Data quality is becoming a hot button issue as more and more businesses aggressively move into artificial intelligence (AI)—both generative and operational—which requires massive amounts of accurate and timely data.
The rise of large language models (LLMs)—both publicly available as well as contained within enterprises—to support business decision making and customer communications means data is being pressed into service in new and highly demanding ways as training data and real-time streaming feeds.
However, ask data administrators and managers about the state of their data, and it becomes evident that enterprises are ill-prepared for the times ahead.
Confidence in the integrity, accuracy, and trustworthiness of data has been falling in recent years, a new survey of 202 data decision makers by Unisphere Research, a division of Information Today, Inc. finds. The survey, titled “Unfinished Business: Taking On The Data Quality Challenge In The Age Of Ai,” conducted in partnership with Melissa, reports no letup in the growing confidence gap in the data needed to support next-generation initiatives.
The survey shows confidence in data quality is slipping. Only 23% express full confidence in their organization’s data—down 7 percentage points from a similar survey conducted 2 years ago.
Why does data quality appear to be waning, and issues associated with data quality on the rise? Evidence from this survey suggests it is likely tied to the growing demand for AI or analytics workloads, which require the best data at a moment’s notice.
Confidence in data quality is slipping, yet organizations have taken their eye off the ball when it comes to addressing data quality issues. Lack of organizational support for data quality efforts—along with determining ROI—may be at the root for lagging progress in such efforts.
With such a panoply of competing challenges, it has grown more difficult for data quality proponents to secure organizational support for their efforts.
In a leaner economy, demonstrating ROI is vital, the key to getting executives’ attention. Unfortunately, it’s often difficult to predict and plan data quality initiatives, and this has continued to be the leading challenge, cited by 58%.
While the use of cloud for data storage and management has expanded, moving to the cloud has not resolved data quality issues, showing little or no improvement from previous surveys.
Data quality needs to be a shared responsibility. Promote ownership and responsibility for data assets beyond the IT department, from anyone who touches the processes that involve data, such as AI.
Often, corporate cultures discourage such shared responsibility, and the silos that have resulted need to be torn down.
To read the full report, go here.