In a large-scale industrial setting, the implications of poor-quality data can be unforgiving: Increasingly industrial leaders rely on data to drive operational decisions and if these decisions are based on poor data, it can result in a poor efficiency, breakdown of physical assets and even compromised on-site safety.
We recently conducted a survey in collaboration with the Gartner Peer Community in order to test the wider demand for data quality solutions among decision-makers in the oil & gas, chemicals, manufacturing, mining, power, and pharmaceutical industries. We wanted to find out to what degree technology leaders are prioritizing tackling their operational data issues.
The results are in, and they are promising. Below are few key highlights from the report, and some of our insights into its results. You can also download the full report here.
Key insight 1: An Escalating Demand for Data Quality Solutions
83% of decision-makers reported a significant increase in their demand for data quality solutions over the past six months. This highlights that the rapid growth in the volume of data that industrial companies are managing is amplifying the need for a scalable way to manage data issues.
Key insight 2: Reporting is Driving Demand
47% of respondents cited their need for better real-time reporting and long-term operational insights as the main driver for wanting to improve their data quality. This suggests that industrial companies are starting to look at their data as a tool to improve decision-making accuracy, and gain a competitive edge in the market. Visit our industries section to learn more about how APERIO addresses industry-sepcific data challenges.
Key insight 3: Revenue Losses Attributed to Poor Operational Data Quality
85% of respondents reporting some degree of revenue loss due to poor operational data quality. Notably, 27% of the respondents cited significant revenue losses, emphasizing the detrimental impact that poor data quality can have on industrial companies’ bottom line.
Key insight 4: Lack of Internal Resources is the Leading Contributor to Poor Data Quality
65% of respondents attributed poor data quality to insufficient in-house resources. This finding reenforces our own experience. We have found that even global, well-funded industrial companies often lack the necessary expertise, tools, or processes to ensure data accuracy and consistency across all their assets. You can read about some of our customer success stories here.
Conclusion:
The survey conducted by APERIO and Gartner Peer community offers a compelling snapshot of the surging demand for data quality solutions in large industrial companies. To view the full survey results, you can download the full report here.