Enterprise Data Management Enables Unique Device Identification (UDI)

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The First Step in EIM
Enterprise Data Management Enables
Unique Device Identification (UDI)

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Agenda
Purpose:
Create Understanding of how Enterprise Data
Management can assist in the requirement to
comply with UDI Regulation
An understanding of:
  Data components of UDI
  Enterprise Data Management’s role in UDI
  How to get started
  How to ensuring success
Outcome:

Current Data Challenges
• Analysis of adverse event reports is limited by the fact that the specific devices
involved in an incident are often not known with the required degree of specificity
Lack of a common vocabulary for reporting and enhanced electronic tracking
abilities
• An UDI will enable the FDA and manufacturers to better identify potential problems
or device defects, and improve patient care
Lack of a reliable and consistent identification of medical devices limits
safety surveillance
• Sometimes it is difficult to identify these products.
Issue with counterfeit products in the market

UDI Requirements
•  In the most basic format, the UDI would be a coded
number registered with standards organizations, and
would incorporate a variety of information, including
(but not limited to):
— Manufacturer of the device
— Model of the device
— Expiry dates
— The make
— Any special attributes that the device may possess
Compliance with the UDI Regulation will be mandatory. All
manufacturers of medical devices will be required to comply with
the new UDI methodology

UDI Benefits
•  Reducing medical errors
•  Reporting and assessing device-related
adverse events and product problems
•  Improve product recall, tracking and tracing
•  Standardized identifier defined
•  Efficient traceability
•  Efficient product authentication
•  Less documentation
•  Supply chain efficiency
•  Improve order and invoice process
•  Optimized receiving
•  Increase productivity
•  Improve shelf management
Benefits

[ ENTERPRISE DATA MANAGEMENT ]

Enterprise Information
Management Framework
Provides a holistic view of data in order to manage data as a corporate asset
Enterprise Information Management
Information Strategy
Architecture and Technology Enablement
Content Delivery
Business Intelligence
and Performance
Management
Data Management
Information Asset
Management
GOVERNANCE
ORGANIZATIONAL ALIGNMENT
Content Management

Develop and execute architectures, policies and procedures to manage the full data lifecycle
Enterprise Data Management
Enterprise Data Management
Ensure data is available, accurate, complete and secure
Traditional
& Big Data
Governance
Data Quality
Management
Data Architecture
Data
Retention/Archiving
Master Data
Management
Big Data
Management
Metadata
Management
Reference Data
Management
Privacy/Security
Enterprise Data Management is the foundation to UDI compliance. EDM
ensures data that underlies an organization is available, accurate, complete,
and secure. Architectures, policies, practices, and procedures that manage the
full data lifecycle are developed and executed

[ DATA GOVERNANCE ]

Why Data Governance?
•  Data Governance can play a supportive role in UDI
compliance. Having a unique, consistent, and
persistent entity identification is one of the first
steps in managing data assets.
•  Data Governance can drive the adoption of data
standards e.g. GS1 within your organization.
•  By setting up a data governance organization,
Healthcare value chain stakeholders, including
device manufacturers, distributors and healthcare
providers will benefit immensely

Data Governance Definition
Data Governance is the organizing
framework for establishing strategy,
objectives and policy for effectively
managing corporate data.
It consists of the processes, policies,
organization and technologies required
to manage and ensure the availability,
usability, integrity, consistency, audit
ability and security of your data.
Communication
Data
Strategy
Data Policies
and Processes
Data
Standards
and
Modeling
A Data Governance Program consists of the
inter-workings of strategy, standards,
policies and communication.

Data Governance Framework
•  Vision & Mission
•  Objectives & Goals
•  Alignment with Corporate
Objectives
•  Alignment with Business
Strategy
•  Guiding Principles
•  Statistics and Analysis
•  Tracking of progress
•  Monitoring of issues
•  Continuous Improvement
•  Score-carding
•  Policies & Rules
•  Processes
•  Controls
•  Data Standards & Definitions
•  Metadata, Taxonomy,
Cataloging, and Classification
•  Operating Model
•  Arbiters & Escalation points
•  Data Governance
Organization Members
•  Roles and Responsibilities
•  Data Ownership &
Accountability
•  Collaboration & Information
Life Cycle Tools
•  Data Mastering & Sharing
•  Data Architecture & Security
•  Data Quality & Stewardship
Workflow
•  Metadata Repository
•  Communication Plan
•  Mass Communication
•  Individual Updates
•  Mechanisms
•  Training Strategy
•  Business Impact & Readiness
•  IT Operations & Readiness
•  Training & Awareness
•  Stakeholder Management & Communication
•  Defining Ownership & Accountability
Change
Management

Data Governance Benefits
•  Governing and managing product data changes
•  Managing item attributes and relationships and product
catalogs
•  Defining and approving new items
•  Establishing a repeatable data quality management program
that ensures data accuracy, completeness & auditability
•  Use history and audit trails for security and proof of
compliance
•  Fully documenting data flow processes and their
transformations allows changes and transformations on the
data to be audited and traced back to the original source and
format
•  Fully documenting business and IT processes provides an
integrated view of data assets
•  Increasing efficiencies and effectiveness to enable better
decision-making throughout the health care value chain
•  Developing a common understanding of data management, data
classification data security, and access to and appropriate
usage of data
Benefits

[ DATA QUALITY ]

Why Data Quality?
•  Data quality management provides reliable data that
satisfies the business functions and technical
requirements of the enterprise to meet UDI
compliance
•  A data quality management program that ensures
accuracy, completeness, auditability and traceability
of UDI data. This ensures that UDI data has high
quality stays clean
•  Having a DQ process will ensure that the UDI
standards that have been implemented can be
monitored and reported on

Data Quality Definition and
Dimensions
Dimension Key Questions Impact
Completeness   Is all appropriate information readily available?
  Are data values missing or in an unusable state?
  Incomplete data can cause major gaps in data analysis which
results in increased manual manipulation and reconciliation
Conformity   Are there expectations that data values need to
reside in specified formats?
  If so, do all values conform to those formats?
  By not maintaining conformance to specific data formats, there is
an increased chance for data misrepresentation, conflicting
presentation results, discrepancies when creating aggregated
reporting, as well as difficulty in establishing key relationships
Consistency   Is there conflicting information about the same
underlying data object in multiple data
environments?
  Are values consistent across all data sources?
  Data inconsistencies represent the number one root cause in data
reconciliation between different systems and applications. A
significant amount of time by business groups is being consumed
with manual manipulation and reconciliation efforts
Accuracy   Do data objects accurately represent the “real-
world” business values they are expected to
model?
  Incorrect or stale data, such as customer address, product
information, or policy information, can impact downstream
operational and analytical processes
Duplication   Are there multiple, unnecessary representations
of the same data objects within a given data set?
  The inability to maintain a single representation for each entity,
such as agent name or contact information (across all component
business systems), leads to data redundancy and inconsistency, as
well as increased complexity in terms of reconciliation
Integrity   Which data elements are missing important
relationship linkages that would result in a
disconnect between two data sources?
  The inability to link related records together can increase both
the complexity and accuracy of any corresponding business
intelligence derived from those sources. It directly correlates to
the level of trust the business has in the data
Timeliness   Is data available for use as specified and in the
time frame in which it was expected?
  The timeliness of data is extremely important. Data delayed in
data denied. Could lead to reporting delays, providing slate
information to customers and making decisions based stale data

Why is Data Quality Important?
•  Organizations of all sizes and in all industries are
recognizing the importance of high-quality data and
the critical role of data quality in information
governance and stewardship driven by broader
enterprise information management initiatives –
Gartner
•  The Rule of Ten: If it costs $1 to complete a simple
operation when all the data is perfect, then it costs
$10 when it is not Achieving Business Success Through a Commitment to High –
Quality Data (TDWI Report Series), Wayne Eckerson
Data is a valuable Corporate Asset

Data Quality Value Proposition
Business Value
• Trusted version data for adverse reporting and
decision making
• Enabling data integrity and integration for UDI
compliance
• Operational efficiencies and on-time delivery, by
elimination of manually-intensive activities, and
reducing error-prone data integration processes
• Collaboration with internal and external data
sources by synchronization and consistency of
enterprise data across various business functions
and business channels
• Maximizing product and services revenue by
offering integrated solutions across business
units, as well as intelligent offerings of services
• Driving costs of bad data out of the system
• Responsiveness to new business opportunities
• Providing “plug and play” capabilities to
consolidate as well as extend IT architecture
• Ability to rapidly assimilate new data elements
into enterprise processes
Technology Enablement
• Integration of data across siloed IT solutions
• Ensuring the quality of the data being delivered
enhances the value of data integration
investments
• Capability of integrating to a single architecture
and solution
• Recognized as part of the driven force for master
data management and information governance
initiatives
• Support for service oriented architecture (SOA)
ensures the data quality capabilities can be
deployed and consumed as services and provides
a flexible, scalable environment for data to move
through the enterprise
• Ability to quickly produce high quality data that
is easily understood by functional users and
management and can generate cost savings in
both time dedicated to reacting and diagnosing
data quality problems and re-entering incorrect
data

[ MASTER DATA MANAGEMENT ]

What is Product Information
Management (PIM) ?
Ventana Research
Product Information Management (PIM) is the practice of using information and
technology to effectively support people and product related processes across the
enterprise supply chain throughout the life of a company’s products.
A PIM Data Hub is an enterprise data management solution that enables
centralization of all product information from various systems, creating a single
view of product information that can be leveraged across all Lines of Businesses,
Business Units and functional areas.
A PIM Data Hub can also be refereed as the MDM for Product data

PIM Information Supply Chain
Source: Riversand

Why PIM ?
•  A PIM Data Hub enables centralization of all UDI
product information from various systems, creating a
single view of product information that can be
leveraged across all Lines of business, trading
partners and UDI compliance
•  Having a centralized place to manage and govern UDI
data ensures you can manage continually changing
data and is of importance to UDI compliance
•  Ensure that your organization has the capabilities to
create and manage the required product information
data to comply with UDI

Data Governance, MDM, DQ
Work Together
Provide Guidance
Track Progress
Create & Enforce Policies
Provide Feedback
DQ
Discovery & Profiling
Cleansing, Duplicate Detection
Workflow, Data Sharing,
Maintenance, Synchronization
Measurements & Monitoring
PIM
Product Data Creation
Hierarchy Management/
Relationships
Media Asset Management
Integration & aggregation
Auto Generation (Description
Generation)
History & Audit Trail
Data Governance
Standardized Methods Data
Definition and Business Rules
Roles and Responsibilities
Decision Rights
Arbiters and Escalation
Statistics / Analysis /
Monitoring

[ GETTING STARTED ]

Getting Started
  Start with Data
Governance
•  Establish a council
•  Identify and train Data
Stewards
•  Engage stakeholders from
the different business units
e.g. compliance, IT, legal,
supply chain, product
management,
manufacturing, etc. to plan
and prepare for compliance
readiness
•  Data quality and
stewardship plays a
critical role in the
management of
product data
•  Create a data quality
process to ensure that
the device data has
the highest data
quality
•  Leverage an existing device project or
start a new project to test the
requirements
•  Select a device or a set of devices to test
the process from start to finish. Identify
data sources
•  Test the device from manufacturing to
distribution using the UDI requirements
•  Address data issues & refine the strategy
•  Perform data profiling to clean the data
•  Identify processes that are producing
inconsistent device data and refine them
•  Clarify data definitions and business rules
•  Define data standards
•  Integrate data standards into IT processes
•  Measure and monitor quality over time
•  If the test is successful add more devices
based on the prioritized strategy
Ensure Organizational
Readiness
Set up a Data Quality
Program
Conduct a Pilot

[ ENSURING SUCCESS ]

Clean
Govern
Consolidate
Share
Product Information Lifecycle
Management
PIM
•  Capture all product attributes and
relationships in a single data model
•  Create a universal ID for each product and
build a cross reference to each connected
system
•  Provide the golden product record and
selected attributes to all applications and
analytical systems
•  Enable product data availability as web
services to support service-oriented
architectures (SOAs)
•  Search product data via an integration
repository
•  Report using template-based XML to publish
information in multiple formats
•  Standardize key product data attributes
•  Match and de-dupe to create a single "blended"
record
•  Validate data thru description-generation rules
•  Auto-generate item numbers and descriptions
•  Govern and control product data changes
•  Manage item attributes and relationships
•  Manage product catalogs
•  Apply changes to groups of items meeting
specific criteria
•  Leverage full history and audit trails for
security and proof of compliance
•  Create configurable workflow-driven
product change processes
•  Define and approve new items
•  Import data from spreadsheets for data maintenance
•  Leverage bulk imports through staging tables
•  Integrate using standards-compliant business services and adapters
•  Create a blended product record from multiple sources

Ensuring Success
•  The following factors are usually evident in a successful
program:
  First create a strategy and then follow it (agreed on starting point and
steps necessary)
  Ensure solid alignment between Business and IT
  Identify and assess the importance of key people and/or groups
  Clearly defined and measureable success criteria
  Small iterations versus all or nothing
  Executive sponsorship is critical
  Really know your data
  Leverage prior experience/work…don’t re-invent the wheel
  Plan for time and resources required for manual reconciliation
  Communicate, Communicate, Communicate

Keys to Success
Successful
Implementation!
Technology
Process
People
Failed
Implementation!
Technology
Process
People

First San Francisco Partners
•  Data Governance Assessments and
Strategies
•  Business Case and ROI Development
•  Alignment Workshops
•  Training and Education programs
•  On-going Business support
•  Data Governance Performance
Management
•  Program Management
•  Data Architecture Assessments
and Strategies
•  Data Quality Assessments and
Strategies
•  Technology Vendor Analysis
and Evaluation
•  MDM and DQ Implementation
First San Francisco Partners is entirely focused on helping our
Customers leverage data as value-producing asset through improved
Data Governance and technology. We are a group of experts from the
industry who can help you create a strategy, align your organization
and deliver business value in both the short and the longer terms. We
do this via:
Facilitation, Enablement, Empowerment

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Enterprise Data Management Enables Unique Device Identification (UDI)

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