Syncsort recently conducted a survey to explore data quality and organizations’ confidence in data across their enterprise.

Though most respondents rated their organization’s data quality either as good (38%) or very good (27%), the software company’s survey revealed a disconnect around understanding and trust in the data and how it informs business decisions.

Syncsort reported on Wednesday that 69% of respondents stated their leadership trusts data insights enough to inform business decisions, yet they also said only 14% of stakeholders had a very good understanding of the data.

Of the 27% who reported sub-optimal data quality, 72% said it negatively impacted business decisions.

The survey determined the top three challenges companies face when ensuring high-quality data are multiple sources of data (70%), applying data governance processes (50%) and volume of data (48%).

Survey findings noted about three quarters (78%) have challenges profiling or applying data quality to large data sets.

About 29% of participants said say they have a partial understanding of the data that exists across their organization, while 48% said they have a good understanding.

Syncsort found that fewer than 50% of respondents take advantage of a data profiling tool or data catalog.

Instead, respondents shared that rely on other methods to gain an understanding of data, with more than 50% using SQL queries and more than 40% using a BI tool.

Of those who reported partial, minimal or very little understanding of their data, the top three attributes respondents lacked visibility into were:

— Relationship between data sets (63%)
— Completeness of data (56%)
— Validation of data against defined rules (56%).

Of the survey participants who reported fair or poor data quality, wasted time was the No. 1 consequence (92%), followed by ineffective business decisions (72%) and customer dissatisfaction (67%).

Syncsort went on to mention 25% of respondents who reported sub-optimal data quality say it has prevented their organization from adopting emerging technology and methods, such as artificial intelligence, machine learning and blockchain.

Only 16% of respondents are confident they aren’t feeding bad data into artificial intelligence and machine learning applications.

The survey also revealed 73% are using cloud computing for strategic workloads, but 48% of them have partial to no understanding of the data that exists in the cloud. A total of 22% rate the quality of their data in the cloud as fair or poor.

“This survey confirms what we’ve been seeing with our customers — that good data simply isn’t good enough anymore,” Syncsort chief technology officer Tendü Yoğurtçu said. “Sub-optimal data quality is a major barrier, especially to the successful, profitable use of artificial intelligence and machine learning.

“The classic phrase ‘garbage-in, garbage-out’ has long been used to describe the importance of data quality, but it has a multiplier effect with machine learning — first in the historical data used to train the predictive model, and second in the new data used by that model to make future decisions,” Yoğurtçu continued.