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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.
Your Challenge
- 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.
Our Advice
Critical Insight- 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.
- 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
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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.
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Identify the areas in the business that are suffering from data quality problems.
Target remedies at the biggest data quality problems plaguing the organization.
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Establish and codify data management policies.
Streamline data operations and clarify accountability for data.
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Create a Data Steward role for managing and improving data.
Drive greater accountability for data integrity.
Related Content
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Download NowLow-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.
Companies Who Helped
12 Contributors (due to the sensitive nature of the problems discussed, all contributors are anonymous).
Solution Road Map
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Move From Chaos to a Realistic Data Integration StrategyFind a balance between a hopelessly tangled web of integration structures and expensive, impractical utopia.
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Tackle Data Quality IssuesLow-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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Implement a Master Data Management StrategyManage the truth, the whole truth, and nothing but the truth with an MDM strategy.
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Effectively Manage Data GovernanceConvert the management of organizational data from a burden into a competitive advantage.
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Integrate a Data Audit into the Data Management PlanData audits are not just about security: quality matters!
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Develop a Five Year Data Architecture PlanSecure your footings in data to lay a foundation that will withstand the winds and rain of today's IT disruptors.