Academic Profile Project (APP)
April, 2013
Agenda
• Introductions
• Project Background, Goals, Objectives
• Project Organization, Roles and
Responsibilities
• Enterprise Information Stewardship on APP
• System Conceptual Design
• Status
• Questions, Issues, Your Thoughts
Project Background, Goals,
Objectives
Project Goals
•
The Academic Profile Project will involve a single college (the College of Agriculture
and Natural Resources) using the Digital Measures faculty profiling tool to
implement an instance of this application in a manner consistent with the broader
data and functional goals required by the University at large. These goals are to:
– Improve institutional processes for developing and recording faculty
accomplishments
– Provide a single institutional system of record for faculty professional activities
– Develop a data governance/stewardship program for these data at an
institutional level
– Comply with the demand by external reporting agencies (e.g., Star Metrics,
SciENCV) for shared researcher profiling
– Leverage faculty accomplishment and activity data collected internally with
external requirements
– Facilitate the identification and connection of faculty expertise
– Discover relevant collaboration opportunities for faculty and researchers
– Promote the accomplishments of faculty both internally and externally
Time Frame
• The time frame for the entire pilot project is
approximately two years (one year for pilot and
one year for CANR implementation). At the end
of the first year, pilot results may be evaluated to
determine the feasibility of expanding the college
scope beyond CANR.
• Because of the stated goal (that the application
will be implemented in a manner consistent with
the broader data and functional goals of the
University at large), other colleges will be brought
into the pilot in an advisory mode.
Problem/Opportunity Definition
• In fall, 2012 the APP development team completed an extensive
Request for Proposal process for a faculty profiling tool which was
inconclusive. There is no one tool that meets all institutional
requirements; furthermore, the market space for such tools
continues to be highly volatile, with functional enhancements
either being developed, or in design…but not actually ready to
deploy.
• Working with CANR creates a significant value-driven opportunity
given their experience with the Digital Measures tool and its
implementation in the College over the last 5 years.
• It is expected that the CANR experience with the tool will greatly
facilitate the functional and data decisions that will be required to
expand the view to be more representative of the University as a
whole (without losing those facets that are unique to them).
Project Urgency
• The elements of urgency which precipitated the APP RFP project are still
present (and most certainly apply to the pilot with CANR). In fact, the
sooner we embark on the pilot, the sooner we will be able to evaluate
the efficacy of the tool (for an institutional implementation) in an effort
to satisfy the following:
– The drive by the federal funding agencies and the Office of Science and
Technology Policy (OSTP) to develop SciENCV and Star Metrics
– Other institutions are developing or licensing tools to address this expectation
as well as meet internal needs. This, in turn, is driving the development of
commercial products that can address the federal funding agencies’
expectations for reporting.
– Certain MSU departments and colleges are already seeking their own
solutions to the problems outlined above, and are securing or developing their
own profiling software.
– The URC project (to be unveiled in the near future) will provide a web site for
showcasing corridor institution faculty accomplishments, and providing
expertise searches. MSU will want to insure the most complete compilation of
accomplishments for its faculty.
Proposed Solution
• Engage CANR over a 2 year time frame as a pilot partner to implement a
version of the Digital Measures, Activity Insights application that supports
not only CANR needs, but those of MSU as a whole. The pilot APP
development team would bring resources to the project in an effort to:
– Provide data from multiple internal sources (e.g., HR, SIS, CLIFMS, etc.) and
data from external sources (e.g., publications from Scopus Experts) using
automated interfaces;
– Permit entry of new elements (that to this point have not been captured in
institutional systems of record, some of which will be mandated by federal
reporting agencies such as the relationship of a publication to a specific grant);
– Facilitate functions and processes such as annual review and review for
promotion and tenure;
– Produce a faculty CV;
– Provide various kinds of reports (operational, management, status, dashboard
metrics, etc.);
– Produce data sets in formats that can be exported to other internal systems
(e.g., CLIFMS) and external agencies (e.g., Star Metrics, SciVal Experts).
Business Requirements; Project Focus
•
Collect full faculty accomplishment data (including as much history as we have)
from CANR faculty using a variety of methods:
– Creating imports from institutional systems (SIS, CLIFMS, Contracts and Grants, HR, etc.)
– Creating imports from external systems, primarily for publications (SciVal Experts,
Scopus publications)
– Converting and ingesting data that may already have been entered in the existing CANR
system (first determining the appropriate system of record for institutional purposes)
– Manually entering data
•
Extract data for use in other internal or external systems; for example, professional
accomplishment data for CLIFMS (which will obviate the need for faculty and/or
department staff to classify, and enumerate these data themselves) and SciVal.
We may soon be required to extract and format faculty accomplishment data to
comply with federal requirements, and must be prepared to do so.
Objectives for Project
• Collect full faculty accomplishment data (including as much history as we
have) from CANR faculty using a variety of methods:
– Creating imports from institutional systems (SIS, CLIFMS, Contracts and Grants, HR, etc.)
– Creating imports from external systems, primarily for publications (SciVal Experts, Scopus
publications)
– Converting and ingesting data that may already have been entered in the existing CANR
system (first determining the appropriate system of record for institutional purposes)
– Manually entering data
– Entering data from CV’s using a partially automated scraper/parser
• Extract profile data for use in other internal or external systems;
for example, professional accomplishment data for CLIFMS (which will obviate the need
for faculty and/or department staff to classify, and enumerate these data themselves)
and SciVal. We may soon be required to extract and format faculty accomplishment
data to comply with federal requirements, and must be prepared to do so.
• Develop reports that can be used in support of annual review,
RPT, and other departmental and college administrative functions.
Project Organization, Roles and
Responsibilities
Project Organizational Structure
Project Directors: Mary
Black, Estelle McGroarty
Advisory Group (meets
every 2 weeks)
Project Directors
EIS
Academic HR
Project Manager
CANR
Med School Rep
Social Science Rep
Nat Sci Dept Rep
Technical Infrastructure
and Integration
Project Manager:
R. Cotter
Digital Measures
Working Group (2
x’s per week)
Project Mgr
OPB DRA
Tech. Resources
Technical
Infrastructure &
Integration
Data Stewardship Working
Group (meets every 2
weeks)
EIS Rep
Project Manager
OPB Rep
OPB DRA
HR Rep
CANR Rep
2 College Reps
Members of Working Group
(as needed given the issue
to be discussed)
Roles and Responsibilities: Project
Directors (Estelle McGroarty & Mary Black)
• Communicate with lead stake holders regarding
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Personnel Resources
Funding
Project Scope and Objectives
Project Status
Escalation of Issues (if necessary)
• Review and approve project plan (tasks, dates, priorities,
dependencies)
• Review and approve project deliverables
• Facilitate issue articulation and resolution
• Organize and chair Advisory Group
• Participate in Data Governance Group (as needed)
• Participate in Working Group (as needed)
Roles and Responsibilities: Project
Manager (R. Cotter)
• Develop project plan (scope, tasks, resources, dependencies, dates)
• Meet weekly with Project Directors & Data Resource Administrator
to review status
• Liaison with Digital Measures
– Insuring DM project plan is in sync with MSU’s objectives for the pilot,
and that appropriate progress is being made
– Providing lead communication and specification regarding system
requirements, elements and attributes, reports, interfaces, etc.
• Organize and facilitate working group (meeting twice per week, or
as needed to keep project pace)
• Provide direction to data governance group: develop agenda, meet
with data governance group, identify issues, recommend
alternatives
• Review working group member tasks, identify issues, facilitate
resolution
Roles and Responsibilities: (Tech) Working
Group
• Responsible for the work plan tasks (system analysis, design
and development; data analysis; functional and business
analysis; process mapping; system testing and usability, etc.)
• Understand the dependencies of the work plan in relationship
to their tasks; provide feedback to project manager with
regard to status and/or issues
• Will meet twice per week to review status, discuss tasks,
surface & discuss issues
• Provide guidance and/or bring issues to Data Governance and
Advisory groups
Roles and Responsibilities: Advisory Group
• Members represent data and functional requirements
of their disciplinary area, while insuring the needs of
the university community as a whole are met.
• Reviews project objectives, scope, tasks, timeline and
status.
• Reviews and approves project deliverables (system
design and documentation, system forms, reports, and
other deliverables)
• Facilitates resolution of, and provides guidance on
issues brought to them by working group and/or data
governance group.
Enterprise Information
Stewardship on APP
MSU Enterprise Information
Stewardship defined
• Definition:
A framework in which key information resources are managed in a
way similar to other university resources, with care and intention.
These information resources include data, definitions and related
technology infrastructure.
• Information Stewardship will be deployed across the
university.
• Information Stewardship will be an on-going initiative.
• Why Stewardship instead of Governance in an
educational institution?
• Data Stewardship at MSU may incorporate Data
Governance when required.
Data Stewardship defined further
• Brings together IT (data) and business
(governance) to foster understanding of
the data that drives the business.
• Facilitates the formation and integration
of information resources.
• Provides the framework for crossfunctional decision-making.
• Ensures that Enterprise data meets the
strategic and operational needs of the
enterprise.
Governance vs. Stewardship
• Data Governance: The execution and
enforcement of authority over the
management of data assets and the
performance of data functions.
• Data Stewardship: The formalization of
accountability for the management of data
resources.
Benefits Of Information Stewardship /
Governance
• Improve data-based decision-making
– Provide consistent and rationalized data definitions
– Establish and maintain central repositories for Meta and
Master data
– Enable consistent reporting across business units
– Provide data quality metrics and support service level
agreements
– Security protocols that are consistently carried out and are
based on policy, guidelines, laws and audits.
How is EIS facilitating this change?
• EIS is an apolitical facilitator to resolve data issues, and
will not drive decisions to a predetermined business or
academic end.
• EIS establishes processes and structures that help MSU
arrive at consensus about:
– Data definitions
– Data structure (data elements, code values to use, data
relationships, etc.)
– Which are authoritative source systems
– Policies for how we create, update, delete and use these
data
Initial Deliverables of Data
Stewardship
 Some things EIS has done collaboratively with
functional and technical stakeholders:
 Developed data flow diagrams of all current and
future processes (take a “data centric” view; tell us
how the data flow across the enterprise, and
where they land);
 Developed conceptual, logical and physical data
models
 Built and maintain prototype Metadata repository
and collaboration web site
 Participated in system configurations with focus
on master data entities
Dimensions of APP Data Governance
• Insuring Information Usefulness By Providing:
– Tools
– Definitions
– A repeatable process for the on-going coordination of shared data that
doesn’t end when the project ends!
• Defining and Providing for Data Security Through:
– Access Policies
– Access Management
– Best Practices
• Seeking Better Data Reliability Using:
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Project Plans and Data Models
Business Rules
Data Quality Metrics
Audit
EIS/APP “Working” Group
• Define and manage the extract, translation and load (ETL)
requirements for conversions and interfaces
• Define and manage the ETL requirements for the data
warehouse
• Facilitate and finalize the data structure of "critical" entities
• Provide oversight in the development of user access
profiles (roles/templates) insuring that they reflect the
laws, policies, regulations etc. that apply.
• Review and recommend the data confidentiality
classifications
• Review and recommend functional and technical data
definitions
APP Advisory Group and Data
Stewardship
• Review, approve and advocate policies governing
access (roles, permissions, content based access).
(Data Security)
• Review, approve and advocate data usage
practices intended to ensure that the data assets
of the institution and the individual are not
misused or abused. (Data Security & Audits)
• Where the group is not able to resolve
definitional or other differences, the issue is
defined and routed to the Advisory Group.
• Review and approve the APP before “go-live”.
Focus on Data Quality early and often
• Understand your customer’s problems and
offer real solutions.
• Implement tools and policies that ensure high
quality data.
• Establish Service Level Agreements with the
stakeholders.
• Implement dashboard metrics that monitor
data quality and utilization.
APP Conceptual Model
Status and Next Steps
Status and Next Steps
• Finalized committee membership, other project resources; meeting
regularly
• Contract finalized w/Digital Measures
• Developed Project Plan; monitoring progress
• Completed Process Mapping (for critical processes---Building and
Maintaining the Profile; Annual Review; Review for Promotion and Tenure)
• Completed Data Flow Diagrams (identifies data sources, data feeds,
update requirements, data at rest)
• Initiated Digital Measures instance, loaded with pilot users, base user
data, personal and contact data
• Working through updates to prioritized Digital Measures screens and data
feeds from MSU systems (more than 50% complete)
• Expect “beta” pilot group to begin accessing the system in late May, early
June with remainder over the course of the summer as more data are
loaded
High Level Schedule Overview
•
PHASE I
– Vendor Start Up: 12/1/2012-1/1/2013
– Initiate Working, Data Governance, Advisory Groups: November/December
– Conduct Process Mapping
– Conduct Initial Data Requirement Review
– Phase 1 is complete
•
PHASE II
– Data Scope Phasing Determination: 12/1/2012-1/1/2013
– Technical Design & Development of interfaces & Unit Testing, Conversion of CANR data,
manual data entry: 1/1/2013 – 6/1/2013
•
PHASE III
– Roll Out Planning, Implementation, Training: 6/1/2013 – 8/15/2013
– Evaluation of Pilot 9/1/2013 – 12/1/2013
– Decision as to future direction: 1/1/2014
– Initiate next steps based on decision: 2/1/2014-12/1/2014 (continue w/CANR
implementation, add colleges, look for another tool, etc.)
Questions?