WATITIS 2016

Panel Session: The Road Ahead: IT Renewal at Waterloo

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Over the past few years, a number of key departments have taken a hard look at the services they offer and the processes that support them. An improved computing and information systems environment is often a cornerstone in moving the yardsticks. As a result, the university has entered an unprecedented period of IT renewal in these areas. In this panel session, there will be an opportunity to hear from leaders in some of these departments about what they hope to accomplish and the projects either underway or coming.


Research: John Thompson
Finance: Corinne Krauss, Associate Director Audit and Communications
Advancement: Sean Thomas, Associate VP Advancement
Registrar's Office: Ray Darling, Registrar
Co-op & Career Action: Diane Eisler
HR: Kimberly Snage, Director Operations

What renewal is happening on campus right now?

Research: “Gateway systems”. 4 projects.

Finance: working on replacement for 1996 system, by “Unit 4”

Advancement: existing is patched together. working on replacement, cloud vendor.

Registrar’s Office: 147 projects. clearly rest of campus using other software

Co-op & Career Action: new app coming.

HR: new recruitment system, pension system. drive self-service, avoid frustration

success criteria?

Research: iterative. faculty consultation.

Finance: reduce paper, processing times of manual processes; try out self-service; transparency.

Advancement: reduce amount of times on separate systems; change-management;

Registrar’s Office: self-service improvements, eg., academic advisement. peoplesoft is good.

Co-op & Career Action: launch in a week, first goal; integration with CECA web apps; engaged stakeholders, self-service for employers (none of these overnight). “value add” activities

HR: reduce paper, enhanced self-service and manager service, on metrics

goals post-project?

- no notes. many buzzwords.

How often do you meet? integration projects?

Research: Unit Four, HR, yes, interactive

Finance:

Advancement:

Registrar’s Office: keen on where Colin Bell is going, with data warehouse; program appraisal reports- will require data from elsewhere. APIs into an IST database (not Quest). OAT.

Co-op & Career Action: via IST “this is something we’re good at” ??? reporting database by February, hopefully.

HR:

features that vendors haven’t supplied?

Research:

Finance:

Advancement:

Registrar’s Office: “de-customization” because we were early peoplesoft adopters, but later versions have important features.

Co-op & Career Action:

HR:

Learnings from Change Management?

Research:

Finance:

Advancement: pacing: some want change, vs. some workers have been doing things for 20 years. bring them onto the team for them to learn. who’s it for? someone who shops on amazon, used to web.

Registrar’s Office:

Co-op & Career Action:

HR: stakeholders. appetite for change across campus? diverse needs. collaboration.

Automating Big Data Cleaning: An Example Using Local Bibliometric Data

Jana Caron, Manager, IAP, Evaluation and Accountability and Shannon Gordon, Bibliometrics, Library

The University of Waterloo recognizes bibliometric data as an important piece of evidence-based research assessment, and recommends bibliometric data as one measure, among many, for capturing research productivity trends, and elements of research impact. Even when working from a basket of measures, bibliometric data remains complex and requires significant cleaning due to issues of name ambiguity. This session will explore an innovative collaboration between the Library and Institutional Analysis and Planning (IAP) to support the integrity of local, discipline-level bibliometric data by automating key data processes of an internal project. This session will introduce how bibliometric data is relevant to the University, the process used to gather and vet local bibliometric data, and the ways in which key data processes have been successfully automated using Python and a database to support efficient reporting. Given known challenges presented by name ambiguity, this collaborative framework makes it possible to support the integrity of local bibliometric data - a key step in supporting this and similar in-demand analyses at the University.

big data: 600 authors, ~25k publications, ~350k citations, 7 research areas -> 200 bibliometric data points [photo]

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“bibliometric data points” = summary for academic output. Research productivity.

— 9 measures by description [photo]

white paper on measuring research output through bibliometrics

NOT individual researchers- research institutes/groups.

InCites / Web of Science

input data workflow

good partners, IAP and Wilson Hsu, PhD student, CS (contract, 25 hours, python, overqualified…)

data problems (q: error-checking results of automated processes?)

documentation:

significant spot checking - re-allocated 200 hours saved, to doing lots of checking

data integrity analysis - did 2016 automated look like 2015? yes.

process documentation - who will be involved years from now?

29 data dictionaries: template

inclusion/exclusion criteria; time frames, data available, source; primary data owner;…

Where next?

tweaking python scripts (library programmer)

web of science & insites APIs

author experience- —> web UI for authors to review their lists (‘international databases…’)

bibliometrics data warehouse on campus? Might happen?

application to other discipline-level analysis

barriers?

identifying experience on campus

finding capacity to support automation needs

Which 6 sources?

university funded

IQC, nanotech, water institute, aging network, Repel population health, in year 2, “wise institute”

google scholar: not fine-grained enough. only interested in publications affiliated with particular 6 groups.

leverage: web of science, scopus, google scholar

other campuses? some have dedicated staff.

“orchid” ID for researchers.

out-of-scope: drilling down to produce metrics on particular researchers; or journal-level disciplines (these were interdisciplinary).

Data Centre Swap with McMaster

jbg: off-site archive with McMaster.

To achieve disaster recovery targets, institutions require geographic distance between data centres. While exploring options for the University of Waterloo, a discussion at a conference indicated McMaster had the same motivations. In this session Jason will outline the purpose, agreements, and process used to swap data centre space between the University of Waterloo and McMaster University.

reliable 3rd copy of data offsite (>40km) for >12 months; secure & simple; test without impacting live data

- but want <80km for ease of access.

options:

tapes: too much volume

co-lo space: space isn’t the most expensive part; it’s bandwidth (since we have cheap bandwidth)- commercial implications: $100k+ for commercial)

cloud

another university or MUSH (municipality, university, school, hospital)

concerns:

data confidentiality, [ transit: IPSec VPN; Rest: Encryption + Keys@UW ]

drives are encrypted; on top of that, keys necessary.

ownership (custody/control; “our data”)- vs cloud providers, mushy

access & availability.

agreements: July 2016.

timeline: 2010: trials; 2013 initial discussions with other universities., probably OUCC Guelph/York; costing Mar 2014; mcmaster visit; July 2016 we install; Dec 2016 McMaster install

20TB/month avg transit, +/-5TB

Used: 130TB; projected April 2017: 250TB

$260k cost over 3 years.

Cloud: cost structure: amazon: S3, glacier. $100k-250k/year - going down 5% or so a year? might become cost-effective eventually.

What’s backed up? WCMS, netapp. anyone buys data, VMs. Not email.

disaster at UW, lose keys? off-site copy.

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Strategies on Cloud and SaaS

Cloud computing is driving change in how IT services are delivered. The Department and Campus Applications (DCA) team, Enterprise Systems, IST has partnered with several campus groups on a number of SaaS projects to provide maximum business value while minimizing potential risks. The focus will be on DCA's emerging and adaptive roles.

Jason Greatrex

notredame moving 80% IT services into the cloud by end of 2017

“Department and Campus Application” - looking for groups on campus with insufficient IT; guide them through finding, implementing, supporting IT systems for them.

“how not to build a minimum viable product”

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they offer business analysis and project analysis: “little b and light p approach”

Do Less, Accomplish More: Strategies for Dealing with Capacity and Demand

IT is ubiquitous at the University - every new initiative or business improvement comes with its own app, feature request, and website. Demand for IT services continues to grow while capacity and resources remains the same, or even shrinks. To succeed, IT managers need to take a step back and rethink their service model. IST's web development group has learned to focus on what matters by standardizing and simplifying services, slowing down, and listening to its clients. This session will highlight strategies to effectively manage capacity and demand, including how to determine your average velocity and stick to it, manage expectations, communicate priorities and timelines, and use advisory and steering committees, as well as feedback systems, to help guide your work through client-driven decision making.

Andrew McAlorum

WCMS is changing how they handle feature requests; some old requests are five years old.

They aren't accepting new feature requests to work through the backlog.

Using Agile methodology, including "user stories" and "story points."

the more ambiguous the user story, the higher the estimated story point

make stories as granular as possible, which result in more accurate estimates.

Estimating task complexity: if you have a 6-10, can it be broken down into smaller items?

aim for 1-4.

“planning poker”

facilitator reads user story and notes.

team asks questions;

everyone votes (sticky notes or notes app)

if there are wide differences, discuss

vote again, repeat until consensus by team.

“Velocity” = measure of team’s rate of progress. summing up number of story points that were completed in a given time. So you can estimate future progress.

Release Plan: for high-risk, high-uncertainty projects with a firm end-date, leave a couple sprints at the end to account for: scope increase, challenges, poor team performance.

create buffers around high-risk projects

https://hbr.org/2016/01/estimate-the-cost-of-a-meeting-with-this-calculator

80/20 : Fridays- people can work on whatever they want, as long as it’s somewhat related to day-job

Monday- team gets together and demo what they came up with on Friday.

Work is meant to be fun; people are expert in their areas; trust them.

Ransomware

Cybercriminals have sought to make money in many ways by attacking our computer systems. Recent years have seen a rise in popularity of ransomware - software that takes computers and data hostage. Victims are told to pay up, or face crippling losses.

In this presentation, Terry will trace the growth of the ransomware industry. He will give examples of the software used in attacks and offer some countermeasures that can prevent or mitigate ransomware attacks.

Terry Labach

We watched him infect a windows machine with ransomware; what it looks like and how quickly it propagates. Unofficial recommendation: if you see it, hard-shutdown ASAP; move the disk to another machine for recovery.

Plenery: IT: Servant or Master?

A discussion of whether information technology is truly serving the needs of society, or whether it is arbitrarily dictating social norms and practices. While IT can empower and inform millions of individuals with remarkable speed, it can also provide an unprecedented array of tools to manipulate and monitor those same individuals. Advertising, after all, funds much of the internet's information services. But perhaps these are early days, and IT's transformative potential is yet to be realized. Perhaps IT's glory years await us.

Video: https://livestream.com/itmsstudio/events/6729713/videos/143683955

-- DanielAllen - 2016-12-08

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