FSA Data Strategy

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(No Transcript)
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FSA Data Strategy
Paul Hill
Senior Technical
Advisor Federal Student Aid Keith Wilson Project
Manager for FSA Data Strategy Federal Student Aid
May 4, 2004
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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 among FSA and our
customers, partners, and compliance and oversight
organization.
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Data Strategy Purpose
The Right Data to the Right People at the Right
Time.

• Enterprise Standard for Student Identification
• Integrated Partner Management
• EnterpriseRouting ID
• Enterprise Access Management

• Integrated Student View
• Integrated School View
• Foundation for more Timely and Efficient
  Processing

• Consolidation of Data into Shared Source
• Focus on Data Quality

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Data Strategy in the Press
The Right Data to the Right People at the Right
Time.
From the January 2004 issue of The Greentree
Gazette
FSAs Data Strategy Initiative is likely to
have a significant impact on FSAs ability to
serve its customers. Its objectives include an
enterprise-wide policy for managing and storing
data and an industry-wide standard for
publication and dissemination. FSA Staff
commonly refers to the critical nature of
getting the right data to the right people as
the right time.
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Data Strategy Desire Outcomes
The Data Strategy defines FSAs enterprise data
vision and strategy for how it will combine
tools, techniques and processes to handle its
enterprise data needs.

• Cross-Program Integration
• Business objective gathering sessions comprised
  of cross-channel business owners and the
  establishment of Standard Identifiers for
  Students and Schools
• Improved Data Quality
• Through the execution of a Data Quality Mad Dog
  and the creation and execution of a Quality
  Assurance and Implementation Plan

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Data Strategy Desire Outcomes

• Improved Organization and Distribution of Data
• Creation of an XML Framework and Internal and
  External Data Exchange Strategy
• Establish a Data Storage Strategy
• Data Warehouse and Data Mart Strategy
• Plan for organizing data to answer broader,
  deeper business questions
• Establish a Target Vision for FSA
• Develop and refine a conceptual business and data
  architecture that outlines a Target State Vision
  for the FSA Enterprise

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Data Strategy Initiatives
Data Strategy has 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

Right Data
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Plain text bullets are initial Data Strategy
Scope. Italics represent Data Strategy 2.0 scope.
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Data Strategy Initiatives

• 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 and Operating
  Guidelines

Right People
Right Time
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Plain text bullets are initial Data Strategy
Scope. Italics represent Data Strategy 2.0 scope.
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Data Strategy Approach
Target State
Vision
Strategic Focus
Gather Business Objectives
Target State Draft
Target State Refinement
Data Strategy
Roadmap
Implementation Plan
Current State
As-Is
Data Quality Discussions
Data Strategy Team Findings And Input

• Gather Desired Outcomes and Current State
• Create and Refine the Target Vision to reflect
  Enterprise Data and Process Usage
• Facilitate Paradigm Shift from Current to
  Target State

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Current State Confirmation
Entity Flow
Current State Overview
Data Flow
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Evolution to the Target State Vision
A 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
FMS
Enterprise Shared
Functions
Students
Trading
Warehouse/Data Marts
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
Consolidate
Aid Eligibility
Aid
Determination
Person
CSB
Loans
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
FSA Integration Vision Framework
FSA Business Architecture
FSA Enterprise Target State
Business architecture drives technology solution.
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Data Integration Strategy
Determine overall Data Integration Strategy by
considering a Business Process and Data
Integration Continuum.
Common Data
Shared Source
Stand Alone
Common Access
Features
Common data and methods shared via Data Access
Objects (DAO) or Web service Single System
Integrated Application Suite and Data
Enterprise data, metadata, and business rules
integrated in one database Business process and
application workflow centrally controlled Operatio
nal Data Store (ODS) used to feed DW
Independent solutions No automated sharing of
information Sharing only possible through one-off
efforts to integrate for analysis research
Independent solutions Sharing of information for
analytical purposes in transactional system
acting as a warehouse
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Target State Vision
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Data Quality Mad Dog and Methodology
Improving Data Quality results in better
information enabling better decisions.

• Data Quality Mad Doghighlights the high
  priority data quality issues facing FSA data
  owners and users
• Data Quality Assurance Strategyplan for the
  continual improvement of FSA data quality and the
  maintenance and refinement of data across the
  enterprise
• Key Concepts
• Fewer data stores less redundancy
• Standardized Definitions and Terminology via XML
  Framework

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XML Framework

• FSA 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 The framework will
  define data standards, as XML Core Components,
  for data exchange to achieve consistent and
  accurate data.
• Data Cleanup and Maintenance Enable data
  cleanup and maintenance activities.
• Standard Data Tools and Processes Establish
  standard data tools and processes, to support
  consistently performed data/XML modeling.
• System Flexibility Provide system flexibility
  to simplify future interface changes and support
  new application and data exchange requirements,
  through XML-based data modeling.

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Standard Student Identification Method (SSIM)
The Standard Student Identification Method seeks
to establish a simple framework by which FSA and
Delivery Partners can consistently identify
students/borrowers across all phases of the
Student Aid Lifecycle.
Key Identification Problems in the Current
Environment

• Unique customer records can be inappropriately
  merged creating privacy concerns.
• A customers records cannot be linked
  appropriately preventing FSA from viewing data
  about a customer across all phases of the
  lifecycle.

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Standard Student Identification Method (SSIM)
Three-Pronged SSIM Solution Each part leverages
effective, proven identifier solutions already
being used in some parts of the FSA lifecycle.
Roll-out of these tools and processes
consistently shall tighten controls and improve
data integrity/consistency.

• Primary Identifier Verification with the Matching
  Algorithm The primary identifier is the Social
  Security Number, verified through enterprise-wide
  business rules and tolerances with additional
  data fields First Name, Date of Birth, and Last
  Name.
• Additional SSA Verification When new, unverified
  identities enter the FSA systems, it is
  appropriate to compare the record with the Social
  Security Administration.
• Consistent Correction Processing and Error
  Notification

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Routing ID (RID)
TPMS
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Enrollment Access Management (EAM)

• Reduce number of User IDs and passwords for web
  based applications (Single Sign-On).
• Provide tools to implement Web Services Security
  standards.
• Provide flexible authentication methods for web
  applications

• Manage security functions across environments and
  platforms
• Reduce the number of passwords (simplified
  sign-on)
• Provides self-service functions (registration,
  password reset, etc.)
• Allow delegated security administration of
  selected tasks
• Synchronizes passwords across multiple systems
  and platforms

Access Management
Enrollment
Access
Identity
Consolidated Data Collection
Control
Management
Data
RID provisioning
Collection
Enterprise User Administration
Web Authentication
Authentication
RID
Eligibility
Eligibility Approval Process
Enrollment information
Identity information, credentials, access rules
Approval
Delegated Administration
Authorization
Authorization

• Provide single management point for enrollment
  data
• Manage security at the enterprise level.
• Eliminate organization-specific or
  system-specific anomalies in user sign up or user
  permissions.

Trading Partner Administration
Trading
Partner
Audit
User provisioning and account configuration data
Eligibility and approval information
Enrollment Security Workflow

• Automate enrollment and access data flow between
  systems
• Route requests and manage approval processes

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Integrated Partner Management System (IPMS)
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Technical Strategies

• Internal Data Exchange
• The way in which internal systems transmit and
  receive data with one another, including the
  type/format of data exchanged
• Qualities and Features
• Improve business services accessibility
• Enable standards based access and communications

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Technical Strategies

• Web Usage (Portals)
• Customer experience and data exchange through the
  Students Portal, Schools Portal, Financial
  Partners Portal and other FSA websites
• Qualities and Features
• Access - The individual user groups ability to
  access various FSA websites and the login
  features that provide a unique user experience.
• Content Presentation The FSA websites patterns
  for design layout and navigational structure
  including the use of graphics, links, fonts and
  colors.
• Content Management The publication and
  distribution of web content including
  customization, personalization and search
  capabilities.
• Technical Architecture How the FSA supporting
  architecture enables web content delivery through
  Web Application Servers and facilitating Web
  Services.

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Technical Strategies

• Web Services
• Software components that use open standard
  communication protocols to interact with other
  applications over the Internet for service
  orientated architectures
• Qualities and Features
• Provide a straightforward, low entry cost
  mechanism for system-to-system interaction
  between trading partners
• Based on a set of industry standard protocols and
  technologies available on all platforms
• Support the reuse and extension of existing
  components/applications

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Technical Strategies

• External Data Exchange (FSA Gateway)
• The means by which FSA extends Enterprise data
  and business capabilities to trading partners
• Qualities and Features
• Extending FSA Enterprise data and business
  capabilities to the external community
• Provide single virtual entry point for exchanging
  data with external trading partners

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Technical Strategies
Data Storage, Management and Access The technical
components and business processes that define the
ability to collect, analyze, access and disburse
data Qualities and Features

• Data Architecture The technical enabler for
  data storage, management and access to enterprise
  information.
• Data Warehouses A collection of data designed
  to support enterprise data relationships. Data
  warehouses contain a wide variety of data that
  present a coherent picture of business conditions
  at a single point in time.
• Data Marts A database or collection of
  databases, designed that contain a slice of
  data for a specific business view or purpose.
• Data Mining Database applications that
  facilitate data investigation and pattern
  discovery.
• Data Analytics The process of analyzing
  different dimensions of data to facilitate
  forecasting and trend analysis.

Business Intelligence
Data Analytics
Data Mining
Data Warehouse
Data Architecture
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Data Strategy Key Findings To Date
The Data Strategy teams have 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.
• Verified that using a Matching Algorithm with
  SSN, First Name, Last Name, and DOB is the most
  flexible and tolerant way to identify customers.

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Data Strategy Key Findings To Date

• Need to develop an single Enterprise solution for
  all Trading Partner Identification and Access.
• As-Is Data Flow Discussions have facilitated a
  broader understanding of End-to-End Business
  Processes across all FSA program areas.

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Data Strategy 2.0
Where We Are

• Gathered Business Objectives
• Drafted Target Data Flows
• Created a Vision of What it should look like

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Data Strategy 2.0
What We Need To Do

• Explore options for new questions raised during
  Target Vision Discussions and Retreats.
• Implement XML Registry / Repository of Core
  Components to the Internet.
• Enact the Data Quality Assurance Methodology for
  the Enterprise.

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Data Strategy 2.0 Functional Gap Activities
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Data Strategy 2.0 Deployment Activities

• Deploy XML Registry / Repository to the Internet
• Makes FSA standardized Title IV Aid definitions
  and Core Components available for both FSA and
  Community usage.
• Provides a vehicle to drive consensus on data
  standards.

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Data Strategy 2.0 Schedule
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Contact Information

• We appreciate your feedback and comments. We can
  be reached at
• Paul Hill
• Phone [phone]
• Email Paul.Hill.Jr_at_ed.gov
• Keith Wilson
• Phone [phone]
• Email Keith.Wilson_at_ed.gov