What is Data Quality?
Data quality is an essential characteristic that determines the reliability of data for making decisions. High-quality data is:
- Complete: All relevant data —such as accounts, addresses and relationships for a given customer—is linked.
- Accurate: Common data problems like misspellings, typos, and random abbreviations have been cleaned up.
- Available: Required data is accessible on demand; users do not need to search manually for the information.
- Timely: Up-to-date information is readily available to support decisions.
Business leaders recognize the value of big data and are eager to analyze it to obtain actionable insights and improve the business outcomes. Unfortunately, the proliferation of data sources and exponential growth in data volumes can make it difficult to maintain high-quality data. To fully realize the benefits of big data, organizations need to lay a strong foundation for managing data quality with best-of-breed data quality tools and practices that can scale and be leveraged across the enterprise.
Business value of data quality
Data quality-related problems cost companies millions of dollars annually because of lost revenue opportunities, failure to meet regulatory compliance or failure to address customer issues in a timely manner. Poor data quality is often cited as a reason for failure of critical information-intensive projects. By implementing a data quality program, organizations can:
- Deliver high-quality data for a range of enterprise initiatives including business intelligence, applications consolidation and retirement, and master data management
- Reduce time and cost to implement CRM, data warehouse/BI, data governance, and other strategic IT initiatives and maximize the return on investments
- Construct consolidated customer and household views, enabling more effective cross-selling, up-selling, and customer retention
- Help improve customer service and identify a company's most profitable customers
- Provide business intelligence on individuals and organizations for research, fraud detection, and planning
- Reduce the time required for data cleansing—saving on average 5 million hours, for an average company with 6.2 million records (Aberdeen Group research)
Teachers Insurance and Annuity Association – College Retirement Equities Fund (TIAA CREF) leveraged IBM InfoSphere Information Server to improve plan reporting and customer view, consolidate legacy environments and improve processing speed by almost 10 times. Also, TIAA CREF was able to meet over 2000% growth in reporting needs not only on time but over 50% ahead of schedule.
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IBM InfoSphere Information Server for Data Quality
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