I have spent my career connecting data to decisions. Every company is looking to maximize the value of their investment in data within the constraints defined by their value levers, whether that is Safety, Carbon Footprint, Productivity, Efficiency, or Quality.

Today is Earth Day in the US, a day set aside since 1970 to show our support for environmental protection. Companies in energy and manufacturing are answering the call to be more sustainable and embracing environmental, social, and corporate governance (ESG).

Maintenance and reliability teams have a key role to play in accelerating the transition to low-carbon energy. A portion of the gains from traditional energy operations is being reinvested in alternative energy ventures.

We recently announced that Uptake Fusion is supporting Microsoft Azure Data Explorer. What does that change mean for Uptake Fusion users, data wrangling, feature engineering, and getting the most out of industrial data?

Having timely, relevant data empowers business decision-making. The dilemma comes from challenges with data access, which is especially true with ESG (environmental, social, governance) programs. Without robust ESG reporting based on data collected, companies struggle to meet compliance requirements, follow ESG frameworks and ratings, or address investor expectations around sustainability.

How can engineers and data scientists get ROI from their predictive maintenance program? In this blog, we’ll run through some of the potential uses of industrial data, and how the resulting industrial analytics form part of a cohesive maintenance strategy.

The areas of Artificial Intelligence (AI), Machine Learning (ML), Industrial Data Analytics, and Industrial Internet of Things (IIoT) seem to be the latest ‘Last Frontier’ of equipment reliability. There is an attitude out there that contends that the adding of sensors will automatically result in an improvement in reliability.

As manufacturers look to meet the competitive mandate to digitally transform their operations, the digital skillset of their team will be critical to their future success and resilience.

Business demands have forced manufacturers to be more agile. Smart manufacturers are adopting technology, including digital twins, to move faster.

When it comes to industrial machines, context looms large. Especially when the difference in data signatures between various operating contexts, original equipment manufacturers (OEMs), maintenance histories, and productivity thresholds reveals whether one asset needs inspection, and another is performing optimally.