CHICAGO, IL — September 9, 2020 — Uptake has announced it will be working with Reliability Dynamics to help customers better collect and prepare equipment data in order to fully gain the benefits of advanced analytics, AI, and machine learning. Reliability Dynamics works with asset-intensive companies to apply international standards to industrial data systems, resulting in high-quality organization and preparation of equipment and maintenance data. The partnership will join Reliability Dynamics’ ability to standardize complex datasets with Uptake’s ability to generate advanced analytics using such datasets.
“Across all heavy industries, corporations are failing to capitalize on the tremendous potential of artificial intelligence and machine learning,” shared Mark Benak, Vice President of Business Development at Uptake. “Uptake has proven to help customers use AI and ML to predict and prevent asset failures and unplanned downtime. By ensuring data integrity, our powerful data science models enable our products to deliver actionable insights to users that mitigate risk, optimize maintenance strategy and asset performance, reduce costs, and enhance safety.”
There’s a common problem that exists across all industries: a lack of data integrity. Unorganized and inaccurate data, as well as data living in disparate systems, hinder advanced analytics. Before customers (or any company for that matter) can reap the benefits presented by AI and machine learning, they must have a clear data taxonomy and high-fidelity in their data.
The partnership with Reliability Dynamics will allow Uptake customers across O&G, manufacturing, mining, and power generation to collect precise and accurate equipment performance data from work management systems and field personnel, thereby fueling Uptake’s data science models with quality data and resulting in more accurate insights to optimize customer’s operations. Uptake customers will have the option of using Reliability Dynamics’ ISPM® toolset in their enterprise software to increase the quality of their field data (see Figure 1).
