Data Quality Imperative.pdf

Data quality issues cost companies $9.7 million annually, with poor-quality data costing the US economy around $3.1 trillion per year.
40% of organizations identify data quality as the primary obstacle to achieving business objectives, while 75% of businesses that improved data quality exceeded their annual objectives.

Impediments to High-Quality Data

Data silos and lack of integration lead to missed opportunities and fragmented views of information.
Data inaccuracy and inconsistency result in suboptimal outcomes due to lack of trust in data.
Lack of data standardization leads to compliance and regulatory risks.

6 Dimensions to Measure Data Quality

A comprehensive data quality solution is necessary to address these challenges.

Delivering Clean, Correct, and Consistent Data

Sigmoid's data quality solution provides diagnostic reports based on 6 data quality dimensions to gain insights into overall data health.
The solution enables comprehensive data validation and profiling, integrating seamlessly with existing workflows.

Impact of High-Quality Data

Good data quality improves customer experience, decision-making, operational efficiency, forecasting, and planning.
Organizations that prioritize data quality see a 25% increase in revenue and 52% improvement in operational efficiency.

Conclusion

High-quality data is vital for informed decision-making, innovation, and competitive advantage in today's data-driven world.
Prioritizing data quality helps businesses improve decision-making, customer experience, and operational efficiency, with Sigmoid enabling enterprises to meet necessary standards for data accuracy, completeness, and reliability.