During the past several decades, managers have expended great effort to stay abreast of the latest information technologies (IT). Despite this, managers still do not have the accurate, timely, and useful data they need to be effective. Data failures are embarrassing and costly. Recent published examples include lawsuits filed to protect consumers from incorrect credit reports, incorrect payment of municipal taxes, and rebates due to incorrect product labeling. No industry — communications, financial services, manufacturing, health care, and so on — is immune. Nor is government. We at AT&T Bell Laboratories QUEST have initiated a broad-based program to determine the root causes of poor quality data and develop simple, effective strategies for mitigating them.1 Our ultimate goal has been to improve data and information quality by orders of magnitude and create an unprecedented competitive advantage. We have found that:
- Many managers are unaware of the quality of data they use and perhaps assume that IT ensures that data are perfect. Although poor quality appears to be the norm, rather than the exception, they have largely ignored the issue of quality.
- Poor quality data can cause immediate economic harm and have more indirect, subtle effects. If a financial services company cannot get my social security number right, I will seriously question its ability to manage my money. Mistrust grows when the data from one department, say, order entry, and used by another, say, customer billing, are flawed.
- Poor data in financial and other management systems mean that managers cannot effectively implement strategies.
- Inaccurate data make just-in-time manufacturing, self-managed work teams, and reengineering infeasible. The right data need to be at the right place at the right time.
- Due largely to the organizational politics, conflicts, and passions that surround data, only a corporation’s senior executives can address many data quality issues. Only senior managers can recognize data (and the processes that produce data) as a basic corporate asset and implement strategies to proactively improve them.
The relatively simple strategies I present here are directly applicable to all data-intensive industries. Their conscientious implementation can vastly improve data quality. At AT&T, the focus on data has led directly to reengineering opportunities and reduced costs. In particular, programs with suppliers (local telephone companies) have greatly improved the quality of bills, at reduced cost to both the supplier and data user.