The Data Governance Maturity Model

A DataFlux White Paper Prepared by: DataFlux Corporation The Data Governance Maturity Model Establishing the People, Policies and Technology That Manage Enterprise Data Leader in Data Quality www.dataflux.com International and Data Integration 877–846–FLUX +44 (0) 1753 272 020 Over the next two years, more than 25 percent of critical data in Fortune 1000 companies will continue to be flawed, that is, the information will be inaccurate, incomplete or duplicated…1 Today, businesses are discovering that their success is increasingly tied to the quality of their information. Organizations rely on this data to make significant decisions that can affect customer retention, supply chain efficiency and regulatory compliance. As companies collect more and more information about their customers, products, suppliers, inventory and finances, it becomes more difficult to accurately maintain that information in a usable, logical framework. The data management challenges facing today’s business stem from the way that IT systems have evolved. Enterprise data is frequently held in disparate applications across multiple departments and geographies. The confusion caused by this disjointed network of applications leads to poor customer service, redundant marketing campaigns, inaccurate product shipments and, ultimately, a higher cost of doing business. To address the spread of data – and eliminate silos of corporate information – many companies implement enterprisewide data governance programs, which attempt to codify and enforce best practices for data management across the organization. Although the goal is clear – the quality of information must improve to support core business initiatives – there is no definitive roadmap for starting these projects. For any organization, the first step to address the quality and value of corporate data is to take an honest assessment of the data management infrastructure. Through the Data Governance Maturity Model, organizations can identify and quantify precisely where they are – and where they can go – to create an environment that can deliver and sustain high-quality information. This paper explores: • The major issues of building better data across the enterprise • Ways to utilize the existing people, business policies and technology to achieve more effective data quality policies across multiple departments • How to determine the maturity of an organization’s data management capabilities – and find a data governance strategy that fits the organization 1 Gartner, Inc press release. “'Dirty Data' is a Business Problem, Not an IT Problem, Says Gartner,” March 2, 2007. 1 The Impact of High-Quality Data In the past, business units were only concerned with entering and tracking data to meet the needs of their specific departments. The result for the enterprise was a buildup of redundant, inconsistent, and often contradictory data, housed in isolated departmental applications from one end of the organization to another. However, two significant forces complicate every company’s data-driven projects. First, the amount of data is increasing every year; IDC estimates that the world will reach a zettabyte of data (1,000 exabytes or 1 million pedabytes) in 2010.2 Second, a significant portion of all corporate data is flawed. The effect of this avalanche of bad data can be stunning. Larry English, author and information quality pioneer, writes, “Process failure and information scrap and rework caused by defective information costs the United States alone $1.5 trillion or more.”3 The impact of poor-quality data was initially felt in applications that focus on customer The amount of information: database marketing, data warehousing and customer relationship management data – and the (CRM). Data quality and data integration technologies, when applied locally or departmentally, prevalence of could address some of these problems within each application. However, this only led to silos of consistent, accurate and reliable data. The goal is to move beyond those silos and find a bad data – is way to manage this data across departments, applications, business units and divisions. growing steadily. The benefits to a holistic approach are obvious; better data drives more effective decisions across every level of the organization. With a more unified view of the enterprise, managers and executives can create strategies that make the company more profitable. A successful enterprise strategy will encompass three main elements: • People – Effective enterprise data governance requires executive sponsorship as well as a firm commitment from both business and IT staffs • Policies – A data governance program must create – and enforce – what is considered “acceptable” data through the use of business policies that guide the collection and management of data. • Technology – Beyond data quality and data integration functionality, an effective data governance program uses data synchronization technology, data models, collaboration tools and other components that help create a coherent enterprise view 2 Mearian, Lucas. “A zettabyte by 2010: Corporate data grows fiftyfold in three years.” Computerworld, March 6, 2007. 3 English, Larry. “Plain English about Information Quality: Information Quality Tipping Point.” DM Review, July 2007. 2 The Data Governance Maturity Model Best-practice data quality programs are not a one-shot measure (clean up and move on)... To achieve such results, successful programs identify the organizational processes behind data quality. Much like regular IT housekeeping, from virus scanning or performance monitoring to data backup, the data quality program becomes part of daily IT routine.4 The Data Governance Maturity Model helps organizations understand their current level of data management. More importantly, the model can identify a path for growth in the future. While achieving a single, unified enterprise view is an evolutionary process, an organization’s growth toward this ultimate goal invariably follows an understood and established path. The four distinct stages are: 1. Undisciplined 2. Reactive 3. Proactive 4. Governed It’s important to identify the current stage of operation and understand why the organization is there. Companies that plan their evolution in a systematic fashion gain over those that are forced to change by external events. The Data Governance Maturity Model can help control that change by determining what stage is appropriate for the business – and how and when to move to the next stage. Figure 1 shows the Data Governance Maturity Model and the typical use of enterprise applications common to each of its four distinct stages. Each stage requires certain investments, both in internal resources and from third-party technology. However, the rewards from a data governance program escalate while risks decrease as the organization progresses through each stage. The model depicts the types of technologies where data consolidation and integration often occur. Companies typically try to drive value from data initially within smaller projects (database marketing, for example) and then move to larger projects. The stages of the model are a continuum, and movement from one level to the next is will not happen all at once. There is also a chasm between the second and third stage (described in more detail later), as organizations have found that the resources and commitment necessary to advance from Reactive to Proactive requires critical changes in executive support and corporate buy-in. 4 “Organizing for Data Quality.” Research note from Gartner Inc., June 1, 2007. 3 Stage One – Undisciplined (Think Locally, Act Locally) At the initial stage of the Data Governance Maturity Model, an organization has few defined rules and policies regarding data quality and data integration. The same data may exist in multiple applications, and redundant data is often found in different sources, formats and Undisciplined records. Companies in this stage have little or no executive-level insight into the costs of bad organizations or poorly-integrated data. Not surprisingly, about one-third of all organizations are at the have limited Undisciplined stage. visibility into Table 1: Characteristics of an Undisciplined Organization data quality problems.