Now that the stage is set, let’s get into some of the software trends that will ultimately be making an impact on an oil and gas organization. It is important to remember in this context, that emerging technologies are just that—emerging—and require operational and business rigor to achieve full value. Let’s break a few down:
Big Data and Cloud Computing
Organizations are dealing with more data than ever and have more access to make decisions. However, is there a 'dark side' to having too much data? Does Cloud Computing make harnessing Big Data easier?
The term “Big Data” shouldn’t be a term that creates fear or anxiety for an organization. The idea of Big Data is the general concept that you are bringing in various types of data from different sources to make business decisions, which for oil and gas organizations, can help decrease risk of downtime, and production losses and show areas if further business efficiencies.
Although technically two different elements of transformation, using Big Data can benefit from the power of cloud computing. For everyone keeping score, these are two LARGE digital transformation initiatives that are becoming commonplace. As cybersecurity practices become more robust, organizations with confidential / critical data are beginning to make the shift to cloud in to give their enterprise the best opportunity to harness the power of data.
Artificial Intelligence / Machine Learning (AI/ML)
The issue with AI/ML today is that everyone seems to be an expert. Between vendors, in-house solutions, open-source technology and more, it is extremely difficult to understand what AI/ML principles will work best for you organization. For us, AI/ML falls under the umbrella of Transformation—but also Big Data and Cloud Computing. But, how do you know where to leverage AI/ML and what parts of AI/ML will really work?
With the increase of data across oil and gas organizations, it is important to remember that leveraging AI/ML is not a one-time change. AI/ML, inherently, becomes smarter with the more clean data that you run through the technology. However, we have seen organizations attempt to do widespread AI/ML but gloss over the importance of defined data classification practices. In the coming years, we will see organizations leverage AI/ML for more pointed use cases, like business scenario planning, simulation of asset health events, and finding connections in data that is not obvious.
Predictive and Prescriptive Analytics
Another trend across all energy verticals is predictive and prescriptive analytics. Along with AI/ML, these analytics require clean and accurate data. Technology like Generative AI to produce prescriptive analytics is now being pushed by nearly every software vendor on the planet—but just how much impact can it have for your organization, and more importantly are you ready to adopt it?
With the push to leverage these analytics, techniques like Descriptive and Diagnostic analytics are becoming forgotten, but they shouldn’t be. If your organization is continuing a digital transformation journey, starting with Descriptive and Diagnostic analytics on a specific use case can provide just as much value. How, you ask? To bring this back to data, often when using new technologies, organizations do not have a consistent data hierarchy practice. Across systems and siloes this could lead to bad data being used in models. By starting with, or simultaneously using Descriptive or Diagnostic analytics, organizations can tease out any data concerns before making the promise of an “Artificially Intelligent” enterprise.