Data Quality Management
In today’s marketplace of quickly changing data products and capabilities, businesses are at risk of making mistakes when it comes to choosing solutions for information systems and data management. In a confusing time of data scientists, data analysts, and data engineers, and all the narrow and overlapping disciplines of each, many companies prefer to rely on consultants and trusted partners for total expertise, rather than climb a steep and ever-changing learning curve.
Forty percent of all business initiatives fail to achieve their targeted benefits because of their poor data quality. Forty percent.
So, ensuring the data your organization relies upon to make business-guiding and revenue-driven decisions is accurate will significantly impact short-term and long-term success.
Download our free whitepaper, Data Quality Management, to learn:
- What is Data Quality Management?
- What Roles are Required of a Team for Successful Data Quality Management?
- How the Typical Data Quality Cycle Works
- The Impacts of Poor Data
- Key Characteristics of High Quality Data
- Our Three-Step Process for Evaluating Data Quality