Improving Business Agility Using the Six Sigma DMAIC Roadmap in a Data Quality Management Project

Data  is the engine that supports both strategic and operational decisions; these decisions depend on what you know and what you know depends on the information provided by the data.

Organizations always have data quality problems—data that are often hard to use, challenging to manage, incomplete, misfielded, incorrect, or completely absent. The data quality problems are found in any industry, including financial services, healthcare, pharmaceutical, telecommunications, and retail.

The problem is not whether the data are wrong or not, the problem is that, oftentimes, nobody even knows that millions of financial investments may be based on half truths; the metrics used to reflect quality, value, or profitability may be wrong. Information is used for anticipating risks and developing methods for mitigation, but errors in the data can snowball into issues with risk analysis, assessment, and management.

From an organization’s perspective, the impacts of bad data and inaccurate information are very serious and lead to:

• Severe impacts on an organization’s ability to sell, service, and market effectively and manage supply chain operations
• Wasted effort and errors due to manual and repetitive data entry
• Lower customer satisfaction, because the call center and service personnel do not have the information they …