September 10, 2007 Data Governance: What Works And What Doesn t. by Rob Karel for Information & Knowledge Management Professionals

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1 September 10, 2007 Data Governance: What Works And What Doesn t by Rob Karel for Information & Knowledge Management Professionals Making Leaders Successful Every Day

2 September 10, 2007 Data Governance: What Works And What Doesn t by Rob Karel with J. Paul Kirby, Boris Evelson, Connie Moore, and Jamie Barnett EXECUTIVE SUMMARY The demand for trusted information continues to spiral upward, driven not only by investments in customer relationship management (CRM), enterprise resource planning (ERP), supply chain management, business intelligence, and data warehousing, but also next-generation strategic initiatives in master data management (MDM) and information-as-a-service (IaaS). The cross-enterprise complexity of these emerging business requirements have led to an obvious yet often ignored fact: Technology is only part of the solution. Until you understand and embrace data governance, your information management initiatives won t deliver their promised value. Effective governance requires defining organizational roles and responsibilities, including ownership of the processes and policies by which information is captured, maintained, and consumed. Unfortunately, no single governance model works for everyone, so your first challenge will be to develop the unique governance model that will be successful for your unique organization. TABLE OF CONTENTS An Evolving Need For Data Governance Has Emerged Case Studies Prove That There Are Multiple Roads To Success Let Others Governance Programs Inspire But Not Dictate Your Own RECOMMENDATIONS Design Your Governance Based On Company Culture WHAT IT MEANS 15 Data Governance Will Evolve Into Information Governance NOTES & RESOURCES Forrester performed questionnaire-based interviews with four user companies and discussed data governance best practices with five vendors: Business Objects, DataFlux, IBM, Informatica, and Trillium. We also solicited input from more than 40 additional customers through inquiries and consulting engagements in the past 12 months to research the range of interest, adoption, and approaches to data governance. Related Research Documents Trends 2007: Governance, Risk, And Compliance April 9, 2007 Ouch! Get Ready Spreadsheets Are Here To Stay For Business Intelligence April 2, 2007 Introducing Master Data Management November 10, , Forrester Research, Inc. All rights reserved. Forrester, Forrester Wave, RoleView, Technographics, and Total Economic Impact are trademarks of Forrester Research, Inc. All other trademarks are the property of their respective companies. Forrester clients may make one attributed copy or slide of each figure contained herein. Additional reproduction is strictly prohibited. For additional reproduction rights and usage information, go to Information is based on best available resources. Opinions reflect judgment at the time and are subject to change. To purchase reprints of this document, please resourcecenter@forrester.com.

3 2 Data Governance: What Works And What Doesn t AN EVOLVING NEED FOR DATA GOVERNANCE HAS EMERGED Data governance is by no means a new concept. Information and knowledge management (IKM) professionals have struggled with quality and confidence in data for most of computing history. What has changed in the past decade is the realization that data not only has value within the application and user community that captures it, but across the organization as well. This cross-functional and cross-architectural demand for the same data is forcing both business and IT leaders for the first time to recognize the dependencies and conflicting perceptions of the data s value. Many information managers are banking on data governance to mitigate these rising conflicts (see Figure 1). Forrester defines data governance as: The process by which an organization formalizes the fiduciary duty for the management of data assets critical to its success. It s important to note the word fiduciary. Black s Law Dictionary defines fiduciary relationships as having the highest standard of duty implied by law, which require great confidence and trust and a high degree of good faith. 1 Until your organization recognizes that a data governance program requires the highest level of long-term support and investment, it s unlikely that the program will deliver the results you require. Figure 1 Data Governance Evolution From: Lack of business ownership Data management not a priority Bottom-up IT development places low priority on data management objectives BU- or functional-group-specific business processes and requirements ignore other cross-functional enterprise impacts Inconsistent business processes, capture rules, and data models To: Sponsorship and accountability Data managed as a corporate asset Development efforts that affect critical data include top-down data stewardship Data governance forum to ensure end-to-end impact assessment of all information management efforts Adoption and enforcement of best practices including standardized data models, definitions, rules, and business processes Source: Forrester Research, Inc. September 10, , Forrester Research, Inc. Reproduction Prohibited

4 Data Governance: What Works And What Doesn t 3 Convincing Data Governance Catalysts... The growing complexity of data management environments and resulting lack of trust by end users has led information managers to consider how strategic data management initiatives can benefit from a data governance program. These data management initiatives range from information management infrastructure initiatives to business-driven initiatives with significant dependencies on the quality of underlying data. Common information management initiatives driving the need for data governance include: Compliance. The risks of compliance whether with regulatory edicts such as Sarbanes-Oxley and Basel II or matters like consumer privacy, information security, and operational risk are the most significant catalysts for many organizations, especially in industries handling the most sensitive of customer information, such as financial services, healthcare, and the public sector. Legacy data migrations. Legacy migrations commonly include application migrations from multiple legacy applications into standardized CRM or ERP systems, application upgrades such as moving from an older version of SAP or Oracle to a newer version, or data migrations from a mainframe legacy infrastructure to a new relational database environment. Instead of simply migrating bad data from one environment to another, IKM professionals are exploring how a data governance program can address data quality concerns. Mergers and acquisitions. The migration and consolidation of customer, product, and transactional data obtained through a merger or acquisition is a painful process, often requiring collaboration among stakeholders from different corporate cultures who have never before worked together. Operational BI. Operational BI facilitates a business-process-aware reporting and analysis capability with data delivered in context from disparate data sources in real time. 2 It can deliver BI to a much larger community of business users, each of which may have new and unique definitions of data quality and usefulness. Enterprise data warehousing and traditional BI. Data warehousing and BI continue to drive large investment by enterprises across industries, but a lack of focus on data quality and governance often inhibits business adoption and acceptance. Information-as-a-service (IaaS). The goal of IaaS is to deliver transformations and data quality in line with information delivery rather than as a batch process to ensure the quality and integrity of the information being provided to the user. Unfortunately, many SOA initiatives overemphasize the service architecture, downplaying how data quality and the transformation layer can ensure that trusted information is actually delivered. 3 Master data management. The quest for a single version of the truth for critical enterprise data (customer, product, employee, asset, etc.) is quietly moving from nice-to-have to must-have. However, MDM isn t just a technology space, it s a business capability where success requires organizational alignment, process engineering, and the adoption of data governance best practices , Forrester Research, Inc. Reproduction Prohibited September 10, 2007

5 4 Data Governance: What Works And What Doesn t Metadata management. Similar to MDM in breadth, but traditionally with an IT-driven focus on reusability and improved visibility across business and IT, metadata strategies aim to create an environment maintaining standardized definitions and views of relevant metadata. 5 Without effective governance, most technology-focused metadata projects fail. Other functional business initiatives. The need for high-quality data is not driven solely by technology-based programs. Information managers responsible for process-focused initiatives such as order-to-cash, customer experience, marketing effectiveness, and supply chain optimization, to name a few, are recognizing the need for a stronger focus on data quality and trust as critical success factors.... Are Often Thwarted By Powerful Adoption Inhibitors Business catalysts such as the ones listed above provide examples of how data governance can achieve optimum business results for your organization by delivering the highest-quality data with widespread stakeholder adoption. So why do a majority of large organizations lack an enterprise data governance function? The inhibitors to successful data governance are often more daunting than the most compelling strategic upside. The most common inhibitors include: Lack of business involvement. Without active accountability and participation from the business especially senior leadership data governance efforts usually sink before they swim, leaving requirements for data quality and strategic alignment undocumented and unstandardized. Also, one of the most critical deliverables of a governance initiative is the identification of data capture, maintenance, and usage process flaws that need to be mitigated. When business owners won t support this identification process, information management initiatives won t deliver trusted data. A common mistake made by business leaders is the assumption that since IT owns the systems that store and maintain data, IT also owns the data. Ambiguity of ROI calculation and the lack of a business case. Many business leaders don t understand that data governance programs aren t ends in themselves with value measurable in isolation, but instead a means of delivering value to the information management initiatives to which the governance programs apply. To calculate ROI and drive a business case, governance evangelists must understand how data supports and affects these dependent initiatives and be bold enough to claim these indirect benefits. Cross-enterprise complexity. Many grassroots data governance efforts begin within a single functional or systems environment. For example, a governance project team may be created to support quality improvements within a data warehouse (DW) or call center application. While these efforts are an excellent (and recommended!) starting point to discover what data governance methodologies and best practices will work within your organization s culture, these initiatives intentionally or unintentionally underscope the breadth of the data management ecosystem across business and IT environments. September 10, , Forrester Research, Inc. Reproduction Prohibited

6 Data Governance: What Works And What Doesn t 5 In the above examples, the DW project can cleanse and standardize the data within the DW environment, but it cannot stop bad data from coming in from the multitude of source systems feeding the DW. And the call center project can make the data within the call center app good enough to improve customer experience or support agent productivity, but it has not considered the impacts and dependencies that downstream reporting applications and users may have on the information captured. Not agreeing to disagree. Data governance aims to standardize on common definitions for critical business information across lines of business and functional areas. But what happens when a common definition doesn t make sense? For example, many companies sell products or services to consumers, small businesses, as well as large enterprises. How should these organizations define something as seemingly innocent as customer? Is a customer a consumer, a person, or a corporation... or all of the above? For the business unit that sells only to consumers, force-feeding a customer definition that requires a company name would be inappropriate. In these situations, a data governance program can effectively mitigate these concerns by creating more concise definitions for example Consumer Customer versus Small-Business Customer versus Enterprise Customer. While the solution is not rocket science, these implied barriers are often enough to inhibit business unit collaboration. Unrealistic scoping. Don t assume that all data must be governed equally. It needn t. Only the data supporting your most critical business processes and decisions require the highest level of governance. Business and IT also often underestimate the time, cost, and resources involved to achieve the end goal of trusted, quality data. Governance must be an ongoing business function, not a one-time project. As the case studies below make clear, many successful governance programs have taken years to mature and deliver results. CASE STUDIES PROVE THAT THERE ARE MULTIPLE ROADS TO SUCCESS As the following case studies in data governance demonstrate, no single approach will work for every organization. The good news: With the appropriate resources, prioritization, and most importantly patience, best practices will lead to exceptional value. Case Study No. 1: BT Recognized The Long-Term Commitment Required In the late 1990s, BT s CIO kicked off and evangelized its seven-year Enterprise Information Program, with the main focus of gaining bottom-line ROI from data quality improvements. After billions of dollars of IT investments over many, many years, the CIO came to terms with the business s belief that years of investment in complex, heterogeneous systems failed to deliver acceptable ROI. The recurring theme from stakeholder interviews concluded, the systems were fine, performance was fine, but the data was often wrong. From the business side of the house, a senior manager from each line of business assumed the role of champion for his or her respective organization, and this grouping created the Information Management Forum the big stick steering committee responsible for the prioritization of data 2007, Forrester Research, Inc. Reproduction Prohibited September 10, 2007

7 6 Data Governance: What Works And What Doesn t management efforts across the enterprise. Organizationally, IT acts as the driver for the data governance program, but the business owns the definitions of quality and the accompanying business rules. The business also ultimately owns the metrics that define ROI and success across the data management initiatives. Long-term investment in data governance and data quality allowed BT to realize extraordinary success. From the beginning of its Enterprise Information Program, BT calculated direct and indirect benefits exceeding $1.2 billion. According to a senior manager at BT, The total benefit of the program surprised us all. Although BT didn t know this when it started, the company has since come to realize that between 10% and 20% of the organization s revenue was being negatively impacted by data management problems. One of the less pleasant surprises that BT learned to deal with was simple human nature: People were not ready to admit to having problems with data, since they saw it as a failure on their behalf so data governance was threatening. 6 Providing the appropriate education and expectation-setting around data governance to its business and IT stakeholders was a critical step in BT s success (see Figure 2). And of course, patience was also a virtue. Figure 2 BT Case Study Problem Billions of dollars of IT investment ROI was difficult to identify Business stakeholder consensus: Systems are fine, performance is fine, but data was often wrong Example of impact: Incorrect addresses led repair personnel to incorrect locations. Costly, with poor customer experience. Complex heterogeneous environment with critical data managed throughout Solution CIO was executive sponsor and primary evangelist A seven-year DQ program kicked off in 1997 running through First project: building a new marketing data warehouse. Decision for IT to drive governance The business owns definitions of quality, rules, and metrics; IT remains the driver Technology enablers: Trillium Software and Ab Initio heavily leveraged to enable data quality processes Key learnings If done well, a focus on data quality can help your business save and make serious money. BT internally recognized more than $1.2 billion in business benefits achieved over the seven-year lifespan of its DQ program. Denial! People were not ready to admit to having problems with data, since they saw it as a failure on their behalf so data governance was threatening. Data governance must match the existing culture in which the company exists IT held a lot of decision-making power and influence, so IT drove governance Governance is a messy and complex problem, and no company can ever fully say they ve solved it it must be viewed as a continuous process Source: Forrester Research, Inc. September 10, , Forrester Research, Inc. Reproduction Prohibited

8 Data Governance: What Works And What Doesn t 7 Case Study No. 2: A Credit Card Issuer Started With Organizational Alignment The executive vice president/cio of this credit card issuer just gets it, and she has assumed the role of primary evangelist and executive sponsor for its data asset management initiative. This executive is not only a technical CIO, she wears many business hats, as well, and is respected within the organization as a business leader. Her active involvement allowed this organization to overcome the common organizational alignment and participation hurdles required to get its newly formed data governance program off the ground. This firm s governance organization starts with an executive-level data governance council, which includes one executive from each business unit. Each executive assumes the responsibility for assigning data stewards from their group, and the council is the top escalation point for policy approvals. Members of the enterprise architecture team represent IT on the council, as well. The assigned data stewards offer subject matter expertise with strong business knowledge most typically represented by senior-manager- or director-level employees within the organization. Business stewards deliver data definitions and standards, accountability for management of the data (including data capture best practices), and responsibility for defining quality. At the center of it all: the manager of data architecture and governance aka the chief data steward. This manager drives data governance and accepts responsibility for ensuring that the data stewards know where to prioritize and focus their efforts. This organization recognizes that role definition and organizational alignment alone will not ensure the success of its data governance efforts, so data stewards now have their stewardship responsibilities as part of their performance goals. While executive and organizational support for this blossoming governance effort has been strong, more needs to be done to ensure future success. To start, the data governance program has not yet determined how it plans to measure the qualitative and quantitative success of the initiative. Its first step will be to analyze, define, and baseline data quality levels so that ongoing key performance indicators can be monitored going forward. Also, while some business cases have been identified to calculate ROI, such as reducing storage costs due to data duplication and reducing wasted marketing costs due to incorrect customer data, the business stewards will be responsible for strengthening the business case by identifying additional opportunities for improvement (see Figure 3). The largest risk that this company anticipates is prioritization. The chief data steward noted, As other things arise, this [data governance program] may become lower in priority. Any new critical projects can likely derail [at this early stage]. 2007, Forrester Research, Inc. Reproduction Prohibited September 10, 2007

9 8 Data Governance: What Works And What Doesn t Figure 3 Credit Card Issuer Case Study Problem Wasted marketing costs due to incorrect customer data High storage costs due to data duplication Internal Audit & Compliance and Risk & Security organizations required additional focus on data management Company leadership decided it was time to recognize data as a strategic asset that needed to be more effectively managed Solution EVP/CIO is executive sponsor and primary evangelist Formation of executive-level Data Governance Council for escalations and policy approvals Creation of manager of data architecture and governance role (aka, chief data steward) to drive initiative Stewardship responsibilities are part of employee performance goals Key learnings Strong executive support and leadership helped fast-track organizational alignment requirements Need to determine how to measure the qualitative and quantitative success of the initiative Largest risk is prioritization: As other issues arise, this (data governance program) may become lower in priority. Any new critical projects can likely derail Source: Forrester Research, Inc. Case Study No. 3: Grassroots Efforts Finally Made The Difference For An Investment Firm This investment management firm started focusing on data governance and stewardship as early as 1994 and had a few failed attempts at instituting through 2002 due to a lack of business prioritization and enterprise sponsorship. The passion and evangelism of a few individuals within the information architecture organization, and the company s drive toward shared data specifically the need for a consolidated customer environment finally made the difference. Following a common theme in data management in the 1990s, this firm designed and implemented a central customer database. When looking to enhance functionality for this database, a lack of data ownership and governance helped move the decision toward creating a new customer database, rather than growing the existing one. To ensure that they didn t go down the same path again, the data governance evangelists convinced the new customer consolidation project to spend 1% of the project budget to start a data stewardship program. This firm leverages its executive ownership at the CXO level for support and funding while merging that with a grassroots success story. Critical milestones included socialization and education of the governance and stewardship concepts across the organization and up through senior management. Now, CXO-level executives are very familiar with the program; they understand, support, and evangelize it, but they are not trying to drive it. September 10, , Forrester Research, Inc. Reproduction Prohibited

10 Data Governance: What Works And What Doesn t 9 Data ownership represents an important concept within this data governance program. For areas where the firm has defined stewardship, each table and column has an owner responsible for the data. These owners may live in both business and IT, depending on the type of data or metadata. Depending on the criticality of the information, the owner of an individual row in a table may be the highest level executive in the company, while the owner at a data structure level may often be middle management within a business unit. For enterprise definitions, multiple managers and owners will often collaborate to create a shared definition, with one manager accepting final ownership. Leveraging the strengths of this firm s existing company culture enabled this data governance program to succeed where past efforts were unsuccessful. These cultural components include the ability to take long-term perspectives and invest for the future, assuming a fiduciary responsibility in all they do to run their business, and enjoying a much more collaborative and less political environment than many other companies of similar size. While this firm believes that it has finally found the formula for driving and maintaining a data governance presence within its organization, the primary evangelists for the program have recognized that with their grassroots effort, selling and marketing (of the program) is very time-consuming and takes high energy and passion (see Figure 4). Figure 4 Investment Management Firm Case Study Problem A few failed attempts at data governance from 1994 through 2002 Customer data consolidation is a high priority; prior central customer data solution had no identified ownership Opportunities for improving communication, transparency, maintainability, leveragability, and continuous process improvement Solution The passion and evangelism of a few individuals within the information architecture organization led this grassroots data governance effort CXO-level executives support and evangelize governance program, but do not drive it Data ownership defined and accepted across all levels of the organization Solution evolved over many years, building inertia ongoing Socialization and education of program value was critical Key learnings Focus on leveraging strengths of company culture helped evangelize program After initial skepticism, business managers, project managers, project leads, business analysts, and developers in early adopting areas have become strong advocates for the value of the process Selling and marketing (of the program) is very time-consuming and takes high energy and passion Source: Forrester Research, Inc. 2007, Forrester Research, Inc. Reproduction Prohibited September 10, 2007

11 10 Data Governance: What Works And What Doesn t Case Study No. 4: Financial Services Company Implements Building Permit Approach The CIO of a North American financial services firm created a single point of accountability for the data management life cycle a vice president of information management (similar to the chief data officer position found in other companies) who reports directly to the CIO. This VP employs approximately 200 professionals responsible for data management for the company globally. This organization, which now includes architects, mainframe specialists, data modelers, DBAs, and other data management personnel, is also responsible for its DW and BI solutions. In addition, this firm adopted the concept of data stewards and custodians. Its data steward is a business stakeholder responsible for data definitions, quality rules, and data access authorization, while its custodians are IT roles responsible for managing the data to comply with those business requirements. In addition to creating the information management organizational structure, from a methodology perspective, the CIO agreed to adopt and enforce a project certification building permit approach. This approach implemented a single standard for process change management for all data modeling. A project management framework manages governance through a tightly gated process. Data management represents an official gate that needs to be passed by any architect, designer, or data modeler across the enterprise to move to the next milestones within any data management project. This organizational and policy shift enabled significant benefits for this firm. It experienced a 56% rate of data development reuse in its multi-terabyte data warehousing environment, which translated to millions of dollars in savings (see Figure 5). Figure 5 Financial Services Company Case Study Problem Lack of communication and collaboration among siloed IT functions including architects, mainframe specialists, data modelers, DBAs, and other data management personnel No accountability for data management life cycle Desire to evolve firm into a data-driven development shop Solution CIO was the champion and created role of VP of information management as single point of accountability Moved from decentralized to centralized data modeling group Identified data stewards and custodians Adopted and enforced a project certification building permit change management approach Data management represents an official gate to be passed in order to move to the next milestone within any data management project Key learnings If the end user doesn t have the energy and fortitude to consolidate data groups, very limited value and frustrating Rate of data element development reuse in DW environment is 56%, translating into millions of dollars in savings September 10, 2007 Source: Forrester Research, Inc. 2007, Forrester Research, Inc. Reproduction Prohibited

12 Data Governance: What Works And What Doesn t 11 LET OTHERS GOVERNANCE PROGRAMS INSPIRE BUT NOT DICTATE YOUR OWN The case studies above teach us that a data governance approach cannot be copied verbatim from another organization. However, there are many lessons to be learned from these organizations that can provide you with valuable insights. Leverage these insights when designing the data governance methodology appropriate for your organization. The key questions to answer include: Evangelism. Do you have executive sponsorship, or will a grassroots approach be required to get started? Scope. How do you reduce the scope to target only the most important data? Ownership. Who will be the business owners for the data most critical to your company? Program management. What internal organization should drive this program the business or IT? Organizational structure. Is your organization primarily centralized, decentralized, or a hybrid? Staffing. Should data stewardship be a full- or part-time job? Can you find the right people to fill the stewardship roles? Funding. Where will funding come from? Can you ride on the coattails of an existing data management project? Business drivers. What major business drivers can help build a business case for governance? Inhibitors. What are the primary inhibitors (cultural, funding, resources, prioritization, etc.) and can they be mitigated? Timeliness. Are we truly ready? If not now, then when? Answering these questions will set the foundation for your governance program and can provide the requisite assessment of your organization s readiness to tackle the data governance challenge. Data Governance Framework, Roles, And Responsibilities As the case studies show, many roles must be identified and filled appropriately for a data governance program to get off the ground (see Figure 6). Forrester defines four key data governance roles: 2007, Forrester Research, Inc. Reproduction Prohibited September 10, 2007

13 12 Data Governance: What Works And What Doesn t Executive sponsor. Appointing a CXO-level executive as executive sponsor provides the greatest chance of enterprise adoption. Chief finance officers often step up to this role since their organizations have traditionally been the most data-driven from a reporting, planning, and compliance perspective. When customer-oriented data is the most critical asset being managed, chief marketing officers often take the reins. But building consensus across functional and lineof-business boundaries is challenging. Increasingly anecdotal evidence is suggesting that chief risk officers are stepping up to the plate since few business and IT managers will resist initiatives mandated by corporate operational risk and compliance policies. CIOs often adopt executive sponsorship responsibilities, but as suggested in the case studies, the CIOs must be viewed as trusted business partners across the organization in order for them to succeed in this role. Program driver. The program driver represents your data governance hub, making it a critical role. All communication, coordination, change management, and escalations come through this individual or team. Staffing this role requires more than just the ability to manage a project plan. The program driver must be the primary liaison between business and IT stakeholders and be trusted by both. Business stewards. Your business stewards are your subject matter experts. They understand both the strategic priorities and tactical processes that drive their business and the data used to support it. This is a challenging role to staff, as it requires someone who understands the detailed requirements of the front-line end users of information management applications, who is tech-savvy enough to communicate to IT architects effectively, and who is senior enough to be a trusted decision-making proxy for the executive leader of their business. IT stewards. Your IT stewards represent the systems that support your data supply chain from source capture systems like call center, ecommerce, and sales force automation tools through data integration and middleware infrastructure and downstream to DW and BI apps that consume and deliver data. The IT stewards are responsible for translating the business policies and definitions created by the business stewards into technical requirements that can be consumed by IT designers and developers within their application space. Staffing this role with an architect-level IT professional with strong communication skills will often meet these needs. Remember: These are roles, not job titles. Very often data governance and stewardship roles may only be a moderate part of these employees overall responsibilities, yet the individuals that share these roles especially your business and IT stewards will be distributed across the organization (see Figure 7 and see Figure 8). To ensure that these roles are performed effectively, include data governance and stewardship responsibilities as part of individual and team performance evaluations. September 10, , Forrester Research, Inc. Reproduction Prohibited

14 Data Governance: What Works And What Doesn t 13 Figure 6 Data Governance Roles And Responsibilities Executive sponsor Must be: Identified early CXO-level An active participant An evangelist The final point of escalation Can be a committee, if appropriate Program driver Must be: Unbiased Coordinator of all stewards Communicating all decisions made by stewards to all relevant stakeholders Driving ongoing data quality auditing and metrics to assess program success Primary point of escalation to executive sponsor or committee, when necessary Program driver is often seen in IT s program management office (PMO) Business stewards Must be: IT-savvy Strong communicators Educators across the business IT stewards Must be: Business-savvy Strong communicators Educators across IT Source: Forrester Research, Inc. Figure 7 Data Governance Roles Exist Across The Organization CEO CIO/CTO LOB executive CMO CFO Chief privacy officer IT director Chief architects LOB manager Direct marketing, channel marketing, marketing analytics Privacy manager/ analyst Project manager Data warehouse/ BI architect Data steward Financial controller Business systems analyst Designers/ developers Data integration architect Information/data architect Market/business intelligence analyst Business or information analyst Budgeting/ planning manager Business architect Data librarian Data quality analyst Budgeting/planning manager Source: Forrester Research, Inc. 2007, Forrester Research, Inc. Reproduction Prohibited September 10, 2007

15 14 Data Governance: What Works And What Doesn t Figure 8 Enterprise Data Governance Model Executive sponsors Business stewards Sales and marketing Customer service and support Finance, order management, supply chain Product and service fulfillment Business units, geographic theaters, business functions Definition: Rules Processes Policies Quality metrics Data governance program driver: Organization alignment Compliance and best practice: Customer Product Asset Employee IT stewards Information technology Application architects Information architects Business process architects Data warehousing and business intelligence architects Integration architects Source: Forrester Research, Inc. R E C O M M E N D A T I O N S DESIGN YOUR GOVERNANCE BASED ON COMPANY CULTURE Face it from the start: Your company s existing culture won t change to support data governance, so instead work to understand your culture and leverage its strengths. For example, in a culture where each business unit independently prioritizes its own strategic direction, encourage a decentralized data governance approach where subject matter experts in different business units collaborate with each other, thereby avoiding a centralized stewardship role that could be viewed as a threat. Start small and grow. Cut your teeth by initiating a data governance program as part of a funded data management project. Many companies kick off governance within a BI or DW project, a MDM initiative, or within a CRM or ERP implementation. Define executive sponsors and data stewards early and use them as your evangelists. Successful data governance programs can start from grassroots efforts, but not very often. Most organizations require top-down prioritization to achieve real changes in data management and data quality. Don t forget the business case. Yes, data governance enables business value, and no, it doesn t in and of itself increase revenue or reduce costs. However, many senior managers will be willing to help you out by talking on the record about the pain caused by poor-quality, untrusted data. Use qualitative and quantitative measures of key business functions, such as marketing, finance, sales, and call center operations, to justify governance programs. September 10, , Forrester Research, Inc. Reproduction Prohibited

16 Data Governance: What Works And What Doesn t 15 Tie participation to employee performance goals. Nothing kills a data governance program faster than expecting voluntary participation. The subject matter experts you target to be your business and IT stewards are certain to be overloaded with too much responsibility. They need your help in prioritizing their participation in governance programs. Ensure that the stewards direct managers set priorities explicitly for each steward s respective responsibilities. Then reward the stewards in their performance evaluations by recognizing their participation and contributions. And if some managers don t cooperate, escalate to your executive sponsor. Prioritize with a hub-and-spoke approach. Shared corporate-level definitions should typically be governed centrally, but data that s only relevant to a single line of business or application can be governed by a subordinate LOB-/department-focused governance organization. But before decentralizing it, be sure to validate your assumption that the data truly has no enterprise value. W H A T I T M E A N S DATA GOVERNANCE WILL EVOLVE INTO INFORMATION GOVERNANCE Within the next two to four years, structured and unstructured information siloes will be bridged, and information workers will demand seamless access to the structured data found in databases and enterprise apps along with the volumes of unstructured content strewn across the enterprise in s, spreadsheets, video and graphic files, and much more. Best practices for taxonomies and classification now only found in the content and search worlds will meet the business glossaries and stewardship functions found in the data and business intelligence world. And the governance required to integrate these technologies and its users will need to evolve, as well. ENDNOTES 1 Black s defines fiduciary duty as a duty to act for someone else s benefit, while subordinating one s personal interests to that of the other person. It is the highest standard of duty implied by law. Source: H.C. Black, Black s Law Dictionary, Sixth Edition, Springer, Data integration techniques such as extract, transform, and load (ETL); enterprise information integration (EII); change data capture (CDC); and even custom-coding play a significant and perhaps the most critical role in delivering operational business intelligence (BI) capability. See the September 25, 2006, Turning Transactions Into Decisions report. 3 IaaS has exploded on the scene over the past two years, moving from an obscure topic to one of the top usage scenarios in service-oriented architecture (SOA). Forrester expects that in 2007, the majority of large enterprises will add SOA to the list of ways they integrate information. See the March 22, 2007, Information-As-A-Service: What s Behind This Hot New Trend? report. 2007, Forrester Research, Inc. Reproduction Prohibited September 10, 2007

17 16 Data Governance: What Works And What Doesn t 4 MDM is a business capability that requires business process improvements and organizational commitments, as well as an ecosystem of technologies to allow the creation, management, and distribution of high-quality master data throughout the organization. See the November 10, 2006, Introducing Master Data Management report. 5 Metadata is information that describes or provides context for data, content, business processes, services, business rules, and policies that support an organization s information systems. See the July 11, 2006, Trends Getting Your Arms Around Metadata report. 6 Source: BT s lead consultant for its Enterprise Information Program. September 10, , Forrester Research, Inc. Reproduction Prohibited

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