Fortune 500 Medical Devices Company Addresses Unique Device Identification New FDA regulation was driver for new data governance and technology strategies that could be leveraged for enterprise-wide benefit As a leading player in the $29.2 billion (in 2012) worldwide orthopedic devices market, this global medical technology company maintains a long history of success and exceptional growth. The company's products help medical professionals perform their jobs more effectively and improve the lives of patients in over 120 countries. But being part of in industry driven by both an aging population and regular technological advancements the global orthopedic devices market, according to one report 1, is expected to grow to $41.2 billion by 2019 means there will always be new regulations to comply with. One such regulation is the FDA s Unique Device Identification (UDI) rule. Finalized in September 2013, the UDI rule requires medical devices distributed within the United States to be labeled with a unique code which links to associated device and packaging information compiled by device companies and uploaded to a central database maintained by the FDA. The rule also mandates date stamps on labels adhering to a designated format and, for certain reusable devices, a permanent marking of the UDI code on the device itself. To spread the cost and burden of implementing these requirements while at the same time acknowledging the primary objective of improving patient safety as it relates to medical devices, the FDA imposed a risk-based, phased compliance schedule with the first deadline impacting primarily Class III devices set for September 2014. Especially in the face of a rapidly approaching deadline, the data management and technology implications of such regulation can be extensive, particularly for entities lacking an established approach to governing data and/or information management. While the company highlighted here practiced certain aspects of data governance and maintained some supporting technology infrastructure, management realized that their current systems, organizational structure, and data policies, processes and strategies were inadequate for meeting the needs of UDI compliance starting with the FDA s first deadline for which the company had over 4,000 applicable devices. They also recognized that their current state was impacting their ability to efficiently manage the organization s data overall. In order to help them address these needs, the company sought the expertise of First San Francisco Partners. Data Governance and Technology Assessments Identify Strengths and Weaknesses First San Francisco Partners (FSFP) began the engagement with assessments of the company s current data governance environment and technology landscape for product data. They conducted 45 interviews with key personnel across the company s business and technology units in order to gain insight into: Product data and business processes, limitations and potential risks Existing pain points relating to product data Usage of data as it pertained to business objectives Organizational challenges, expectations, benefits and success factors FSFP also reviewed relevant supporting documentation. And they analyzed existing product data and data architecture, including relevant databases and applications, product data movement and workflow, user interfaces and reporting tools and infrastructure. 1 Transparency Market Research (Jan. 1, 2014). Orthopedic Devices Market - Global Industry Analysis, Size, Share, Growth, Trends and Forecast, 2013-2019. Retrieved from http://www.transparencymarketresearch.com/orthopedic-devices-market.html Copyright 2014 First San Francisco Partners Medical Devices Company Addresses UDI Page 1
Data Governance Was Evolving but Still Limited in Scope FSFP identified a number of data governance capabilities and efforts being made within the company. At the same time, they uncovered key governance-related shortcomings and challenges. Data Governance Strengths Aspects of data governance existed at the divisional level. Some data issues as well as certain data quality metrics and KPIs were being tracked on a limited basis. In some divisions, data ownership, roles and responsibilities had been defined to help improve data quality. Product life-cycle management (PLM) and design control systems were employed in certain cases and, where used, worked well. These served as the primary repository/source of truth for device documents and drawings. In some divisions, procedures were in place with training provided to address how to create, use and update device data. Also, manuals existed to help improve data quality. There were ongoing but isolated efforts to improve data ownership and drive data governance. Data Governance Weaknesses Data governance, while present, existed only at the divisional level and, even then, only in some divisions and only to a certain extent. Ownership and accountability of data required for submission to the FDA s database was not clearly defined. A history of decentralization had lead to silo d divisions and inconsistent language concerning product data. Absence of a data governance strategy, a formal data governance organization and data principles had limited the understanding, usage and sharing of data across the company. Highly manual processes and data scrubbing were pervasive throughout the organization, introducing errors and redundancies and taking the focus away from value-added activities. Key enterprise data functions, roles and responsibilities present in mature organizations were informal or non-existent. There was no formal data quality control in place and no enterprise standard processes to audit, measure and monitor the quality of product data. Lack of enterprise data management processes, policies and standards had contributed to data inconsistencies, inaccuracies and incompleteness, which resulted in diminished trust in the data. Lack of awareness, education and communication as it pertained to UDI data could result in operating inefficiencies, one-off solutions and further lack of trust in the data. The IT Organization Was Unified Globally, but Product Data Management Remained Highly Decentralized In the technology arena, FSFP identified several key achievements and competencies at the company. However, certain technology-related limitations and obstacles were also present. Copyright 2014 First San Francisco Partners Medical Devices Company Addresses UDI Page 2
Technology Strengths Technology Weaknesses Master data management (MDM) technology awareness was strong. A formal MDM execution team was in place, and the company had implemented a Party (Customer) MDM Hub. The organization lacked a centralized master data model that would house master data and all UDI attribute data as well as support the company s transactional business systems across divisions. Management had executed on a product data hub/product information management (PIM) system with the intention of consolidating and centralizing product data from contributing systems across all divisions. The company had implemented technically capable solutions for ERP and PLM that possessed the foundational functionalities for a service-oriented architecture. Business and operational leads in the different divisions were aware of the data quality and fragmentation issues and generally welcomed change and/or improvement to existing technology. Core product data was not consolidated in a central location. Data resided in disparate systems across many divisions. Data quality was not formally managed, leading to data duplication, inaccuracies and missing data. Master data was not connected across all divisional systems via an enterprise data integration technology or a hub technology. Key business services and workflows that automated business processes, or that could issue concurrent requests for definitions and approvals, were not enabled. Histories of device changes and associated lineage were not well-maintained. Traceability was difficult or impossible. The existing Item Master and System of Record being used for device data lacked core commercially available product hub functionalities. Gap Analysis According to the FSFP Maturity Model Guides Development of Targeted Recommendations With the current states for data governance and technology for product data clarified, FSFP worked with the company to identify and define desired future states. These were based on industry best practices while taking into account the unique qualities of the organization. Current and desired future states were then mapped for key components in each case according to the FSFP Maturity Model. For data governance, maturity was mapped for the three major pillars of data governance: People, Process and Technology. In a similar fashion, technology maturity was mapped according to the following MDM elements: Data Profiling, Enterprise Data Model, Data Quality, Integration Architecture, Workflow Tool, Skills and Competencies, Master Data Architecture, MDM Processes, Master Data Repository and Implementation Methodology. Analyzing the maturity gaps, FSFP was able to identify areas in need of improvement and develop data governance and technology recommendations that would bridge the gaps. The visual mapping also served a secondary purpose: To help align stakeholders, build consensus and ensure a unified vision for the overall strategic direction of the organization. Importantly, FSFP recognized that their recommendations would need to be pragmatic tangible results would be necessary in order to create momentum and sustain business interest and they would need to provide the company with the direction to proceed beyond the immediate focus on UDI compliance. Data Governance Operating Model Recommendation FSFP recommended the company establish a hybrid data governance operating model (hub and spoke). The recommendation was based on criteria including the company s culture, how decisions were made Copyright 2014 First San Francisco Partners Medical Devices Company Addresses UDI Page 3
consensus-based desired future state data governance requirements and existing decision-making bodies. The operating model included a comprehensive and detailed list of the roles and responsibilities along with skill sets and time commitments that would be essential to the success of the model. The operating model addressed all of the key weaknesses identified during the initial data governance assessment. Enterprise Data Model UDI Recommendations FSFP recommended the company define a canonical, all-encompassing product data model within the existing master data architecture which would house all of the FDA-mandated UDI attributes. A canonical data model would enable information sharing and centralize device data in a cost-effective and standardized manner. This would mean users would only need to go to one place to retrieve all required attribute information for a given device. FSFP also suggested the company define attribute-level rules and validations which would aid in sustaining clean attribute data. Integration Architecture UDI Recommendations FSFP recommended the company devise and implement a reusable and sustainable data integration strategy, architecture and processes to simplify and automate collection of the currently fragmented UDI data into a central database (hub). The architecture should also be able to support batch and real-time data interfaces and automatically capture changes to UDI data. Data Quality UDI Recommendations FSFP recommended the organization implement a toolset for finding and fixing quality issues with UDI attribute data as well as establish a central repository for creating and reviewing data quality rules (and remediation rules). They further proposed the company enable operational systems to utilize these services and rules to improve quality at the point of entry. Data Profiling UDI Recommendations FSFP recommended the company expand on their current data profiling/scorecard activities to include metrics on UDI data accuracy, completeness and structure, duplication, linkages and relationships and availability. They should also collect similar data profiling metrics from key divisional systems that are the source of UDI attributes. FSFP further recommended that the company identify and execute on corrective actions to resolve data errors and discrepancies on a periodic basis and they should run several iterations each time they do so. Master Data Architecture UDI Recommendations Finally, eyeing the long term, FSFP recommended the company establish a robust master data architecture optimized for mastering UDI data from both existing and new divisional systems and purposed as a UDI submission tool. The system would need to be capable of performing data quality functions such as cleansing, matching, consolidating and surviving the best version of given product data, cross-referencing mastered data to source versions, maintaining source data history and audit trails and publishing UDI data to external systems, including the FDA s UDI database. Roadmaps Provide Guidance on Implementing Recommendations FSFP subsequently worked with the company to formulate phased, prioritized, actionable data governance and technology roadmaps addressing how to execute the recommendations. Copyright 2014 First San Francisco Partners Medical Devices Company Addresses UDI Page 4
Data Governance Roadmap The data governance roadmap articulated a clear plan for operationalizing the data governance operating model. The roadmap needed to be practical in scope and provide gains early on with respect to UDI. But it also needed to align with the evolution of technology and product hub architecture. Starting with planning and resource on-boarding, the initial focus of data governance would be on establishing the right staff and resources and on defining the data governance strategic vision and direction needed to address UDI compliance. As the UDI product hub was designed, built and operationalized, data governance would focus on orienting the organization, creating foundational deliverables and establishing baseline data quality and data governance metrics. It would also require governance of key data assets. As hub architecture evolved and expanded to incorporate not just UDI data but product data overall, governance would focus on assessing the effectiveness and efficiency of the operating model and on seeking continuous improvement. The roadmap also took it one step further, extending the plan to address all types of enterprise data, including customer, vendor and other data. On-going attention to change management, communication, training and awareness would ensure the successful implementation of data governance. Technology Roadmap The technology roadmap addressed gaps in infrastructure, streamlining of data processes and data quality improvements. FSFP focused on specific future state technology requirements in the areas of enterprise data model, integration architecture, data profiling technology, data quality technology and master data architecture. While an initial stop-gap solution focused exclusively on Class III devices would ensure the company could achieve compliance by the first UDI deadline, this would also help the company meet the initial product hub milestone. At the same time, they would plan, design and then build a UDI product hub to accommodate the data for all classes of devices. After execution and deployment, this hub would serve as the master of all UDI data, and the stop-gap solution for Class III devices could be retired. Having laid a strong foundation for hub architecture with the UDI hub, the organization would be able to extend that hub to encompass data for all products, not just that specific to UDI devices. As this global product hub matured, the organization s inadequate, outdated Item Master and System of Record would be retired. Conclusion Over the course of less than two months, First San Francisco Partners was able to assess the current state of the highlighted company s data governance and technology environments identifying both strengths and weaknesses for each. They then mapped those findings to desired future state according to the FSFP Maturity Model. Based on the maturity gaps identified, they were able to devise actionable recommendations to bridge the gaps. Finally, FSFP was able to articulate a clear plan for operationalizing those recommendations which would enable the company to successfully address the immediate, tactical needs of UDI compliance as well as set a course for achieving longer-term, strategic goals that would benefit the enterprise on the whole. Throughout the course of the engagement, FSFP helped build consensus across business, IT and executives regarding people, process and technology successes and gaps. They were able to strengthen executive commitment and buy-in to address both short-term and longer-term needs. FSFP was able to achieve successes at the company by blending resources that knew MDM, data governance and organizational change alongside those with deep technical data skills. Copyright 2014 First San Francisco Partners Medical Devices Company Addresses UDI Page 5
About the Author As CEO and Founder of First San Francisco Partners, Kelle O Neal provides specialist data governance and data management consulting services to complex organizations that deliver faster time-to-results. Kelle can be reached at kelle@firstsanfranciscopartners.com or through the First San Francisco Partners website (www.firstsanfranciscopartners.com). About First San Francisco Partners First San Francisco Partners is a unique consulting firm that specializes in the definition and implementation of enterprise information management and big data strategies. First San Francisco Partners helps organizations better govern, manage, integrate and share their critical information assets to ensure companies can maximize profits, reduce risks, increase operational efficiency and reduce operating expenses. For more information, please visit www.firstsanfranciscopartners.com or call 1-866-761-3742. Copyright 2014 First San Francisco Partners Medical Devices Company Addresses UDI Page 6