DATA GOVERNANCE FRAMEWORK IMPLEMENTATION PLAN

Transcription

1 City of Philadelphia Department of Behavioral Health and Intellectual disability Services DATA GOVERNANCE FRAMEWORK IMPLEMENTATION PLAN VERSION FY JANUARY 2018 Actionable Steps to Align Information Management Concepts, Practice and Context DAVID. T. JONES COMMISSIONER

2 TABLE OF CONTENTS Data Governance Framework Strategic Plan Executive Summary... 3 DBHIDS Data Governance Framework Implementation Plan... 5 From the Data Governance Framework Strategic Plan... 5 Status of DBHIDS Data Governance Strategies, Version Transition to Data Governance Framework Version Implementation Plan 2018 Objectives, Strategies & Levers... 8 Data Governance Objectives by Goal... 8 Data Management Implementation Strategies Implementation Plan Levers FY2018 Tactical Plan Alignment of Goals, Objectives, Strategies, and Tactics Implementation Activity Programs Implementation Activity Timeline Works Cited Credits Appendix A: The Office of Enterprise Data Management Appendix B: Data Governance Roles and Responsibilities Appendix C: The Owner/Steward/Custodian Model Appendix D: Mapping of Responsibilities to Roles Appendix E: Mapping Information Management Use Cases Appendix F: Data Governance Framework v1 Goals Scorecard DBHIDS 2018 All Rights Reserved Daniel J. Paolini, CIO with input from the Enterprise Data Management Team A data management professional s job is to change the status quo. David P. Marco, DataUniversity.com (2017) Page 2 of 41 Data Governance Framework Implementation Plan v2 11 January 2018

3 DATA GOVERNANCE FRAMEWORK STRATEGIC PLAN EXECUTIVE SUMMARY The Data Governance Framework Strategic Plan Executive Summary is provided here for context. This Implementation Plan is consistent with and derived from that Strategic Plan. The Strategic Plan also includes the Glossary of Information Architecture Terms defining terms used in this document. Information Technology represents the Information, Applications, and Infrastructure necessary to meet the needs of the Business. Of these, the most important resource is the information asset. It is this information that represents a model of the functioning business over time. Infrastructure and applications change to reflect changes in technology or process, but the data that records the transactions of key entities with the business must be logically defined and maintained so that it is consistent, persistent and useful. Data quality degrades when data is not managed continually by the business. The ability for data systems to support business processes in the pursuit of business goals degrades over time when data quality degrades. This results in more cost but less benefit. Attempting to fix these problems at the operational level will not work. Adding more data and data systems in an effort to fix these problems only makes them worse. What will fix these problems is not more technology, more systems or more data, but Data Governance. Data Governance advances the twin goals of Data Quality and Data Reusability; data that is timelier, more accurate, more complete, more accessible, more useful and less costly. Data Governance is not a technology function. It is driven by the business and forms a bridge between business management and technology providers. Our Data Governance Framework identifies the desired future-state needed to address the problems that compromise our information asset today. It documents our Information Architecture. Taking our cue from the Practice Guidelines, we will align information management concepts, practices, and context. (White, 2014) Below are the Information Architecture Principles, Data Governance Strategic Goals, Data Management Implementation Strategies, and Information Service Delivery Use Cases identified in the Data Governance Framework Strategic Plan. These represent the what and why of our approach. The how, who, when, and where are discussed in more detail in this Implementation Plan. (Paolini, 2017) 11 January 2018 Data Governance Framework Implementation Plan v2018 Page 3 of 41

4 TEN INFORMATION ARCHITECTURE PRINCIPLES 1. Information architecture is the reflection of the business; it is not just a technology domain. 2. The identification and definition of data attributes must involve the business. 3. Data is an organizational asset and must be managed with an enterprise perspective. 4. Data that is common to more than one business unit must be defined through consensus by representatives of those business units. 5. The value of data to the enterprise is in its fitness for reusability, not its exclusivity. 6. The value of data management staff is in its ability to build high quality, reusable data assets. 7. Different information use cases require different data management solutions. 8. In order to be sustainable, physical data stores must be governed by a conceptual understanding of the enterprise, captured in a business architectural model. 9. The purpose of a data management organization is to produce a data product that meets the information needs of the business commensurate with the investment made by the business. 10. Information architecture must be formal and proactive; it cannot be improvised or reactive. TEN DATA GOVERNANCE STRATEGIC GOALS 1. Create an information-centric and informed organizational culture. 2. Continue and strengthen the data governance program by adopting a centrally-managed data and information request process. 3. Provide for effective and appropriate information security. 4. Improve the quality and usefulness of information by making it timelier, more accurate, more complete and more accessible. 5. Reduce the costs of managing information. 6. Reuse data through shared processes; share data through reusable processes. 7. Provide self-service business intelligence capabilities. 8. Develop enterprise-class data management staff. 9. Adopt enterprise-class data management tools. 10. Redefine the roles of data-related analysts in the organization to better match analyst capabilities to organization needs. THREE BROAD DATA MANAGEMENT IMPLEMENTATION STRATEGIES 1. Evangelize and implement the Data Governance Framework to the department. 2. Clearly define and implement data governance roles and responsibilities. 3. Manage data as an organizational resource so it is available whenever and wherever needed. SEVEN INFORMATION SERVICE DELIVERY USE CASES 1. Transactional Processing: To Do 2. Operational Reporting: To Know 3. Key Performance Indicators: To Measure 4. Analytical Reporting: To Learn 5. Data Integration & Persistence: To Remember 6. Data Discovery & Mining: To Explore 7. Probability Analysis: To Predict Page 4 of 41 Data Governance Framework Implementation Plan v2 11 January 2018

5 DBHIDS DATA GOVERNANCE FRAMEWORK IMPLEMENTATION PLAN Actionable Steps to Align Information Management Concepts, Practice and Context FROM THE DATA GOVERNANCE FRAMEWORK STRATEGIC PLAN DBHIDS requires a fully-developed data governance approach to mandate enterprise data management. It requires an enterprise data architecture unit to design and document data structures, objects and their definitions. It requires an enterprise data integration unit to manage reusable data resources. It requires a business intelligence unit to make data available to knowledge workers. This will place Enterprise Data Management responsibilities into the hands of data management professionals rather than in the hands of technologists or analysts currently responsible, enabling those technologists and analysts to spend more time in their respective and equally important worlds. This effort will be guided by the DBHIDS Data Governance Framework Strategic Plan. The Data Governance Framework Strategic Plan version 2 is a substantial update. The reasons for the update to the Strategic Plan include: We know more now than we did two years ago. Our organization mission is evolving. Our data management capabilities are growing. We have implemented things that did not exist before the original plan and based upon what we learned we can now evaluate their efficacy and adjust efforts moving forward. Furthermore, this revised Strategic Plan is aligned with the DBHIDS Vision and Mission. The DGF Implementation Plan addresses the DBHIDS Information Service Delivery Use Cases within the DBHIDS Conceptual Information Architecture Schema. The plan establishes Data Governance Objectives, Strategies, and Tactics to achieve the DBHIDS Data Governance Goals while adhering to DBHIDS Information Architecture Principles. The Plans and Tasks necessary to implement the Strategies and achieve the Objectives will be developed by the units responsible for achieving the Goals. Principles (Martin, 2013) Please refer to the Strategic Plan s Glossary of Information Architecture Terms for useful definitions. Goal, Objective, and Strategy alignment is described in more detail in the sections that follow. 11 January 2018 Data Governance Framework Implementation Plan v2018 Page 5 of 41

6 Informationcentric culture Data Governance Program Information Security Usefulness of Information Reduce Information Costs Share Data and Processes Self-Service BI Reporting Data Management Staff Data Management Tools Do Know Measure Learn Remember STATUS OF DBHIDS DATA GOVERNANCE STRATEGIES, VERSION 1 Relationship of Data Governance Strategies to Goals Data Governance Strategic Goals Data Governance Strategic Objectives 1. Establish the Office Enterprise of Data Mgmt. 2. Implement the Data Governance Framework 3. Achieve compliance with policies and standards 4. Develop new draft policies and standards 5. Manage enterprise LDM and business glossary 6. Provide enterprise metadata management 7. Manage master data management domains 8. Manage data integration & persistence layer 9. Provision data for those that need as required 10. Evangelize the Data Governance Framework TRANSITION TO DATA GOVERNANCE FRAMEWORK VERSION 2 As a result of the efforts to implement the initial Data Governance Framework, the data management landscape in 2017 is radically different. DBHIDS is still a diverse organization. It has a centralized enterprise data management focus while incorporating a federated model for data stewardship and business analysis. Most new data system development is reviewed first to leverage existing capabilities and align with department data requirements. Most business units recognize the difference between data that needs to be collected to document transactions and reports and analysis that are derived from that data. Many new requests for data are reviewed to leverage existing data, avoiding redundant effort, as well as to identify new opportunities to build sustainable solutions. While master data management has not yet been implemented, data stewards have been identified for various MDM domains and have begun identifying and reconciling business rules required for each domain. A Strengths, Weaknesses, Opportunities, and Challenges analysis is included in the Data Governance Framework Strategic Plan Version 2. A summary of the 2017 analysis is below. Strengths include the establishment of the Office of Enterprise Data Management, the establishment of the Data Governance Executive Board and Data Stewardship Council, the creation of master data management domain stewardship committees, the initiation of the enterprise data warehousing, master data management, and business intelligence platform initiatives, and the identification of guidelines for the coordination of data analyst activities. The most important strength continues to be a commitment by executive management to support enterprise data governance, data quality, and data management programs. Page 6 of 41 Data Governance Framework Implementation Plan v2 11 January 2018

7 Unfortunately, there are several significant weaknesses that must be addressed. There is a continued treatment of some data requests as custom research projects, resulting in redundant effort, no sustainability, and poor data quality. Some business unit managers do not accept their role in prioritizing data requests and projects. We still rely too much on outside vendors and contractors for data architecture and design decisions that should be owned by the organization. There are numerous opportunities present in The loss of key staff has highlighted the danger of relying on hero-based analysts and has steered the organization towards a formal, enterprise approach. We have demonstrated value of the information architecture to executive management through pilots and small-scale projects even as large-scale initiatives were getting underway. We have filled many staffing needs but there remain opportunities to add needed skills and capacity. We need to refine the data request process and make sure that it is in use throughout the organization. Many challenges also remain. Our analyst organizations are still too research analyst heavy, which does not provide the right skills to improve data quality. Our new enterprise data management efforts are often slowed or delayed due to the day-to-day needs of the organization for data produced through the less-efficient legacy processes. The capabilities and resources of the City of Philadelphia s central information technology office (OIT) do not always align with our needs. Practical Architecture It is important to explain the benefits to the organization that will be provided by an enterprise approach, but it can be difficult at times to make these overarching issues resonate with business management. While keeping the big picture of our enterprise information management and data governance landscape in perspective, we must demonstrate how implementation plan activities address the pain points of the individual business units. Moving forward, for each project, we must connect the enterprise activities required by this implementation plan to either the solution sought by the business or the avoidance of both seen and unforeseen problems; ideally, we will accomplish both. 11 January 2018 Data Governance Framework Implementation Plan v2018 Page 7 of 41

8 IMPLEMENTATION PLAN 2018 OBJECTIVES, STRATEGIES & LEVERS In this version of the Implementation Plan, Objectives are measurable activities aligned with Goals and with expected target dates. Strategies are created to help achieve the Objectives and a Strategy may influence more than one Objective. The Tactics for each Strategy are specific to the Objective or Objectives to which the Strategy applies. Refer to the FY2018 TACTICAL PLAN for a matrix showing the ALIGNMENT OF GOALS, OBJECTIVES, STRATEGIES, AND TACTICS. Refer to the 2018 TACTICAL ACTIVITIES TIMELINE in APPENDIX F for a chronological listing of objectives. DATA GOVERNANCE OBJECTIVES BY GOAL 1. Create an information-centric and informed organizational culture. a. All stakeholders will receive access to regular informational sessions about Data Governance, Information Architecture, Data Management, and Business Intelligence at least every two years, starting July 1, b. The executive management team will be educated to understand the value of defining the data of interest to a business unit before building a process or report that uses data at least quarterly, starting July 1, Continue and strengthen the data governance program by adopting a centrally-managed data and information request process. a. A central Data Request address shall be created and put into use by July 1, b. A standard set of questions to define a request will be identified and put into use by August 1, c. A review and response process for requests will be established and put into use by September 1, Provide for effective and appropriate information security. a. A DBHIDS Information Security Coordinator (ISC) will be designated by October 1, b. Database encryption will be deployed across all DBHIDS data stores by January 1, c. Employees will receive regular training about cyber security at least annually and reminder information about cyber security at least quarterly, starting October 1, Improve the quality and usefulness of information by making it timelier, more accurate, more complete and more accessible. a. An Enterprise Data Warehouse environment will be deployed into production by October 1, b. Enterprise Master Data Management will be implemented around common data domains by January 1, c. Knowledge workers will have access to governed data using self-service Business Intelligence tools by April 1, Page 8 of 41 Data Governance Framework Implementation Plan v2 11 January 2018

9 5. Reduce the costs of managing information. a. Redundant independent reporting data stores will be eliminated by January 1, b. Redundant independent data integration processes will be eliminated by July 1, c. Institutional knowledge and metadata about information assets will be documented and published in accessible catalogs by October 1, Reuse data through shared processes; share data through reusable processes. a. The Enterprise Data Warehouse will be used as the source for most reporting and all data exports by January 1, b. The Enterprise Business Architecture Model (BAM) will be leveraged to define the formats for real-time data exchange between systems by January 1, Provide self-service business intelligence capabilities. a. QlikSense will be deployed throughout the organization by July 1, b. Processes for identifying new reporting needs will be developed and put into use by January 1, Develop enterprise-class data management staff. a. Staff will have access to online and in-person data management training on a regular basis by October 1, b. Staff will belong to and actively participate in data management professional organizations by July 1, Adopt enterprise-class data management tools. a. Kalido will be implemented successfully as the Enterprise Data Warehouse and Master Data Management platform by January 1, b. QlikSense will be implemented successfully as the Enterprise Business Intelligence platform by July 1, c. Erwin will be implemented successfully as the Enterprise Data Modeling platform by July 1, Redefine the roles of data-related analysts in the organization to better match analyst capabilities to organization needs. a. Data analysts and architects will be recruited and hired to staff central Enterprise Business Intelligence and Enterprise Data Management teams to provide guidance to other analysts in the organization, with full staffing by July 1, b. Business Data Analysts (BDAs) will be recruited and hired and attached to individual business units but guided by the appropriate central Enterprise Business Intelligence or Enterprise Data Management organizations, with all units having access to BDAs by January 1, c. Research Analysts will disengage from day-to-day data management responsibilities and will be refocused on research and evaluation activities by January 1, January 2018 Data Governance Framework Implementation Plan v2018 Page 9 of 41

10 DATA MANAGEMENT IMPLEMENTATION STRATEGIES There are three broad data management implementation strategies that will be used to guide the implementation plan efforts. These cover three essential areas necessary for a successful information architecture program: Organizational awareness and understanding of the problem, definition of roles and responsibilities to address the problem, and, identification of the tools, methodologies, and practices to manage data effectively in response to the problem. Roles and Responsibilities Evangelize and Implement DGF Data Governance Goals and Objectives Organizational Data Assets 1. Evangelize and implement the Data Governance Framework to the department 2. Clearly define and implement data governance roles and responsibilities 3. Manage data as an organizational resource so it is available whenever and wherever needed These strategies are defined more fully in the sections that follow. Evangelize and implement the Data Governance Framework to the department This strategy includes the following approaches: 1. Evangelize the Data Governance Framework principles to the department. 2. Develop information architecture and data management policies and standards for consideration by management. 3. Achieve compliance with those policies and standards through effective data governance roles and bodies. The use of the term evangelism to describe transformative activities in the information technology space is not familiar to everyone. A technology evangelist is a person who builds a critical mass of support for a given technology, and then establishes it as a technical standard in a market that is subject to network effects. (Lucas-Conwell, 2006) Like a religious evangelist, the role of a technology evangelist is to promote a particular architectural approach, using a variety of channels and media. The technology evangelist is delivering a new way of thinking to the listener with the goal to convert the listener to that way of thinking. This is most successful if the listener is convinced willingly or concludes that the new way is his or her idea. Because new approaches are transformative, technology evangelism is more successful when led by an internal employee rather than an external consultant. While the consultant may be best positioned to identify the transformative direction, the long lead time to adoption requires someone with persistence and permanence. Page 10 of 41 Data Governance Framework Implementation Plan v2 11 January 2018

11 The CIO and the Office of Enterprise Data Management leadership have the task of evangelizing to the department the Data Governance Framework principles. This includes the following target audiences, each of which will need to be approached in a different manner: Executive Management to secure sponsorship and policy approval Operational Management to secure participation and cooperation Knowledge Workers to secure requirements and compliance Technology Staff to secure support and coordination In addition, the following approaches will be used. 1. The Data Governance Executive Board will be kept informed of the activities of the Data Stewardship Council and the Office of Enterprise Data Management. 2. Data practitioners will meet regularly to share techniques and discuss common challenges. 3. Data governance-related presentations will be made available to DBHIDS staff on a regular basis. 4. The Data Governance Framework will be made available for reference and appropriate sections cited in RFPs, project proposals and any other description of a data-related initiative. 5. Policies and practices will be developed and monitored for compliance. Practical Evangelism Authors Mary Lynn Manns and Linda Rising state You need three things to make your idea happen: your belief in the idea, the determination to act on your belief, and some strategies to make it happen. They believe strategies are formatted in the form of a pattern. (Manns & Rising, 2004) In a ZDNet BriefingsDirect podcast interview Bill Rouse, Professor in the School of Industrial and Systems Engineering and the College of Computing at the Georgia Institute of Technology stated: We ran a study recently about what competencies you need to transform an organization based on a series of successful case studies and we did a survey with hundreds of top executives in the industry. The number one and two things you need are the top leader has to have a vision of where you re going and they have to be committed to making that happen. Without those two things, it seldom happens at all. (Rouse, 2012) Clearly Define and Implement Data Governance Roles and Responsibilities This strategy includes the following approaches: 1. Define data governance roles and responsibilities. 2. Establish the governing bodies necessary for effective data governance. 3. Create and sustain the Office of Enterprise Data Management (OEDM) within the Office of the CIO to provide department-level focus and coordination for data management, architecture, governance, integration, collaboration and business intelligence (see. 4. Engage with stakeholders throughout the rest of the organization to educate them about their role and responsibilities. The definitions of the various data governance bodies, enterprise data management roles, and individual data governance roles and their responsibilities are included in Appendix B. 11 January 2018 Data Governance Framework Implementation Plan v2018 Page 11 of 41

12 Manage data as an organizational resource, available whenever and wherever needed In order for data to become an organizational resource, it we must stop considering it a by-product of technology processes and instead designate it as THE product. In order to do that, we must embrace functions that are not addressed by traditional IT staff. These include the following areas: 1. Manage the enterprise business glossary and business architecture model. 2. Provide enterprise metadata management for users. 3. Manage the enterprise master data management domains. 4. Manage the enterprise data integration and persistence layer. 5. Leverage Data Governance roles to create a Data Quality program of continual improvement. 6. Provision data for those that need it in the manner required. The enterprise business glossary is most important, as it is here that the organization identifies the key business concepts represented in data and agrees to official, consistent definitions. It is also where synonyms and variants are documented and aligned with the official version. Once the business glossary is compiled and is being managed, it becomes the source for the enterprise business architecture model (BAM). This is an enterprise logical data model that captures all relevant business entities and attributes in a normalized (non-redundant) way. It defines each and shows how they are related. All physical system data dictionaries can be mapped to this enterprise BAM, serving as a guide to integration. Creating a business glossary, a BAM, or compiling physical data dictionaries is of limited use unless these artifacts are made available to knowledge workers and others seeking data or answers to questions. These metadata objects must be managed and made available to those that need them. Having identified the most important organization-wide business glossary items and then mapped the source systems to the enterprise BAM, we must implement the integration and management of these key business domains: People Receiving Services, Providers of Services, and the Services Provided. This master data management requires engagement with data stewards within the business units to both define the domains and then reconcile newly identified records in each. It is not a technology exercise, but it is coordinated with the data integration and persistence functions. The enterprise data integration and persistence layer is referred to as our enterprise data warehouse (EDW) environment. This area uses the mapping of source system data dictionaries into the enterprise BAM to develop data integration processing to create an enterprise view of the disparate systems. It is within this processing the data quality issues are often identified and it is from this that the resources become useful for reporting and analysis. This layer is also integrated with master data management. All of the above activities, in combination with well-defined roles and responsibilities, support an effective data quality program. Data quality is a continual process of making data fit for purpose. All stakeholders contribute to its improvement. Data quality is not perfect data, it is known data. Data quality issue resolution is moved as close to the source systems as possible ideally at the source. Once these activities above are underway, the real purpose of our information architecture delivering the right data to those that need it when they need it can be fulfilled. Page 12 of 41 Data Governance Framework Implementation Plan v2 11 January 2018

13 IMPLEMENTATION PLAN LEVERS Role of Information Service Delivery Use Cases The Information Service Delivery Use Cases are not implemented themselves. Rather, they are design patterns that categorize specific types of solutions. As such they serve as levers to maximize investment in data and processes and keep them consistent with the information architecture. Here are representative data management solutions for each use case. 1. Transaction Processing Custom-built and off-the-shelf transaction processing applications Integrated enterprise resource planning (ERP) applications Basic self-contained operational reporting to monitor and manage the business function Real-time process integration via web services between applications and external sources of master and reference data (shareable data through reusable processes) 2. Operational Reporting Robust operational reporting against operational data marts or replicated transactional system data stores Limited integration of external data for enriched reporting (this must be closely monitored and moved into Data Integration and Persistence if more than minimal) 3. Key Performance Indicators Line of business or organization-wide Current state with limited history 4. Analytical Reporting Line of business or organization-wide Longitudinal/historical/aggregate 5. Data Integration and Persistence Real-time and batch integration Single source of the truth (reusable data through shareable processes) Master data management 6. Data Discovery and Data Mining General and applied research Prototyping new information delivery solutions 7. Probability Analysis Time-based regression analysis Multi-variant regression analysis Risk Scoring 11 January 2018 Data Governance Framework Implementation Plan v2018 Page 13 of 41

14 Alignment with Conceptual Information Architecture Schema The information service delivery use cases provide context for the organization of information solutions by describing the broad categories of information needs to which design patterns can be applied. They do not, however, clearly indicate the relationship to traditional data processing. The DBHIDS Conceptual Information Architecture Schema illustrates the components of our data management and information delivery activities. It provides a lower level perspective with additional detail to more clearly illustrate the relationships between the different components. This conceptual model is used to guide the design of the logical components of the DBHIDS information architecture which are then instantiated in various physical systems. It also serves as a lever for maximizing investment benefits. DBHIDS Conceptual Information Architecture Schema On the next page, the DBHIDS Conceptual Information Architecture Schema is overlaid with the seven information service delivery use cases to show the alignment of these use cases with our information architecture. Page 14 of 41 Data Governance Framework Implementation Plan v2 11 January 2018

15 Here is the high-level process view of the specific data integration, master data management, and business intelligence components of the DBHIDS information architecture. This process view is used to guide the physical implementation of the Integrated Processing Environment, Integrated DBHIDS Data Resource, Master Data Management, DBHIDS Access Layer, and the Dashboards, Analytics, and Data Mart components. 11 January 2018 Data Governance Framework Implementation Plan v2018 Page 15 of 41

16 FY2018 TACTICAL PLAN Each of the Data Governance Strategic Goals has two or more objectives. Each of the objectives is influenced by one or more of the broad Data Management Implementation Strategies. In the chart below, the specific implementation activity (tactic) for each strategy or strategies for each objective are identified. Those highlighted in green represent primary implementation activities. Those highlighted in orange represent supporting implementation activities. Those highlighted in red are tactical efforts that will carry over into fiscal year A listing of the activities by program category and a listing of activities chronologically follow. ALIGNMENT OF GOALS, OBJECTIVES, STRATEGIES, AND TACTICS Strategy Goal Objective 1 Create an information-centric and informed organizational culture. a. All stakeholders receive access to regular training sessions about Data Governance, Information Architecture, Data Management, and Business Intelligence, starting by July 1, b. The executive management team understands the value of defining business data before building a process or report that uses data, starting by July 1, Continue and strengthen the data governance program by adopting a centrally-managed data and information request process. a. A central Data Request address exists and is in use by July 1, b. A standard set of questions to define a request is identified and is in use by August 1, c. A review and response process for requests is established and is in use by September 1, 2017 Evangelize and implement the DG Framework Multiple one-hour information management training sessions are presented annually to three audiences. A 30-minute session is presented to EM quarterly to reinforce DGF/IA. Define and implement roles & responsibilities Specific Tactics EM functions as DG Executive Board and approves policies and programs. The EDM Office receives data request s. The EDM Office determines the attributes. The EDM Office triages requests promptly and determines need for follow-up. Manage data as an enterprise resource EM directs that units coordinate their data activities with EDM. DBHIDS.Request4 is activated. A Request for Data form is created and deployed. Requests are logged and those requiring follow up activate more detailed review. Page 16 of 41 Data Governance Framework Implementation Plan v2 11 January 2018

17 Strategy Goal Objective 3 Provide for effective and appropriate information security. a. A DBHIDS Information Security Coordinator (ISC) is identified by October 1, b. Database encryption has been deployed across all DBHIDS data stores by January 1, c. Employees receive regular ongoing education about cyber security, starting by October 1, Improve the quality and usefulness of information by making it timelier, more accurate, more complete and more accessible. a. An Enterprise Data Warehouse environment is created and is in use by October 1, b. Enterprise Master Data Management is implemented around common data domains by January 1, c. Employees have access to governed data using self-service Business Intelligence tools by April 1, Evangelize and implement the DG Framework Define and implement roles & responsibilities Specific Tactics The ISC is identified and serves as a nexus for compliance, perimeter, and data security. Employees receive specific security training annually and cyber security reminders at least monthly. Manage data as an enterprise resource Data is stored in an RDBMS that supports data encryption. Employees understand the dimensions of confidentiality, integrity and availability and manage data accordingly. The Kalido DIW is in production and is the source for enterprise data analysis and external data consumers. The Kalido MDM product is in production and providing MDM for Person, Provider, Service, and related reference data. The Qlik Sense product is in production and providing access to the most requested data and measures. 11 January 2018 Data Governance Framework Implementation Plan v2018 Page 17 of 41

18 Strategy Goal Objective 5 Reduce the costs of managing information. a. Redundant independent reporting data stores have been eliminated by January 1, b. Redundant independent data integration processes have been eliminated by July 1, c. Institutional knowledge and metadata about information assets is documented and published by October 1, Reuse data through shared processes; share data through reusable processes. a. The Enterprise Data Warehouse is in use as the source for reporting and data exports by January 1, b. The Enterprise Business Architecture Model is leveraged to define the formats for real-time data exchange between systems by January 1, Provide self-service business intelligence capabilities. a. QlikSense has been deployed throughout the organization by July 1, Evangelize and implement the DG Framework A data mart catalog is available to users so that they understand what data is available. Business management is taught to contact EDM when a new integration need is identified. Business and technology users acknowledge the need to create business process and data documentation before building anything. Business units and their analysts requests for data through the enterprise process. Analysts and system developers work with the Data Architect to define formats. Define and implement roles & responsibilities Specific Tactics In conjunction with the request for data process, business data analysts know not to create new one-off reports. Business data analysts know not to create new one-off integrations. Business data analysts participate in data stewardship activities and document the data for which they are responsible. Manage data as an enterprise resource The Web Focus environment is sunset and use of spreadsheets as personal data marts is reduced or eliminated. The independent movement of batch files is eliminated. A business glossary, data dictionary, and data mart catalog is published and accessible by data consumers. All pertinent department data has been integrated and documented. XML and JSON schemas are based upon the ELBM attributes. Data analysts and key knowledge workers have access to the Qlik platform and necessary applications. Page 18 of 41 Data Governance Framework Implementation Plan v2 11 January 2018

19 Strategy Goal Objective b. Processes for identifying new reporting needs have been developed and are in use by January 1, Develop enterprise-class data management staff. a. Employees have access to online and in-person data management training by October 1, b. Employees belong to and participate in data management professional organizations by July 1, Adopt enterprise-class data management tools. a. Kalido is successfully used as the Enterprise Data Warehouse and Master Data Management platform by January 1, b. QlikSense is successfully used as the Enterprise Business Intelligence platform by July 1, Evangelize and implement the DG Framework Business units and analysts direct requests for data through the enterprise process. Define and implement roles & responsibilities Specific Tactics The OEDM will implement a process to receive, review, and fulfill new data requests. OEDM staff will deliver a series of trainings on data management topics on a regular basis targeted for general, business and technical audiences. OEDM staff will be encouraged to join and participate in associations such as DAMA, AIIM, DGPO, etc. Manage data as an enterprise resource Data requests will be met with existing solutions where possible. Where none exist, they will be evaluated for potential future reuse. The initial data integration is in production and used to create data marts, and at least one master data domain is in production. Using data marts from the EDW and operational data, analysts and power users have access to multiple BI apps. 11 January 2018 Data Governance Framework Implementation Plan v2018 Page 19 of 41

20 Strategy Goal Objective c. Erwin is successfully used as the Enterprise Data Modeling platform by July 1, Redefine the roles of data-related analysts in the organization to better match analyst capabilities to organization needs. a. Data analysts and architects are recruited and hired to staff central Enterprise Business Intelligence and Enterprise Data Management teams to provide guidance to other analysts in the organization by July 1, b. Business Data Analysts are recruited and hired and attached to individual business units but guided by the appropriate central Enterprise Business Intelligence or Enterprise Data Management organizations by January 1, c. Research Analysts have disengaged from day-to-day data management responsibilities and have been refocused on research and evaluation activities by January 1, Evangelize and implement the DG Framework Define and implement roles & responsibilities Specific Tactics The OEDM is fully staffed with the proper skills to function as department Center of Excellence teams. For significant business units, the need for one or more business data analysts is identified and ratified by the business. Requests for data no longer go first to research units but are instead reviewed by the OEDM. Manage data as an enterprise resource The business architecture model (BAM) is maintained in Erwin and used to develop new applications. A staffing plan for the OEDM is formalized and positions have been created, posted, and filled. Business analyst positions have been created, posted, and filled. Principles (Martin, 2013) Page 20 of 41 Data Governance Framework Implementation Plan v2 11 January 2018

21 IMPLEMENTATION ACTIVITY PROGRAMS Implementation activities have been grouped into a set of logically related programs below to provide another perspective. For a chronological list of these activities, see the IMPLEMENTATION ACTIVITY TIMELINE. Executive Sponsorship Executive Management functions as DG Executive Board and approves policies and programs. Executive Management directs that units coordinate their data activities with the EDM Office. Training, Awareness, and Education EDM Office staff will deliver a series of trainings on data management topics on a regular basis targeted for general, business and technical audiences. A 30-minute session is presented to EM quarterly to reinforce DGF/IA. A business glossary, data dictionary, and data mart catalog is published and accessible by data consumers so they can understand what data is available and what it means. Office of Enterprise Data Management A staffing plan for the OEDM is formalized and positions have been created, posted, and filled. The EDM Office is fully staffed with the proper skills to function as department Center of Excellence teams. EDM Office staff will be encouraged to join and participate in associations such as DAMA, AIIM, DGPO, etc. Requests for Data The EDM Office will implement a process to receive, review, and fulfill all new requests for data. The DBHIDS.Request4Data@Phila.gov group box is activated. The EDM Office receives data request s, determines the attributes, triages requests promptly, and determines need for follow-up. A Request for Data form is created and deployed. Requests are logged and those requiring follow up activate more detailed review. Business management is taught to contact EDM when a new integration need is identified. Business units and analysts direct requests for data through the enterprise process. Data requests will be met with existing solutions where possible. Where none exist, they will be evaluated for potential future reuse. Business Data Analysts For significant business units, the need for one or more business data analysts is identified and ratified by the business. Business analyst positions have been created, posted, and filled. Business data analysts participate in data stewardship activities and document the data for which they are responsible. 11 January 2018 Data Governance Framework Implementation Plan v2018 Page 21 of 41

22 Information Security The Information Security Coordinator is identified and serves as a nexus for compliance, perimeter, and data security. Employees receive specific security training annually and cyber security reminders at least monthly. Data is stored in an RDBMS that supports data encryption. Employees understand the dimensions of confidentiality, integrity and availability and manage data accordingly. Data Management Practices Business and technology users acknowledge the need to create business process and data documentation before building anything. Analysts and system developers work with the Data Architect to define data formats. In conjunction with the request for data process, business data analysts know not to create new one-off reports and data integrations. The independent movement of batch files is eliminated. Data Management Technologies The Kalido DIW platform is in production and is the source for enterprise data analysis and external data consumers. The Kalido MDM platform is in production and providing MDM for Person, Provider, Service, and related reference data. The Qlik Sense business intelligence and data visualization platform is in production and providing access to the most requested data and measures. The enterprise business architecture model (BAM) is maintained in Erwin and used to develop new applications. Data Management Effectiveness The Web Focus environment is sunset and use of spreadsheets as personal data marts is reduced or eliminated. All pertinent department data has been integrated and documented. XML and JSON schemas are based upon the enterprise BAM attributes. Data analysts and key knowledge workers have access to the Qlik Sense platform and necessary applications. Using data marts from the EDW and operational data, knowledge workers, analysts, and power users have access to multiple business intelligence applications. KNOWLEDGE ACTION INFORMATION DATA Page 22 of 41 Data Governance Framework Implementation Plan v2 11 January 2018

23 IMPLEMENTATION ACTIVITY TIMELINE This table lists the strategic objectives in chronological order by Start/Target Date along with the Tactical Activities that are necessary for implementation. The color stratification represents six-month calendar periods ending on January 1 and July 1 of each year. Activity items in bold are primary activities while non-bold items represent supporting activities. Checkbox status is as of January 1, Obj # Strategic Objective 1.a All stakeholders receive access to regular continuing education sessions about Data Governance, Information Architecture, Data Management, and Business Intelligence. 1.b The executive management team understands the value of defining the data of interest to a business unit before building a process or report that uses data. 2.a A central Data Request address exists and is in use. 8.b Employees belong to and participate in data management professional organizations. 9.c Erwin is successfully used as the Enterprise Data Modeling platform. 2.b A standard set of questions to define a request is identified and is in use. 2.c A review and response process for requests is established and is in use. 3.a A DBHIDS Information Security Coordinator (ISC) is identified. 3.c Employees receive regular ongoing education about cyber security. Start or Target Date Starting 7/1/2017 Starting 7/1/2017 In place by 7/1/2017 Starting 7/1/2017 In place by 7/1/2017 In place by 8/1/2017 In place by 9/1/2017 In place by 10/1/2017 Starting 10/1/2017 Tactical Activity Multiple ninety-minute information management training sessions are presented annually to all stakeholders. A 30-minute session is presented to EM quarterly to reinforce DGF/IA. EM functions as DG Executive Board and approves policies and programs. EM directs that units coordinate their data activities with EDM. The EDM Office receives data request s. DBHIDS.Request4 is activated. OEDM staff will be encouraged to join and participate in associations such as DAMA, AIIM, DGPO, etc. The business architecture model (BAM) is maintained in Erwin and used to develop new applications. The EDM Office determines the attributes. A Request for Data form is created and deployed. The EDM Office triages requests promptly and determines need for follow-up. Requests are logged and those requiring follow up activate more detailed review. The ISC is identified and serves as a nexus for compliance, perimeter, and data security. Employees receive specific security training annually and cyber security reminders at least monthly. Employees understand the dimensions of confidentiality, integrity and availability and manage data accordingly. 11 January 2018 Data Governance Framework Implementation Plan v2018 Page 23 of 41

24 Obj # Strategic Objective 4.a An Enterprise Data Warehouse environment is created and is in use. 5.c Institutional knowledge and metadata about info assets is documented and published. 8.a Employees have access to online and in-person data management training. 3.b Database encryption has been deployed across all DBHIDS data stores where the capability exists. 4.b Enterprise Master Data Management is implemented around common data domains. Start or Target Date In place by 10/1/2017 In place by 10/1/2017 Starting 10/1/2017 In place by 1/1/2018 In place by 1/1/2018 Tactical Activity The Kalido DIW is in production and is the source for enterprise data analysis and external data consumers. A business glossary, data dictionary, and data mart catalog is published and accessible by data consumers. Business and technology users acknowledge the need to create business process and data documentation before building anything. Business data analysts participate in data stewardship activities and document the data for which they are responsible. EDM Office staff will deliver a series of trainings on data management topics on a regular basis targeted for general, business and technical audiences. To the extent possible, data is stored in an RDBMS that supports data encryption. The Kalido MDM product is in production and providing MDM for Person, Provider, Service, and related reference data. 6.b The Enterprise Business Architecture Model is leveraged to define the formats for real-time data exchange between systems. 7.b Processes for identifying new reporting needs have been developed and are in use. 9.a Kalido is successfully used as the Enterprise Data Warehouse and Master Data Management platform. In place by 1/1/2018 In place by 1/1/2018 In place by 1/1/2018 XML and JSON schemas are based upon the ELBM attributes. Analysts and system developers work with the Data Architect to define formats. The EDM Office will implement a process to receive, review, and fulfill new data requests. Data requests will be met with existing solutions where possible. Where none exist, they will be evaluated for potential future reuse. Business units and analysts direct requests for data through the enterprise process. The initial data integration is in production and used to create data marts, and at least one master data domain is in production. 10.c Research Analysts have disengaged from day-to-day data management responsibilities and have been refocused on research and evaluation activities. Starting 1/1/2018 Requests for data no longer go first to research units but are instead reviewed by the EDM Office. Page 24 of 41 Data Governance Framework Implementation Plan v2 11 January 2018

25 Obj # Strategic Objective 4.c All employees with the need have access to governed data using selfservice Business Intelligence tools. Start or Target Date In place by 4/1/2018 Tactical Activity The Qlik Sense product is in production and providing access to the most requested data and measures. 5.b Redundant independent data integration processes have been eliminated. 7.a QlikSense has been deployed throughout the organization. 9.b QlikSense is successfully used as the Enterprise Business Intelligence platform 10.a Data analysts and architects are recruited and hired to staff central Enterprise Business Intelligence and Enterprise Data Management teams to provide guidance to other analysts in the organization. In place by 7/1/2018 In place by 7/1/2018 Starting 7/1/2018 In place by 7/1/2018 The independent movement of batch files is eliminated. Business management is taught to contact EDM when a new integration need is identified. Business data analysts know not to create new one-off integrations. Data analysts and key knowledge workers have access to the Qlik platform and necessary applications. Using data marts from the EDW and operational data, analysts and power users have access to multiple BI apps. The OEDM is fully staffed with the proper skills to function as department Center of Excellence teams. A staffing plan for the OEDM is formalized and positions have been created, posted, and filled. The following activities overlap into the first half of FY 2019 are included below for planning continuity. 5.a Redundant independent reporting data stores have been eliminated. 6.a The Enterprise Data Warehouse is in use as the source for reporting and data exports. 10.b Business Data Analysts are recruited and hired and attached to individual business units but guided by the appropriate central Enterprise Business Intelligence or Enterprise Data Management organizations. In place by 1/1/2019 In place by 1/1/2019 In place by 1/1/2019 The Web Focus environment is sunset and use of spreadsheets as personal data marts is reduced or eliminated. A data mart catalog is available to users so that they understand what data is available. In conjunction with the request for data process, business data analysts know not to create new one-off reports. All pertinent department data has been integrated and documented. Business units and their analysts requests for data through the enterprise process. For significant business units, the need for one or more business data analysts is identified and ratified by the business. Business analyst positions have been created, posted, and filled. 11 January 2018 Data Governance Framework Implementation Plan v2018 Page 25 of 41

26 WORKS CITED Lucas-Conwell, F. (2006, December 4). Technology Evangelists: A Leadership Survey. Retrieved June 19, 2017, from Growth Resources, Inc: Manns, M. L., & Rising, L. (2004). Fearless Change: Patterns for Introducing New Ideas. New York: McGraw-Hill. Martin, C. (2013, January 14). The Business Motivation Model a Key Tool for Talking Business. Retrieved July 6, 2017, from EA Learning: Paolini, D. J. (2017). DBHIDS Data Governance Framework Strategic Plan version 2. Philadelphia: DBHIDS. Rouse, B. (2012, February 22). Professor in the School of Industrial and Systems Engineering and the College of Computing at GIT. (D. Gardner, Interviewer) White, W. (2014). Philadelphia Behavioral Health Services Transformation: Practice Guidelines for Recovery and Resilience Oriented Treatment. Bloomington: AuthorHouse Publishing. CREDITS Author: Daniel J. Paolini, DBHIDS Chief Information Officer With input and guidance from the DBHIDS Enterprise Data Management Team Barbara Bunkle Minky Kernacs Charles Parrish and the DBHIDS Data Stewardship Council Page 26 of 41 Data Governance Framework Implementation Plan v2 11 January 2018

27 APPENDIX A: THE OFFICE OF ENTERPRISE DATA MANAGEMENT The Office of Enterprise Data Management (OEDM) is led by the DBHIDS Chief Information Officer and consists of the following units/functions. Enterprise Data Governance Unit Enterprise Data Architecture Unit Enterprise Data Integration Unit Enterprise Business Intelligence & Analytics Unit Each of these units is described in more detail in Appendix B, Roles and Responsibilities. The mission of the OEDM is consistent with the DBHIDS Data Governance Mission Statement. The Mission of the Office of Enterprise Data Management In support of the DBHIDS population health mission we will define, cleanse and integrate the data needed to provide better clinical services, measure outcomes and make more informed policy decisions while reducing the cost of those activities. We will identify the core data in use across the department and establish a collaborative approach to managing it so as to provide information to those that need it while meeting the quality, format and content expectations of the organization. Our Motto is Our Vision PROCURATIO DATA EXCELLENTIAM (Excellence in Data Governance) DBHIDS Mission Statement The mission of the Department of Behavioral Health and Intellectual disability Services is to educate, strengthen, and serve individuals and communities so that all Philadelphians can thrive. (adopted January, 2017) DBHIDS Vision Statement We envision a Philadelphia where every individual can achieve health, well-being and self-determination. (adopted January, 2017) Data Governance Mission Statement Our mission is to define and manage a quality data resource that enables DBHIDS to educate, strengthen, and serve individuals and communities to achieve its mission. Data Governance Vision Statement We envision an information architecture that provides secure, defined, quality data whenever and wherever needed in a costeffective and repeatable manner so that DBHIDS can realize its vision. 11 January 2018 Data Governance Framework Implementation Plan v2018 Page 27 of 41

28 APPENDIX B: DATA GOVERNANCE ROLES AND RESPONSIBILITIES For additional information, see the MAPPING OF RESPONSIBILITIES TO ROLES in Appendix D. Data Governance Bodies and Their Roles and Responsibilities Ultimately, data governance is a business responsibility. It must be endorsed by senior executive management. It must be led by executive management. It must require involvement by business unit representatives to make sure that data is properly identified and defined, made fit for purpose and used appropriately. While the Data Governance Office and its responsibilities as the facilitator, enabler and implementer of data governance sits within the Office of the CIO, the responsibility for enterprise data governance resides with executive management. Enterprise Data Management follows from Enterprise Data Governance. These bodies must be established and become sustainable components Executive Management Core The Executive Management Core (EMC) team establishes the business vision, mission and priorities of the Department. It resolves data governance and data management priorities against other organizational priorities. Led by the Commissioner, its members serve on the Data Governance Executive Board for DBHIDS. Data Governance Executive Board The Data Governance Executive Board (DGEB) is responsible for approving data governance and data management policies, resolving data governance issues presented by the Data Stewardship Council and prioritizing enterprise data management efforts and initiatives. It is led by the Commissioner and consists of the members of the Executive Management Core team but may include others as designated by the Commissioner. It serves as the de facto executive sponsor of enterprise data governance and management initiatives and is supported by OEDM staff. Data Stewardship Council The Data Stewardship Council (DSC) is responsible for defining enterprise data, developing data management policies, and guiding enterprise information architecture efforts. Ultimately, its responsibility is to help the Department improve its data quality. The DSC is led by the CIO/Enterprise Data Governance Officer or designee and consists of the enterprise Business Intelligence and Analytics director, the Department s Data Architects, the Master Data Stewards, and Data Ambassadors from Department business units and systems. It is also open to analysts from the Department s various analytic and IT development units as well as OEDM staff. Data Architecture Council The Data Architecture Council (DAC) is responsible for documenting enterprise data, developing data management policies, and guiding enterprise data management efforts. It directs efforts to improve data quality that have been identified by the Data Stewardship Council. Its responsibility is to help the enterprise improve the dimensions of data quality, accessibility, security, completeness, timeliness, and value. The DAC also plans and coordinates enterprise data management initiatives. Page 28 of 41 Data Governance Framework Implementation Plan v2 11 January 2018

29 The DAC is led by the CIO/Enterprise Data Governance Officer or designee and consists of the enterprise Business Intelligence and Analytics director, the enterprise Data Integration manager, the Department s Data Architects, and the Technology, Development, and Support director. 11 January 2018 Data Governance Framework Implementation Plan v2018 Page 29 of 41

30 Enterprise Data Management Units and Their Roles and Responsibilities The Office of Enterprise Data Management is the focal point for information architecture and data management for DBHIDS. Reporting to the CIO, it is comprised of units for the enterprise activities of data management, architecture, governance, integration and business intelligence. Each unit has a designated lead employee. The role of Data Security Coordinator transcends the Office of Enterprise Data Management and has responsibilities in the traditional application development and network administration realms. Data Governance Unit The Data Governance Unit (DGU) is the unit responsible for executing data governance policies and supporting the Data Stewardship Council. The DGU is coterminous with the Office of the Chief Information Officer (OCIO), which provides staff support. All new data initiatives will be submitted for review by the DGU as well as Information Security and IT Strategic Planning. The review intensity will be based upon the initiative s risk, size/cost, cross-unit impact and potential to advance department objectives. The review will determine compliance with architecture and standards and identify any need for a waiver from them. Enterprise Data Governance Officer The DBHIDS Chief Information Officer (CIO) or designee serves as the Enterprise Data Governance Officer (EDGO) for the Department. In this role, the EDGO is responsible for ensuring that enterprise data governance policies and practices are followed. The EDGO is responsible for the proper application of the enterprise information and data architecture principals and the overall quality and usability of enterprise data assets. The EDGO or his or her designee leads the Data Stewardship and Data Architecture Councils. Data Architecture Unit The Data Architecture Unit (DAU) is the unit responsible for maintaining the Enterprise Business Architecture Model, reviewing application logical solution models for consistency, overseeing the maintenance of the enterprise business glossary and other metadata resources and for the proper application of enterprise data management policies, standards and practices. The DAU is also responsible for the design of data warehouse subject area models and Master Entity and Master Reference Data processes maintained by the Data Integration Office. The DAU is led by the Enterprise Data Architect (EDA). Enterprise Data Architect The Enterprise Data Architect (EDA) is the individual responsible for advancing the role of data architecture within the DBHIDS business and IT communities. The EDA shall guide the efforts of data architects and analysts within the Office of Enterprise Data Management and the business units in the development of conceptual and logical business models, and logical solution and physical data models. The EDA shall be responsible for overseeing the modeling of master data and the design of master data management processes that will be maintained by the Data Integration Office. The EDA reports to the CIO and is a member of the Data Stewardship and Data Architecture Councils. Page 30 of 41 Data Governance Framework Implementation Plan v2 11 January 2018

31 Data Architect A Data Architect (DA) is an individual assigned to an IT project, business unit or subject area with the responsibility to develop a logical solution model, a business subject area model or a dimensional model consistent with the Enterprise Business Architecture Model. Data Architects receive guidance, either directly or in a dotted-line relationship, from the Enterprise Data Architect. DAs serve on the Data Stewardship and Data Architecture Councils. Data Integration Unit The Data Integration Office (DIU) is the unit responsible for integrating and maintaining DBHIDS and external data sources, supplying data to enterprise data marts, business unit data marts and external partners, and managing enterprise master entity data and master reference data. The DIU implements source system integration models provided by the Data Architecture Unit and data reporting and analysis models provided by the Business Intelligence and Analytics Unit. Manager, Data Integration The Manager of Data Integration (MDI) is the individual responsible for advancing a rational data integration approach within the DBHIDS business and IT communities for the purpose of developing reusable data resources. The MDI shall be responsible for the integrity of data integration processes and for the measurement of data quality against the expected baseline. The MDI serves on the Data Architecture Council Business Intelligence and Analytics Unit The Business Intelligence and Analytics Unit (BIAU) is the unit responsible for determining the information needs of knowledge workers and other data consumers, developing appropriate data reporting structures to meet those needs and providing them to the Data Integration Unit, and implementing self-service reporting, analysis and visualization (business intelligence) tools so they are available throughout the organization. Director, Business Intelligence and Analytics The Director of Business Intelligence and Analytics (DBIA) is the individual responsible for supporting a data-driven organization by identifying appropriate subject areas for reporting and evangelizing and implementing ubiquitous business intelligence capabilities that leverage those subject areas. The DBIA is responsible for operationalizing key performance indicator that are identified by business management and providing reporting solutions for them.. In addition to the above units and supervisors, enterprise data management will also include: Data Security Coordinator The Data Security Coordinator (DataSec) is responsible for identifying data security policies that need to be followed throughout the organization, making implementation recommendations and advising the Data Governance Office. The DSC will work in cooperation with network security and HIPAA privacy officials to coordinate data protection and address the dimensions of Confidentiality, Integrity, and Availability of data for only those entitled to access it. 11 January 2018 Data Governance Framework Implementation Plan v2018 Page 31 of 41

32 Other Data Governance Roles and Responsibilities Data Stewardship Organization The Data Stewardship Organization (DSO) is the organization responsible for the definition and use of data collected or maintained within its functional areas. A data stewardship organization is responsible for developing policies specifically related to the use of the data, and for designating data ambassadors to implement and enforce those policies. The Data Stewardship Organization may also be the Data Owner or the Data Custodian, but not always (or even usually). For Master Data domains, the DSO is the Master Data Domain Committee. Master Data Steward Each Master Data Domain (MDD) will be managed by one or more Master Data Stewards (MDS), typically a Business Data Analyst. An MDS represents the business unit that contributes some or all of the authoritative data in a MDD. The MDSs are core members of the Data Stewardship Council. The MDSs for a MDD comprise the Master Data Domain Committee for that domain, serving as its Data Stewardship Organization. The MDS is as core member of the Data Stewardship Council. Chief Data Steward Each division or significant administrative unit will designate a Chief Data Steward (CDS), typically a Business Data Analyst. Each CDS represents the data for which his or her unit is the data stewardship organization. In addition, the CDS works with other data stewards to guide master data management efforts around data sets related to their data needs. The CDS is a core member of the Data Stewardship Council. Data Ambassador The Data Ambassador is the individual within the Data Stewardship Organization identified as the point of contact for questions about the definition and use of data collected or maintained by the DSO within its functional areas and for the documentation of that data in a metadata registry. It is often a Business Data Analyst. On behalf of the data stewardship organization, a data ambassador follows and/or implements policies, procedures, and guidelines that pertain to the data during the lifecycle of that data entrusted to his or her stewardship, and participates as a member of the Data Stewardship Council for enterprise data governance issues as well as representing his or her Data Stewardship Organization. The same data may have multiple data ambassadors; e.g. technical, business, collection, publication, etc. Subject Matter Expert The Subject Matter Expert is an individual within a business unit or the analyst assigned to the business unit that has the most complete understanding of a particular subject area or process. The Subject Matter Expert is engaged to make sure that the right data is being used appropriately to meet business needs for both transaction processing and reporting purposes. A Subject Matter Expert may also be a Data Ambassador and/or a Knowledge Worker, but does not have to be. Page 32 of 41 Data Governance Framework Implementation Plan v2 11 January 2018