unable to adjust when a variation is created in the data at hand. It is also unable to pick up on discreet complexities that define a particular data set.

As our use of AI continues to grow the problem of bias in AI will continue to be very pervasive and not yet even fully realized.

Google image search (circa 2012) on “What is a CEO?” shows that only 11% of company CEOs are women, when the actual number was about 27%. A similar search on “What is a telemarketer?” the opposite effect was true (50% versus actual of 64%).

• Was it the model causing the bias?
• Was it the data that the model was given?

In 2016, Microsoft released Tay.ai to the world and after 24 hours, was reduced to spouting racist and other awful statements. What caused this chatbot to become a xenophobic Nazi-lover? LEARNED BEHAVIOR. Tay learned its behavior based on the people who were tweeting at it – taking its conversational cues from the WWW. Given that the internet can (and often is) at times a massive verbal garbage firehose of the worst parts of humanity, it is no surprise that Tay began to take on those characteristics.

Word2Vec tries to find associations to words via vectors in interconnected space and ties them together. With language translation, this theory supposes you can take the cascading vectors to translate languages. Google’s translation of articles online fails at this due to vector bias. Vector bias, data influence/inference: nuance in languages are not weighted appropriately in vector space.

Types of Bias

• Data-driven: missing or one side data set
• Bias through interaction: learned behavior
• Emergent bias: providing matching data to requested data
• Similarity bias: matching like for like
• Conflicting goal bias: stereotyping results

How to Prevent AI Bias?

• Know your environment
• Know your data and where it came from
• Verify your model logic – code reviews
• Spot check during training of models