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Poor Data Quality is Pervasive, Impacting Trust in Data and Success Across Data Programs

Precisely

The 2023 Data Integrity Trends and Insights Report , published in partnership between Precisely and Drexel University’s LeBow College of Business, delivers groundbreaking insights into the importance of trusted data. Let’s explore more of the report’s findings around the challenges and impacts of poor data quality.

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Maximizing the Value of Your Address Data with Geo Addressing

Precisely

Geo Addressing in Action: 3 Valuable Use Cases 77% of data and analytics professionals surveyed cite data-driven decision-making as the leading goal of their data programs. But 41% say poor address data quality is the top challenge to the effective use of location data for those decisions.

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Maximizing the Value of Your Address Data with Geo Addressing

Precisely

Geo Addressing in Action: 3 Valuable Use Cases 77% of data and analytics professionals surveyed cite data-driven decision-making as the leading goal of their data programs. But 41% say poor address data quality is the top challenge to the effective use of location data for those decisions.

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Five Reasons Automation Is Key to Data Governance

Precisely

For data teams, that often leads to a burgeoning inbox of new projects, as business users throughout the organization strive to discover new insights and find new ways of creating value for the business. In the meantime, data quality and overall data integrity suffer from neglect.

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Solving 5 Big Data Governance Challenges in the Enterprise

Precisely

According to a recent report from Drexel University’s LeBow Center for Business Analytics , 77% of data and analytics professionals say that data-driven decision-making is an important goal of data programs. However, fewer than half of survey respondents rate their trust in data as “high” or “very high.”

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Validation vs. Verification: What’s the Difference?

Precisely

According to a 2023 survey by Drexel University’s LeBow College of Business , 77% of data & analytics professionals say that data-driven decision-making is a leading goal for their data programs. Yet fewer than half rate their ability to trust the data used for decision-making as “high” or “very high.”

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Data Scientist vs Data Engineer: Differences and Why You Need Both

AltexSoft

The movement of data from its source to analytical tools for end users requires a whole infrastructure, and although this flow of data must be automated, building and maintaining it is a task of a data engineer. Data engineers are programmers that create software solutions with big data. Programming.