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…

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