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Building a case for data quality: What is it and why is it important
According to an IDC study, 30-50% of organizations encounter a gap between their data expectations and reality. A deeper look at this statistic shows that:
Data quality API: Functions, architecture, and benefits
While surveying 1900 data teams, more than 60% cited too many data sources and inconsistent data as the biggest data quality challenge they encounter. But
Batch Processing versus Real-Time Data Quality Validation
Research shows that while 24% of data teams use tools to find data quality issues, most of their problems still go unresolved. This means that
The Impact of Poor Data Quality: Risks, Challenges, and Solutions
Poor data quality can have devastating risks on your business. Most organizational workers realize the impact of of poor data quality, but it is hard
Designing a Data Quality Management Framework
Data is the lifeblood of decision-making in the business world today. Yet as revealed by the State of Data Quality Report 2022, less than half
5 data quality processes to know before designing a DQM framework
Most companies trying to become data-driven cite poor data quality as one of the top 5 challenges. Invesp published a report where they discovered that
The definitive buyer’s guide to data quality tools
A recent survey reported that the top KPI for data teams in 2021 was data quality and reliability. But the majority of the respondents said
Building a Data Quality Team: Roles and Responsibilities to Consider
Despite organizations claiming their data strategies are effective, only 56% report achieving their data goals in 2023, and a staggering 66% of enterprise data remains
What is the Difference between Data Quality and Master Data Management?
We have delivered data quality solutions to Fortune 500 companies for over a decade. We often come across clients who are confused about how to