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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