An RCG White Paper The Data Governance Maturity Model

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1 The Dataa Governance Maturity Model This document is the copyrighted and intellectual property of RCG Global Services (RCG). All rights of use and reproduction are reserved by RCG and any use in full requires the advance written consent of RCG, all quotations and partial usage allowed providing the source is credited.

2 ABSTRACT Data governance is increasingly getting attention from companies involved in business intelligence and service oriented architecture projects, but some companies have strategically been pursuing data governance for many years already. While recognizing the different levels of data governance maturity among firms, our intent with this paper is two fold: To pique interest in data governance for those who are not yet practicing it, To assist those companies which have been struggling with attaining the benefits expected when they began their data governance programs. Recognizing the value of data governance, companies may want to assess where they are in the data governance maturity model, if their IT and business units are aligned, and what they must do to attain the next level of maturity and realize the associated benefits. This may take some rethinking of their business opportunities and corresponding IT strategy. Companies that have not embarked upon a data governance practice may use the information in this paper to assess their readiness to embrace data governance RCG Global Services, Inc. All Rights Reserved. Proprietary and Confidential. Page 1

3 An RCG White Paper SUCCESSFUL DATA GOVERNANCE IS CONSTRUCTED OF POLICIES, GUIDELINES, AND STANDARDS Data governance is not just a collection of ad hoc data qualityy projects, but the development and integration of a set of rules policies, guidelines, and standards for managing the corporation s data. It is implemented by a data governance management team of information technology and business associatess who are unified by a common goal to ensure that: Data is what it is supposed to be (Data Quality) Data is in the correct context (Data Integrity) Data and its associated metadata are accessible (Dataa Usability) The data governance structure is formed to ensure that authority to manage data is properly delegated from the senior most levels of the firm to the appropriate parties, and that these parties are held accountable for executing governance policies as required byy their respective mandates. This authorization, in turn, is accomplished by instituting processes to understand the cause and effect of poor data in an organization. Then solutions can be developed to correct the problem and a means for monitoring and evaluating the implementation of these solutions can be adopted. SUCCESSFUL DATA GOVERNANCE IS A TEAM EFFORT In addition to the necessary structure, successful data governance also requires a team structure a coordinated effort among executives, governors, and stewards, as shown in Figure 1. Figure 1: Data Governance Team RCG Global Services, Inc. All Rights Reserved. Proprietary and Confidential. Pag e 2

4 EXECUTIVE LEADERSHIP The role of executive leadership, or the leadership committee, is to support, lead, and fund the governance program. The leadership committee establishes priorities and ratifies governance principles. Perhaps most importantly, the committee approves the data governance strategy/charter, and reviews the governance team to ensure it executes to the priorities of the company s strategy. GOVERNANCE TEAM The governance team ensures the availability of processes and infrastructure it analyzes costs and monitors, tracks, and reports on progress toward goals and objectives, all with a focus on coordinating tactical delivery of the resolution of data issues and challenges. During the early stages of implementing governance, organizations typically establish a data governance manager. This manager s role is responsible to facilitate and communicate: Facilitate processes to resolve issues/conflicts that prevent integration and/or share ability of data/information Facilitate inclusion of business/data analysis into software development life cycle [SDLC] Facilitate empowerment of stewards Communicate recommendations (including impact analysis) to the leadership committee Communicate progress and benefits to operations Communicate involvement in business planning Over time, depending on the size, organizational structure, globalization, and culture of the company, the firm may establish additional data governors to assist the manager as appropriate. STEWARDSHIP AND QUALITY MANAGEMENT Supporting the governance manager and team are stewards. They educate developers and endusers on the data standards and the importance of data quality, and they take on the responsibility to ensure compliance via their role in developing and enhancing projects and resolving issues. There are three categories of stewards: Business stewards are Subject Matter Expert s [SME s] who represent the interests and needs of the functional units of the business (e.g., marketing) and who define the business rules for their associated area RCG Global Services, Inc. All Rights Reserved. Proprietary and Confidential. Page 3

5 Application stewards, or systems stewards, are SME s who know and understand each application or system (e.g., POS or data warehouse) and who declare the business rules associated with that application Quality stewards are IT SME s who are responsible for procedures and processes that ensure the quality of data (e.g., data profiling, maintaining LOV s) and who define the business rules by which quality is established and/or measured In addition to the quality stewards, IT also provides the data architecture team that supports the data governance program. The data architecture team consists of the data architecture manager, data architects, and data analysts who prepare and maintain the artifacts (e.g., data model, metadata, etc.) that support the governance processes. DATA GOVERNANCE VERSUS MASTER DATA MANAGEMENT All businesses have a set of common data, which is referred to as the master data. Businesses with large heterogeneous and distributed IT landscapes often encounter the challenge of having the same master data customer names, product numbers, or vendor billing information duplicated across multiple systems and locations within the organization. This produces data redundancies and inconsistencies which adversely affect the quality of information. Master data management, or MDM, is the practice of utilizing data services to consolidate, cleanse, and harmonize master data to avoid data redundancies and inconsistencies, thus controlling the maintenance and distribution of data across the enterprise and ensuring data quality and integrity. The goals and objectives of MDM are primarily the same as those of data governance, so, in many cases, organizations begin their data governance program with a master data management focus. It s important, however, to realize that data governance programs extend beyond the realm of what is considered the set of master data. MDM is a multidisciplinary project that typically involves business process analysis, data assessment, data cleansing, data consolidation, data reconciliation, data migration, and development of a master data layer. In contrast, data governance principles dictate that ownership and accountability for the quality of data must reside with the business owners. The focus of data governance is on resolving data issues at the source of the problem, relegating MDM to the strategic levels of the organization and embedding data quality metrics (key performance indicators, or KPI s) into performance measurements. In short, data governance provides the framework for the intersection of IT and RCG Global Services, Inc. All Rights Reserved. Proprietary and Confidential. Page 4

6 An RCG White Paper business working together to establish confidence and credibility in the enterprise s information, while MDM is a subset of a data governance program. THE DATA GOVERNANCEE MATURITY MODEL Just as there is a capability maturity model for software development processes, there is also a maturity model for data governance. In this model, seven layers of maturity define the growth cycle and associated benefits that can be attained by organizations as they embrace the principles, policies, procedures, and standards of a well defined data governance program. (Each stage is described in detail on the following pages, and illustrated in Figure 2.) The first three layers take an average of two years to fully implement, while the remaining four layers are totally dependant on the organization. Factors such as mergers and acquisitions, changes in executivee management which affect a company s culture, and the willingness of management to embrace governance, play a determining role in the length of time required to progress from stage four to stage five and from stage five to stage six. Reaching the top tier stage seven does not rely on tasks or procedures that are put in place as in prior stages, but is more of an overall organizationa al adaptation or acclimation to dataa governance. Figure 2: The Seven Stage Data Governance RCG Global Services, Inc. All Rights Reserved. Proprietary and Confidential. Pag e 5

7 STAGE 1: STRATEGY AND FRAMEWORK Strategy A data governance strategy aligns the goals and objectives of the governance program by defining how the guidelines, principles, and procedures will ensure that the organization has a way to identify the cause and effect of data issues, and a way to determine a solution to resolve these issues. The strategy includes a charter stating how the data governance team will develop and deploy integrated business and technical support policies, guidelines, and standards to manage the organization s data assets. The charter further defines the roles and organizational structure that responsively and proactively focus on ensuring data integrity, including IT development of standards for technical blueprints (models), databases, and automated procedures that standardize data collection and distribution. Framework The framework defines the roles and responsibilities of the Leadership Committee, Data Governance Manager(s), the Stewards, which together comprise the data governance team. The framework also defines the relationships and dependencies between the data governance team and data architecture. Until the strategy and framework are implemented, there are no realized organizational benefits from stage one. STAGE 2: SCENARIOS & VALIDATION To test its data governance strategy and framework, an organization will typically take an existing data issue and follow the reactive process to determine the cause and effect of the issue, and then propose a solution. In conducting this exercise, associates of the organization act out the roles as defined in the framework to see how well the process fits the organization s culture. The results of the exercise provide a means to refine the processes and framework to best fit how the organization operates in its day to day functions. In addition, associates will learn the variances in the roles of governance and stewardship, as well as the communications steps involved, and how best to formulate those steps (e.g., via e mail or telephone). The exercise helps the organization determine the appropriate individuals to play these roles moving forward. Stage two offers a proof of concept approach that provides the opportunity to leverage industry best practices while adapting to the nuances of the organization. Individuals begin the learning curve while applying the processes to real issues. In conducting the exercise, some value in resolving data issues can be attained RCG Global Services, Inc. All Rights Reserved. Proprietary and Confidential. Page 6

8 STAGE 3: FORMALIZED ORGANIZATION AND RESPONSIVE PROCESS ROLLOUT As the responsive process is being played out, the roles of data governance (e.g., data governance manager or business steward) are formally defined within Human Resources, where appropriate new positions within the organization are created and filled. We recommend that these roles be filled with internal resources having the required skills and aptitude to carry out the responsibilities as defined in the framework. As these roles are filled, the responsive process can then be rolled out enterprise wide in full force. The company now has a structured approach to bridge business and IT through a common goal, providing accountability for establishing and maintaining data quality. In addition, as the roles of stewards, data architects and data analysts are incorporated into development/enhancement projects, companies typically experience project improvements in meeting deadlines and budgets, as well as enjoying higher satisfaction rates with the application s usability. STAGE 4: PROACTIVE PROCESS ROLLOUT As the governance team members become proficient in identifying the causes of data issues, they also become more aware of the things that can be done to prevent data issues from occurring in the first place. As this occurs, the proactive process becomes invoked to do just that stop the data issue from occurring before it happens. At this stage, stewards take on the responsibility to identify business events or activities that trigger the process. Organizational process improvements and enhanced communication between business units and IT are some of the benefits noted as organizations advance to the proactive data governance model. Identifying the things that interact when a transaction occurs enables business people to collaboratively manage data in a workflow driven environment RCG Global Services, Inc. All Rights Reserved. Proprietary and Confidential. Page 7

9 STAGE 5: EXPANDED BUSINESS INVOLVEMENT Reaching this stage is marked by the explicit buy in from key stakeholders and executive management in the data governance program. Priorities have been established to aid in difficult decisions such as those involving trade offs between security, utility, and cost in resolving data issues. Standards compliance monitoring is established and reported as part of performance measurement. The alignment of dataspecific technology, processes, and organizational components with the company s most important business objectives is developed, as well. Continuous improvement efforts are enabled by the establishment of measurable metrics that are monitored and reported on a timely basis. Companies that utilize project management offices (PMO) incorporate data governance activities and work products to enhance their SDLC methodology, resulting in improved project performance. The organization now has reusable information/documentation, which effectively reduces future project efforts and reduces the cycle time of the reactive and proactive resolution processes, making data governance more efficient. STAGE 6: STEWARDSHIP CULTURE Governance protocols include procedures across divisions and departments that explicitly reconcile priorities, expedite conflict resolution, and build cooperation in support of data quality as a common objective shared at every level of the enterprise. Data quality education and awareness programs are an integral part of the organization s in house on going employee training programs. There is a common focus and delivery throughout the organization that empowers the workforce. Every corporate associate considers himself/herself a data steward, and this perspective carries beyond the boundaries of the organization and extends to the organization s partners in data sharing, data integration, and enabling of service oriented architecture RCG Global Services, Inc. All Rights Reserved. Proprietary and Confidential. Page 8

10 STAGE 7: STRATEGIC GOVERNANCE At this stage, the organization transforms data governance and compliance from time based audits to real time, change driven, on demand business processes that continually assess risks, update policies, and manage resources across the enterprise. Ultimately it is the organization s people, processes, and technology working together organically and autonomically that result in an effective data governance program. Utility of information can now drive flexibility and agility of the organization. CONCLUSION There is a tendency in organizations to be complacent about data quality and integrity issues, as though these issues are things one just has to deal with. This is at odds with the increasing demands of government regulations such as Sarbanes Oxley and Basel II, which dictates that data management must address any quality / integrity issues that could compromise credibility of the organization s information. Enterprises must employ a cross company control model to govern how information is used, to promote the security and integrity of all data, and to protect privacy on both the individual and the corporate level. An organization s data governance policies and procedures must also balance effective information access with appropriate use of the information. However, a data governance program is not an application that can be purchased, installed, and implemented. It is not a project that has a timeline for completion. It is a program that, over time, affects the culture and the way an organization conducts business. ABOUT RCG GLOBAL SERVICES RCG Global Services (http://www.rcggs.com/) has a rich history of providing professional services to the Global 1000 marketplace. Our industry expertise allows us to engage business executives in discussions about specific business initiatives. Our repeatable blueprint to affect business change typically involves the application of technology. Our technology design and implementation follows a rigorous predictable approach that ensures a customized solution tailored to the client. We are committed to helping our clients define and realize ideas and are CMM certified enterprise wide. Each Business Consulting offering follows a consistent, proven approach to developing strategy and conducting analysis that leads to a custom fit implementation roadmap. Technology solutions are based upon repeatable processes and practices applied to Development & Integration, Business Intelligence & Data Delivery, QA & Software Testing, Application Management and Project Management. RCG serves 360 clients and 7 of the Fortune 10 across a range of markets, with special focus on the insurance, retail, healthcare, entertainment & hospitality, and energy & utilities industries. RCG is based in Edison, New Jersey, with 12 offices nationwide, an Offshore Delivery Center in the Philippines that is assessed at SW CMMI Level 5, and a global recruiting engine RCG Global Services, Inc. All Rights Reserved. Proprietary and Confidential. Page 9

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