by John Walton Senior Delivery Manager, 972-679-2336 john.walton@ctg.com Data Governance: The Lynchpin of Effective Information Management Data governance refers to the organization bodies, rules, decision rights, and accountabil ities of people and information systems as they perform information-related processes. Abstract: As the importance of information quality has become more widely accepted during the past decade, methodologies, roles and responsibilities, and tools have been developed and adopted by organizations committed to data analytics initiatives. These have collectively become known as data governance, and its ultimate objective is the management of information as a strategic corporate asset. Although data governance initiatives normally begin as a component of data analytics implementation efforts, they must span the enterprise to be truly effective. Successful data governance strategies are almost universally implemented in a top-down fashion with clear support and direction from the executive leadership team. Grassroots or bottom-up initiatives are rarely successful as they quickly run into issues of ownership, accountability, and territorial battles that cannot be readily resolved by middle management. This white paper provides an overview of many data governance concepts, such as master data management and operational data management. It describes the roles, responsibilities, and business processes, related to data ownership and data stewardship, which are instrumental to successful data governance initiatives. Defining Data Governance There are many definitions of data governance, but the Data Governance Institute s version most concisely conveys the necessary elements: Data governance refers to the organization bodies, rules, decision rights, and accountabilities of people and information systems as they perform information-related processes.
The primary goal of data governance is to enable the management of an organization s information as a strategic corporate asset. According to a 2004 PricewaterhouseCoopers survey of CIOs in the U.S., Great Britain, and Australia, information represents 37 percent of the overall value of an organization; yet, it is rarely managed effectively. In healthcare organizations, the value of information is arguably much higher. Data governance has three key objectives intended to ensure greater accountability for information quality, as well as more consistent definitions and business rules for information management. These are to: Reactively Resolve Issues Proactively Identify Issues Manage Information as a Strategic Asset Enforce Standards Figure 1: Data Governance Objectives Data governance establishes roles and responsibilities to ensure consistency of data management standards, which helps improve this invaluable metadata. This also leads to end users having a much higher level of confidence in Proactively Identify Issues: Data quality issues are too often identified upstream when an executive or stakeholder questions the information contained in a report or dashboard. Extensive effort is then required to trace back to the root cause of the issue, only to find that data was entered incorrectly or it was transformed in some way using invalid business rules. An effective data governance program includes methodologies to identify data quality issues before they become visible and costly. Reactively Resolve Issues: Many organizations have weak business processes in place to remediate data quality issues. Far too often, the Information Technology (IT) department is held accountable, when the actual causes of the problem are poorly defined business rules, inconsistent data definitions, or undocumented and unapproved workflows. Data governance ensures that well-documented workflows are established, and business stakeholders are held responsible for data quality with support from IT. Enforce Standards: Many data quality issues are caused by the lack of consistent data definitions and business rules. Data governance establishes roles and responsibilities to ensure consistency of data management standards, which helps improve this invaluable metadata. This also leads to end users having a much higher level of confidence in the information they use to make business decisions. Data Quality Methodology A best practice data quality methodology consists of three high-level processes, which together help to proactively identify and reactively resolve issues. Identify the information they use to make business Monitor Remediate decisions. Figure 2: Data Quality Methodology Identify: The first step to improved data quality management is to profile the data and establish a baseline, which typically includes: Metadata Validation: Does the data in tables actually match its definition? Pattern Analysis: Is the data in a consistent format? Frequency Counts/Outlier Detection: What percentage of data is incorrect? Business Rule Validation: Does the data comply with the organization s business rules? 2
Remediate: Whether issues are proactively identified by data profiling, reactively identified during the Extraction/Transformation/Load (ETL) process (also known as the data warehouse data acquisition process), or discovered by an end user while reviewing a report, a well-defined workflow must exist to assign issue responsibility and track its resolution. The remediate process ensures that issues are correctly assigned, tracked, and escalated when necessary. It is an important point to emphasize here that data quality issues must be resolved in the source application rather than in the data warehouse or data marts. As a general rule, data can be flagged as suspect or invalid in the data warehouse, but should not be remediated there. Absent this, data will be repeatedly cleansed during each load cycle, leading to inconsistent information in the data warehouse and raising issue as to which system is correct. Monitor: The monitor process ensures that issues are quickly identified by establishing a series of trigger points. For example, the ETL process should contain error handling routines that automatically send a message when an issue is encountered. A series of data validation routines should also be established to continually monitor information quality. Finally, a data quality dashboard should be developed to measure ongoing effectiveness of the data governance program. Master Data Management and Operational Data Management The final two data governance overview concepts presented in this white paper include master data management and operational data management. Figure 3 depicts the key distinction between these two categories of information. Provider PCP Provider Master data is common, shared enterprise-wide reference data. It is inherently non-transactional in nature. For example, Figure 3 depicts three types of master data:, PCP Provider, and. Other types of master data, not depicted, include Provider Procedure, Diagnosis, or Department. Operational data in Figure 3 includes Provider,, and Provider, each associated with two master data types. Figure 3: Master and Operational Data Type Examples Several years ago, focus was placed on managing master data quality by implementing tools, organization structures, roles and responsibilities, and workflows. This approach made a great deal of sense as transactional data, such as Visit, is largely composed of various types of master data. However, Visit also contains non-master data, such as the date Visit was scheduled or occurred. Management of master data and operational data falls on designated enterprise resources as organizations embark on adopting data governance initiatives. Data Governance Roles and Responsibilities In order to successfully implement enterprise-wide data governance strategies, it is important to carve out key roles with designated responsibilities. These roles include: Data Owner, Business Data Steward, Technical Data Steward, and Gatekeeper. Other resources, such as Data Architects, ETL Architects/Developers, and Business Analysts, are also involved in data governance, but the scope of this white paper is limited to the description of the four primary roles. 3
As stated above, these resources are also instrumental in the management of master data and operational data. Briefly, master data management is the responsibility of Data Owners and Data Stewards within an organization. Management of operational data requires close collaboration between multiple master data owners to resolve data quality issues and to approve data definitions and business rules. It should be noted that these key subject matter experts almost inevitably have many other assignments. Therefore, it is often necessary to hire additional resources to assume some of their current job responsibilities. Many data governance initiatives fail because organizations overload their most valuable resources with added data stewardship tasks. Data Stewardship Roles Data Owner Business Steward Technical Data Steward Gatekeeper Data Owner Data Owners are typically director-level or above executives who have full accountability for one or more types of master data. Their primary responsibility is to determine the appropriate solution to data quality issues based upon recommendations from their supporting Business and Technical Data Stewards. They approve recommended data definitions and business rules for ensuring data quality. They also approve business rules for transforming data and aggregating information in the data warehouse, as well as approve data access privileges. Another important responsibility of the Data Owner is to work closely with other Data Owners to manage operational data. Using /PCP Provider (master data) described above and depicted in Figure 3, the Data Owners for, PCP Provider, and must collectively determine the business rules and workflows to ensure that the, Provider, and Provider relationships are correctly defined and implemented. Again, it is important to note that majority of the detailed analysis required to make these decisions is the responsibility of the Business and Technical Data Stewards. However, it is the role of the Data Owners to consider recommendations and make the final decisions. Business Data Steward Responsibilities Ultimately responsible for quality of master data types Coordinates efforts of Business and Technical Data Stewards Approves data definitions and business rules (metadata) Monitors quality of master data Improves business processes Prepares data definitions and business rules Determines data quality solutions Performs data profiling Implements data quality solutions Performs impact analysis Logs data quality issues and assigns them to the responsible Business Data Steward Monitors status and generates weekly reports Escalates issues to Data Owner when necessary Figure 4: Data Governance Roles and Responsibilities The single most important role in a data governance program is the Business Data Steward. The Business Data Steward is a subject matter expert in a specific domain or knowledge area. In this role, the primary responsibility is to support the Data Owner. Business Data Stewards 4
are responsible for determining optimal solutions to data quality issues, which could be program code fixes or changes to business processes. Preparation of data definitions and business rules for data quality, data transformation, and aggregation is also a key responsibility of the Business Data Steward. It should be noted that these key subject matter experts almost inevitably have many other assignments. Therefore, it is often necessary to hire additional resources to assume some of their current job responsibilities. Many data governance initiatives fail because organizations overload their most valuable resources with added data stewardship tasks. Technical Data Steward About the Author John Walton is a healthcare information management strategist with 30 years of IT and consulting experience at leading health information technology and consulting firms. His 20 years of project management experience includes 15 years of managing data warehousing, business intelligence, and data governance engagements at academic medical centers, health plans, IDNs, and pharmaceutical companies. For more information Michael Garzone Solutions Director, 972-530-5755 michael.garzone@ctg.com John Walton Senior Delivery Manager, 972-679-2336 john.walton@ctg.com The two primary responsibilities of the Technical Data Steward are to proactively identify data quality issues using data profiling tools, such as Data Insight, and to implement program code fixes that have been approved by the Data Owner. They are also responsible for using the impact analysis feature of the metadata management tool to identify the tables, reports, and code modules affected by planned database changes. Gatekeeper Despite the part-time nature of this role, the Gatekeeper fulfills an important need to monitor the status of data quality issues. When issues are identified through data profiling, ETL errors, or other means, they must be logged, assigned to the responsible Business Data Steward, and tracked until they have been resolved. The Gatekeeper provides the Data Owners with weekly reports on unresolved issues, and escalates issues that are not being addressed in a timely manner. Summary This white paper addresses the primary goal and objectives of data governance, a recommended data quality methodology, and key concepts such as master data management and operational data management. Roles and responsibilities for data ownership and data stewardship are also explained. As organizations plan to adopt enterprise-wide data governance strategies, they must consider two important points. First, organizations will most certainly require additional resources to effectively implement a successful data governance initiative. It is simply not possible to assume that the additional burden of data stewardship responsibilities can be placed on existing subject matter experts and technical resources who are already assigned to other strategic initiatives. Second, it is critical to ensure C-level adoption and buy-in. Senior leadership must continually reinforce the importance of the initiative throughout the organization. Bottom-up or grassroots efforts to implement data governance are far less likely to succeed than strong top-down approaches with unwavering support from the highest levels of the organization. 5