No%20Data%20Left%20Behind:%20Federal%20Student%20Aid%20A%20Case%20History

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No Data Left Behind Federal Student Aid A Case
History

• Holly Hyland, Federal Student Aid
• Lisa Elliott, Federal Student Aid

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Agenda

• Federal Student Aid Overview
• Data Strategy Initiative
• Enterprise Data Management Program
• Description of the End Game
• Lessons Learned
• DAMA Impact
• Questions

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Who We Are

• The Higher Education Amendments of 1998
  established Federal Student Aid as the federal
  governments first Performance Based Organization
  (PBO) to modernize the delivery of the
  Departments Title IV federal student aid
  programs. As specified in the authorizing
  legislation, the purposes of the PBO are to
• Increase accountability
• Improve service to students and parents
• Integrate business processes and information
  systems
• Strengthen program integrity
• Reduce costs

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Who We Are

• As a PBO, Congress grants Federal Student Aid
  certain limited managerial flexibilities over its
  human capital management, budget and procurement
  activities
• Our focus is on
• Delivering world-class customer service
• Developing award-winning products and services
  that are of value to our customers
• Effectively managing the programs to ensure fair,
  effective and appropriate oversight, and service
  delivery at the lowest cost without sacrificing
  service levels or quality

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Our Mission

• Our federal grant and loan programs represent the
  largest sources of student aid in the United
  States, annually providing approximately 74B to
  more than 11M students/parents, including nearly
  13B in Pell Grants to the most needy students.
  We provide this aid through and with the nearly
  10,000 program participants.

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Our Critical Functions

• Federal Student Aid is responsible for a range of
  critical functions that include, among others
• Processing millions of student financial aid
  applications
• Disbursing billions of dollars in aid funds to
  students through schools
• Enforcing financial aid rules and regulations
• Partnering with schools, financial institutions
  and guaranty agencies to prevent fraud, waste and
  abuse
• Educating students and families about the process
  of obtaining aid
• Servicing millions of student loan accounts
• Securing repayment from borrowers who have
  defaulted on their loans
• Operating information technology systems and
  tools that manage billions in student aid dollars

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Our Environment

• FSA's technology environment is understandably
  complex
• 21 different information systems provide
  services.
• 140 internal exchange points across FSAs
  computing environment, and 175 external exchange
  points entering into our environment.
• 60 million FAFSAs processed a year 97 on-line
  applications
• Different identifiers are used to identify
  partners based upon particular system and type of
  business transaction.
• Multiple procedures required to enroll and
  register for access to FSA systems.
• FSA systems require different user credentials
  and enforce different policies using different
  User ID formats.

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Our Integration Challenge

• The Higher Education Act (HEA) legislation of
  1998 specifically called on Federal Student Aid
  to integrate, and defined three principal goals
  with respect to integration of Federal Student
  Aids information systems
• To integrate the information systems supporting
  the Federal student financial assistance
  programs.
• To implement an open, common, integrated system
  for the delivery of student financial assistance
  under Title IV.
• To develop and maintain a student financial
  assistance system that contains complete,
  accurate, and timely data to ensure program
  integrity.

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Enterprise Architecture
Business Architecture
Strategy PRM Alignment
BRM
Shared Business Processes
Information Architecture
Data Architecture
DRM
Shared Data
Technology Policies Standards And Products Guide
Governance (CPIC and Business / Technology
Alignment)
Application Architecture
Application Portfolio
SRM
Shared Services
Technology Architecture
Technology Inventories
Shared Infrastructure
TRM
Security Architecture
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FEA DRM Standardization Areas
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The Target State Vision (Architecture)
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Information Exchange
A single access point for online information and
services for Federal Student Aid customers,
partners, and the general public
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Enterprise Development Support Services (EDSS)
Model
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Getting to the Data

• 2000 Modernization Blueprint the wall
• 2003 Terry Shaw (former COO) the data is
  missing
• Representatives from all business areas vision
  without constraint to determine how things
  should work.
• Bumblebee Chart (Integration Vision Framework)
• Target State Vision (TSV)
• 2004 Data Strategy
• 2006 Enterprise Data Management

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Evolution to the Target State

• The Target State outlines the vision to achieve
  integration.

Enterprise Analytics and Research
Acquisition
Enterprise Performance
Case Tracking
Recommend
Analytics
Planning Strategy
Management
(Ombudsman)
Policy Changes
Audit
Credit Check
SSCR
Servicing Reporting (FFEL Campus Based)
Generate/Distribute ISIR/SAR
Send/Receive from Matching Agencies
Enablers
Transfer Monitoring
Process Promissory Notes
History
Common Data Architecture
NSLDS
Enterprise Shared
Functions
Enterprise Shared
Functions
FMS
Trading
Warehouse/Data Marts
Students
Partners
Transactions
Authentication Access Management
Edit Checks
Match Against CDA (FAH)
SSIM Logic
Partner Payment Calculation/PrePopulation
CDR
Computation Edits - EFC
Distribute Eligibility
RID Mappings
Application
Establish
Aid Eligibility
Consolidate
Aid
Person
CSB
Loans
Determination
Awareness
Record
Authentication
Access Tools
Application
Relationship
Process
Mgmt
Business
Intelligence Tools
Payment
Partner Payment Management
Partner Payment
State Agency
Processing
Admin
Funding
Ancillary Services
GL
External Financial
Process
Budgeting
AR Management
Accounting
Reporting
Payments
FMSS
GAPS
Business Function
External Transfer
Integration Vision Framework
Business Architecture
Enterprise Target State
Business Architecture Drives Technology Solution
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The Target State Vision
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Data Strategy Purpose
Develop an overall approach towards data to
ensure that accurate and consistent data is
available to and exchanged between Federal
Student Aid and our customers, partners, and
compliance and oversight organization.

• Get the Right Information
• To the Right Person
• At the Right Place and Time
• At the Right Cost

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Data Strategy Initiatives
Data Strategy evolved into the integration of
five core initiatives

• Data Framework
• As-Is and Target State Data Flows
• Refine Target State Vision
• Data Quality Mad Dog
• Develop Quality Assurance Strategy
• Implement Data Quality Assurance Strategy
• XML Framework
• Develop XML ISIR
• Develop XML Registry / Repository
• Production Deployment of XML Registry / Repository

• Common Identification
• Standard Student Identification Method
• Routing ID
• Trading Partner Enrollment and Access
• Enrollment and Access Management
• Technical Strategies
• Data Storage, Web Services, Web Usage, and FSA
  Gateway
• Web Consolidation Options
• Enterprise Analytics Architecture
• CDA Operating Guidelines

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Data Strategy Key Findings
The Data Strategy team confirmed several key
findings

• Data should be organized by business process, not
  by system.
• Providing data access to business experts is the
  key component of improving the enterprises
  ability to make informed business decisions.
• Need to develop a single enterprise solution for
  all trading partner/person identification and
  access.
• As-Is data flow discussions have facilitated a
  broader understanding of end-to-end business
  processes across all Federal Student Aid program
  areas.

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Benefits of the XML Framework to Data Quality
Federal Student Aid will use XML, via a single
set of enterprise and community standards, to
simplify and streamline data exchange across
postsecondary education.

• Benefits
• Data Exchange Standard Standardize FSAs data
  exchange using XML as the data exchange
  technology standard.
• Consistent Accurate Data Define data standards,
  as XML Core Components, for data exchange to
  achieve consistent and accurate data.
• Standard Data Tools and Processes Establish
  standard data tools and processes to support
  consistently performed data/XML modeling.
• Ease of Maintenance Simplify future interface
  changes, and support new application and data
  exchange requirements, through standardized
  XML-based data modeling.

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Enterprise Data Management

• Formalized October, 2006
• Building off of the work that weve been doing
  informally for the last four years with PESC and
  adding formal discipline with the Data Management
  Body of Knowledge (DMBOK)
• EDM is a service to the business with the
  following goals
• Support the improvement of enterprise analytics
  and
• Decrease the cost of and improve the quality of
  new development projects
• Focus on data as an enterprise asset.

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Enterprise Data Management

• Three year strategy
• Concentrate on fundamentals and
  foundation/communication materials.
• Start an Enterprise Data Governance Workgroup and
  improve and expand Metadata Management.
• Develop and implement Data Quality policies and
  procedures (started in year two).

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Target State EDM
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Data Policy and Strategic Planning

• Defines EDM strategic direction and promotes
  compliance with EDM policies, procedures and
  standards.
• Drafted of an EDM Strategic Plan.
• Developed an EDM Concept of Operations.
• Drafted Enterprise Data Policies.
• Developed EDM Language for inclusion into
  Contracts.
• Maintained EDM Business Case.
• Published Performance Metrics.

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EDM Monthly Status Report
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Data Governance

• Implements data governance processes to maintain
  standardized data definitions and associated
  metadata.
• Started two years ago with a Data Governance
  Pilot tackling Enterprise Address.
• Developed Data Standardization Policies and
  Procedures.
• Developed Data Dictionary Standards.
• Developed Data Governance Plan.
• Formed Enterprise Data Governance Workgroup.
• Business people agreed to learn basic to
  intermediate data modeling concepts.
• Joint development of data-related artifacts with
  business and technical staff.

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Data Governance Framework
Data Governance Management Structure

• Requires
• Collaboration
• Commitment
• Consistency

Executive Council
Escalation Path
Enterprise Data Management Program Management,
Quality Assurance
Strategic Steering Committee
Tactical Working Group
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Target State
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Metadata Management

• Uses metadata to guide, control and integrate
  data activities and products.
• Donated metadata registry, the XML Registry and
  Repository for the Education Community, to the
  Education Community of Interest.
• www.fsaxmlregistry.ed.gov
• Published as Open Source on SourceForge.
• http//sourceforge.net/projects/fsaxmlregistry/
• Classification scheme extends beyond that used by
  Federal Student Aid.
• Promotes use of common standards across the
  Education Community of Interest.

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XML Registry and Repository for the Education
Community
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Metadata Management

• Uses metadata to guide, control and integrate
  data activities and products.
• Developed Enterprise Metadata Inventory.
• Developing Master Data Management.
• Purchased and testing IBM Information Server.

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Data Architecture

• Promotes sharing of database assets, the use of
  an integrated architecture to support
  enterprise-wide data movement, access to common
  data, data transformation and migration.
• Developed an Enterprise Conceptual Data Model
  (signed off by the business areas).
• Developing Enterprise Logical Data Model.
• Data Modeling Standards and Procedures.
• Developed Naming Standards.
• Developed Data Model Registration Policies and
  Procedures.
• Developed Data Migration Roadmap A Best Practice
  Summary.
• Developed Data Synchronization Policies and
  Procedures.
• Researched Data Integration Services Best
  Practices and Recommendation.

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Data Quality

• Institutionalizes a set of repeatable processes
  to continuously monitor data and improve data
  accuracy, completeness, timeliness and relevance.
  
• Developed Enterprise Data Quality Scorecard
• Building upon previous work (Data Quality
  Assurance Strategy) to promote reuse
• Developing Data Quality Policies and Procedures
• Planning a Data Quality service for the for
  business areas (data profiling services, etc)

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Future Areas of Focus

• The US Department of Education
• Develop US Dept of ED Enterprise Conceptual Data
  Model created from the business areas.
• Form US Dept of ED Data Governance Workgroup.
• Encourage PK12 and Postsecondary data alignment.
• Encourage and contribute to PK 20 Data
  Standardization and Education Taxonomy.

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When Well Be Done

• Federal Student Aid is a proactive organization
  with sophisticated enterprise analytics that are
  used to inform Congress and help determine new
  policy.
• An Education Taxonomy is not a misunderstood
  word.
• Enterprise Data Artifacts are complete, of
  high-quality and used by the business areas
  often.
• Federal Student Aid owns its data and its
  organized by Business Capability Area, Business
  Function, and Data. This information is in the
  SOW for development projects and creates
  high-quality, lower cost development.

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Lessons Learned

• If its not in business (plain) language and/or
  its not clear how it supports the business EDM
  doesnt pay for it.
• True change takes time. Period.
• The technology is not the hard part the people
  part is the hard part. Have someone whos
  really, really good with the soft skills at the
  table.
• Luck is not a good business strategy.
• Little and often.
• It really does help when you have consistency of
  staff.
• Collaboration is so worth the time it takes.
• Business areas and Technologists can work
  together and develop really, really good work.

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DAMA Impact

• Changed CONOPS after researching DMBOK.
• Developed data definition standards after
  attending The Dictionary Heart of Data
  Quality.
• Felt better about EDM coming from a business area
  after attending Mastering Master Data.
• Prioritized Data Quality work from year three to
  year two after attending Introduction to Data
  Quality Tools and Technologies.
• Purchased Data Modeling Made Simple for members
  of Data Governance Workgroup.
• Changed data models after attending Make Your
  Data Model Diagram Really Communicate.

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Closing Remark

• As you can gather from our presentation, Federal
  Student Aids data work did not begin as a mature
  Enterprise Data Management Program in fact, it
  began in chaos. However, over time
• Opportunities were identified,
• Huge goals were set,
• Communities of interest were engaged
• Purpose was communicated
• Strategic decisions were made
• Crazy ideas were tried and evaluated
• Best practices were researched
• Mistakes were made and course corrections were
  implemented
• Baby steps were accomplished
• Everyone was heard and valued

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Questions

• We appreciate your feedback and comments.
• Holly Hyland Phone [phone]
• Email Holly.Hyland_at_ed.gov
• Lisa Elliott Phone [phone]
• Email Lisa.Elliott_at_ed.gov