Tackle Data Quality Issues
Low-quality data saps time and money. Stop bad data in its tracks by developing a comprehensive strategy for cleaning your data – and keeping it clean.
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Last Revised: November 30, 2010
- Poor data quality is a problem facing most organizations.
- There are a variety of hard and soft costs associated with poor data, including increased direct marketing costs, lost revenue, inadequate financial reporting, unfulfilled orders and damaged customer relationships.
- Despite the costs and headaches associated with having poor data, most organizations struggle to define and implement a strategy for effectively improving data quality.
- While the business must be accountable for data ownership, IT needs to be proactive in providing solutions for data quality problems.
- There are five types of interrelated data quality issues: data duplication, stale data, incomplete data, invalid data, and data conflicts. Each problem requires a combination of corrective and preventative measures by the business and IT.
- Some methods for solving data quality problems are more effective than others. Mass cleanups and strong data validation are more effective than user-training and vendor-supplied tools.
Impact and Result
- Mass cleanups will solve most data hygiene problems (i.e. duplicate, incomplete and invalid data). Stale data can be fixed through external source integration.
- Assigning data stewards will improve accountability for data accuracy.
- Clean data means improved marketing campaigns, decision making and operations planning; both hard and soft costs are reduced dramatically when corrections are put in place.
Get to Action
Understand how data quality problems arise and develop effective solutions for combating them.
Get the maximum use out of clean data for operations, planning, and decision making.
Identify the areas in the business that are suffering from data quality problems.
Target remedies at the biggest data quality problems plaguing the organization.
Establish and codify data management policies.
Streamline data operations and clarify accountability for data.
Create a Data Steward role for managing and improving data.
Drive greater accountability for data integrity.
Companies Who Helped
12 Contributors (due to the sensitive nature of the problems discussed, all contributors are anonymous).