Access to data doesn’t guarantee access to transformative insights. Data science is an essential core competence that companies, particularly in asset–intensive industries, must achieve in 2025 to effectively extract knowledge from data.
Techniques like data mining, statistical analysis, and machine learning are important capabilities companies need data scientists to perform to uncover patterns and trends in the data that deliver more informed business decisions for companies in fields like manufacturing, oil, and gas.
Yet in today’s highly competitive manufacturing and energy industries, for example, the importance of data science and data scientists cannot be overstated. With the rise of Industry 5.0 and Internet of Agents, companies in this space are generating vast amounts of data from their production processes, supply chains, and technical data. This data holds valuable insights that can either amount to noise or be optimized to deliver competitive advantages in operations, including boosting production, increasing profitability, and minimizing waste.
How to Empower Your Data Scientists to Deliver Competitive Advantages
The effective use of data is not simply transforming asset-intensive industries, it is rewriting the rules of how we live and work.
With effective access to data, scientists and engineers analyze production, identify inefficiencies, and find solutions to improve productivity and lower costs. Make more with less and produced safer, more scalable, and more efficiently. That’s the goal, but how? Enable your data scientists to create value utilizing the same real-time data that engineers and operators use. solutions such as process agents.
Right now, most data scientists are off the plant or factory floor and remotely accessing outdated data in a disparate and incohesive manner that commonly lacks context.
In citing a recent press release, Trey Lowe, Devon Energy’s Vice President and Chief Technology Officer said that,
“As a leading oil and natural gas company, we [Devon] have a vast amount of data coming in from the field,” and need to “enhances our ability to integrate this data, enabling engineering and data scientists to optimize our performance."
One of the key challenges to achieving that goal is tapping into siloed and on-premises data by moving that data into an environment where data scientists can quickly access and leverage crucial, near-real-time operational information.
Virtually Move Your Data Scientists to the Factory or Plant Floor
To solve this issue, Radix developed a solution that seamlessly gathers data from existing data historians and makes it readily and securely available in event hubs or message brokers on either a private cloud or on-premises environment. Called Leafcutter, this solution enables users, like data scientists, to consume time series data on-demand and in real-time, with a proven ability to stream upwards of 250,000 tags. By streaming up to 70 million data points per day to your data lakes, companies can provide plant or factory floor access to their data scientists from the convenience of their work environments.
This capability bolsters a data scientist’s ability to deliver services such as predictive maintenance since they can more quickly analyze sensor data from machines and equipment, and therefore, better predict when an asset is likely to fail or require scheduled maintenance before a breakdown occurs.
Leafcutter also helps companies avoid costly downtime and reduce maintenance costs by empowering data scientists to extend the life of machines and equipment more accurately.
Hit 2025 Running
Contact Radix today at Connect with us | Radix to get started with Leafcutter and take the role of data scientists in your company to a whole new level in 2025 by leveraging the power of data to gain a competitive edge.
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