Data Steward Meeting - New York University · Data Stewardship vs Data Custodianship Data Custodianship • Application / system knowledge; technical skills • Maintenance of data

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Institutional ResearchAnd Data Integrity

Data Steward Meeting

April 3, 2019


Agenda• Data Governance Consultant Report Updates

• Roles of Data Stewards and Data Custodians

• AIDA – Authoritative Institutional Data Assets

• Reporting and Information Redesign

• Feedback from Data Stewards

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DATA GOVERNANCE CONSULTANT REPORT UPDATES


Project Overview

• Assess current state of data governance at NYU

• Proposed Charter for Data Governance

• Proposed Target State for NYU Data Governance

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Report and Recommendations

Data Usage and Support

Governance

Executive

Vision, Priorities. Escalation

Stewardship Data Management & Architecture

IR IT

Policy development & coordination

Stewardship

Monitoring

Communications

Definitions and Standards

Access policies

Rules, regulations

Quality standards and rules

Tools

Architecture

Objective quality

Lineage and life cycle management

Metadata management

Access request and certification process

System integration and API requests

Role-based Access Control

Access decisions

Quality improvement and standards Identity and Access

Management

Target State Operating ModelOverview


Report and RecommendationsVision

“New York University’s data is treated as an enterprise‐wide asset and is readily available to support evidence‐based decision‐making and informed action.”

Goals

4. Establish a data governance program to improve NYU’s ability to create, preserve, and disseminate knowledge

1. Improve data quality to enhance trust in NYU data

2. Support easy and secure access to University data assets

3. Reduce risk through regulatory, policy and procedural compliance

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What Has Happened and Next Steps

• Executive Steering Committee has met, and is reviewing Charter document

• Institutional Research and NYU IT are coordinating more closely

• Beginning to reconstitute existing committees to form a Data User Task Force


Data User Task Force

• Expect to create three committees:– Student Data– HR and Finance Data– Faculty and Research Data

• Student Data User Task Force– Takes place of former UDW+ Student Operating

Committee and IR Counterparts

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• Task Forces will create a “Reporting Portfolio” which will include information on existing reports and methods of having questions answered.– Expect to structure it with the following attributes:

• Purpose (How many degrees were awarded in AY2018?)• Content (UDW+ Departmental Metrics Dashboard/Degrees

Conferred Report)• Audience (Internal: Deans and Department Chairs)

(continued)



• Role of the Task Force– Serve as prioritization review and escalation point for

new requests– Communicate existence of, and then new content in, the

Request Portfolio to respective schools and administrative units

– Raise through the Data Governance structure disagreements about who should answer a question or decisions on new investments in reporting needs

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THE ROLE OF DATA STEWARDS AND DATA CUSTODIANS

Data Governance Roles and Responsibilities

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• Vision and Mission• Alignment with Corporate Objectives• Implementation Plan

• Arbiters and Escalation points• Stakeholders• Roles and Responsibilities • Data Stewards• Decision Rights• Accountability and Ownership

• Important Data Policies• Important Control Processes

• Statistics and Analysis• Tracking of progress• Monitoring of issues• Continuous Improvement• Score‐carding

• Data Quality• Data Sharing• Data Architecture• Data Security• Metadata Mgt.• Data Mastering

• Training and Awareness• Mass – Wiki or Intranet• Individual Updates• Pertinent, specific and timely• Maintain interest and commitment

Data Governance Categories used for assessment

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• Data Stewardship is the role that supports the development of the policies, practices, processes, and standards that an enterprise uses to organize its data and information. Also, data stewards guide the data custodians in the implementation of the policies.

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Common Data Steward Activities

In the context of their defined subject area domains, data stewards will:

• Define / describe business data elements

• Define data domain values

• Establish and validate data quality rules

• Identify and help resolve data quality issues

• Help develop data domain business rules (algorithms, calculations, processing requirements, etc.)

• Define data security requirements

• Promote the use of accepted data definitions, common reference data and sound data usage practices

• Create and maintain business metadata definitions, domain values, business rules, etc.

• Represent subject area for the University, Schools or Functional Unit

• Provide accountability for subject area data, ensuring timely and consistent collection of data

• Guide and advise others on meaning and use of data

• Determine retention period of data

• Provide data understanding and usage expertise to projects (development and enhancement)

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• Data Custodianship: The role in an organization that is responsible for entering and maintaining the correct data values in an application based on the requirements provided by the data stewards.

• Data Custodians also verify that the metadata for the data under their control is maintained accurately, according to standards.

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Data Governance vs Data Stewardship

Data Governance

• Policies

• Practices

• Standards

• Enterprise focus

• Enterprise communication and socialization

Data Stewardship

• Domain content knowledge

• Domain development / implementation of data governance policies, standards, practices

• Domain implementation of data governance procedures

• Socialization of data governance in domain

• Data and metadata quality standards development for domain

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Data Stewardship vs Data Custodianship

Data Custodianship • Application / system

knowledge; technical skills • Maintenance of data and

metadata according to data governance policies, standards, practices

• Adherence to data governance procedures provided by data stewards

• Support for data governance program and data stewards in application activities

• Data and metadata quality control

Data Stewardship • Domain content knowledge • Domain development /

implementation of data governance policies, standards, practices

• Domain implementation of data governance procedures

• Socialization of data governance in domain

• Data and metadata quality standards development for domain

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Design standard documents and forms

Data Governance Program team creates standard documents/forms/processes that will support the data governance activities

• Document/form examples: change control, data catalog, business glossary etc.

• Forms/documents will be examined regularly and updated as necessary with version control

• Central storage of forms/documents will be established and information will be shared with the data stewardship community during the next quarterly meeting

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Sample Data Catalog Format (work in progress)

● Provide a central store to organize both business and technical metadata● A searchable interface is planned for end of FY19, as part of the AIDA project

Source: Data Catalog - Identity

Source: Source Data Mapping - Identity20

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AIDA – AUTHORITATIVE INSTITUTIONAL DATA ASSETS

Presentation Subtitle Arial 24pt

AIDA (Authoritative Integrated Data Assets)People, Space, Academic Program Hubs - Update04/03/2019

Presenter Name: Adrian Kulpa

Project Manager: Huey-Chih Lee

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Data governance [is] the specification of decision rights and an accountability framework to ensure appropriate behavior in the valuation, creation, storage, access, use, archiving and deletion of authoritative data assets.

AIDA

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The project formerly known as Master Data Management (MDM) has been renamed to Authoritative Institutional Data Assets (AIDA).

AIDA will provide and maintain an authoritative, trusted, shared, governed and reusable set of common data assets.

Planned Timeline

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Oct 18 Nov 18 Dec 18 Jan 19 Feb 19 Mar 19 Apr 19 May 19 Jun 19 Jul 19 Aug 19

Establish AIDAFoundation

Prepare for POC

Conduct POC

Select Vendor

AIDA Phase 2

• On-Board Team

• Stakeholder Analysis

• Develop RACI

• Target Operating

Model

• Vendor Assessment

Criteria

• Define AIDA POC Data

Sources

• Develop Canonical Model

• Create Request for

Proposal (RFP)

• Prepare Data for POC

• Finalize Vendor Short List

• Communication Plan

• Align with University Data

Governance

• Distribute RFP to Vendors

• Conduct Vendor Q&A

• Establish Data Landing Zone

• Perform POC Demos

• Create People Data Hub

Business Requirement

• Identify Hosting Options

On-board Vendor

• Create Student Hub

• Setup Testing and Production Environments

• Update Business Requirements

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Student Life Cycle

1.1 Potential Applicant

1.2 Applicant 1.3 Admitted 1.4 Matriculated

Student Inquiry Application

Admissions Decision

Financial Aid Decision

Register for Courses

Sign up for Student Activities(Athletics, Club, Service, Leadership)

1.5 Enrolled

Apply for Housing/Dining

Complete Coursework

Add Academic Enrichment (Study Away, Research)

Utilize Student Resources (Health, Wellness, Career)

Final Records Submitted

Graduate/Earn Credential*

Undergraduate life cycle only*Students may not complete every milestone (e.g. graduation, housing, student activities)

Plan Selection Tuition Deposit or Waiver

Student Acceptance or

Decline

1.6 Alumni

WORK IN PROGRESS

Goals● Understand product offerings in the marketplace● Assess product capabilities and alignments to NYU

Status● Engaged vendors in the MDM space via RFP● Developed use cases and supporting data● Execute vendor POCs and summarize findings

Next● Exhibit select vendor demonstrations to broader community

Vendor Solution Proof of Concept

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OUTPUT FROM AIDA

INPUT FOR AIDA AIDA

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Appendix: AIDA - Under The Hood

Internal SourcesInternal and External

Consumers

External/Other Sources

InternalIng

esti

on

Dis

trib

uti

on

Data Store

Warehouse

Advanced Analytical Apps

Tableau

3rd Party Data (USPS & Others)

Social Media

Other External

Mobile Apps

Business Systems

Administrative Units ..

SIS

ADVANCE

Student

Alumni

Employee

Space/Facility

Authoritative Cleansed Data Storage

Supporting Data Sets

Supporting Data Sets

Validations and Rules

Create Authoritative SourceCreate Authoritative Source

AP

I AP

I

Data Management Interface

Deduplicating - Match Merge SurvivorshipDeduplicating - Match Merge Survivorship

Cleansing - Apply Data Quality RulesCleansing - Apply Data Quality Rules

WORKDAY

SAILPOINT

STUDENT CLUBS

PROCESSINPUT OUTPUT

Provides Data Steward Control Over Dataset

Reporting and Analytics Consumers

Students Prospects Alum Etc. Consumers

Data Steward User Interface

Schools

CSAKS

EnrichEnrich


REPORTING AND INFORMATION DELIVERY REDESIGN

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NYU Reporting and Information Delivery Redesign

Peter Sheehan, Decision Support GroupUniversity Data Stewards April 3, 2019

• Simplify, clarify and consolidate reporting

• Promote analytics and data oriented analysis

• Provide user friendly, intuitive content

• Enable the application of new tools, including in-application reporting

and self-service

Objectives

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• Finance and HR reporting• UDW+• FAME• Workday• i-Buy

Scope

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Three work streams:

FY2019 – Analysis and Planning

FY2020 – Improve, streamline, retire or consolidate Workday and UDW+ reports / FAME / i-Buy

FY2021 – Self-service reporting and curated data sets

Timeline

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Roles and Responsibilities (in progress):

• Customers – Fiscal Officers, Finance and Budget Analysts, any user of NYU financial reporting• Executive Sponsor – Stephanie Pianka• Steering Committee – Beth Davidovich, Angela Chen, Peter Christensen• Project Management - Huey-Chih Lee• Governing Body – Fiscal Officers • Project Team – Designees as appointed by fiscal officers, UDW+ team, NYU IT EDM, DSG, ESM, FSM

Who?

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Why?

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• Focused attention for issue resolution

• Smaller catalog of UDW+ standard reports

• Increased flexibility of information delivery

• Take full advantage of in-system reporting capabilities

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Next steps

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• Finalize project governance

• Coordinate “Reporting Design Team”

• Retire UDW+ reports with very light use

• Seek opportunities to retire / consolidate

• Develop detailed work plan

• Evaluate systems for reporting strengths

Questions?

Questions?

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Thanks for your time!

Thanks

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FEEDBACK FROM DATA STEWARDS

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Your Feedback

• What are the main challenges you face when you perform data stewardship activities?

• What information would be helpful for the Data Governance Offices to share with you?

• What are the best communication methods to use between quarterly Data Steward meetings?


WRAP-UP

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Next Meeting

• Tuesday, July 16, 2019: 10:00 – 11:30am


Thank You!

Documents

Data Steward Meeting - New York University · Data Stewardship vs Data Custodianship Data Custodianship • Application / system knowledge; technical skills • Maintenance of data