The Experts Tackle Automation and Data as Business Drivers

Blog     · 5 MIN READ

Posted on January 05

Back to blog

HUVR CEO Bob Baughman hosted a panel of industry experts to discuss the state of automation as a tool to drive business. We have condensed a tremendous amount of information, lessons learned and wisdom into a humble blog post.


It has been, all things considered, a very challenging year for businesses that don’t deliver things to your doorstep—which is not to say those businesses haven’t faced hardship as well. In the energy sector specifically, saving capital and increasing efficiency have gone from important to life-sustaining for most operations. 

This does not mean that their long-term goals have changed: the march toward digitization continues. In fact, in many cases, it has moved from a methodical walk to a full-on sprint. With safety concerns chivvying the process, it is even possible to effect change where it might have been resisted in the past—the massive increase in remote work has made digital information even more critical. 

That being said, it’s important to keep the end in mind and make focused changes toward a larger goal where it makes the most sense, operationally and financially. Increased use of digital inspection tools and remote management are important, but so is a quick return on investment. So make sure that you’re staying mindful as you make decisions about digitization that will affect your operation long term.


The view for small-to-midsize companies is very similar to what is seen by large, enterprise operations. How can you quickly adapt to changing market conditions, often quarter-to-quarter? How can you ensure your decisions are fast, informed and intelligent? Ultimately, how do you stay competitive in market conditions that are about as complicated and fraught as they get?

The answer, unsurprisingly, is good data (more on this qualifier later). Leveraging your data to be competitive is massively important—the more data you have, the better your decisions will be. Coupled with this is automation, although automation is a tool and not an end itself. With smart automation and good data, you can make the best possible business decisions for your company and be as agile as possible as we move towards the next stage in the evolving situation.

In essence, the business case for automation and digitization has not changed: more efficiency, improved performance, solid analytics, etc. The timeline has just sped up. 


Speaking of automation, putting a specific definition to AI and ML can be tricky. Is it simply going beyond basic statistical analysis? And while we’re at it, how do you judge the success of automation in general? Hard KPIs are key to knowing if you are automating for the sake of automating or really making an impact on the bottom line. 

All automation is targeting either savings or earnings: which is your goal, and how will your digitization efforts support that goal? And are those efforts incremental and disjointed or cohesive and smooth? The difference may be as simple as good management, explaining each stage to the stakeholders and ensuring you have complete buy-in.

When thinking about AI/ML, it’s important to remember that no amount of automation or digitization can remedy bad data. If data is the answer to many business problems, it is critical that the data is good. If you have a less mature digitization effort, it may be smart to target areas where the data is proven to be solid, like real-time analytics, instead of trying to digitize and correct problems simultaneously. Bad data leads to bad outcomes, which usually trigger a lack of trust in the technology that produced them. Instead, seek out the most solid and easy-to-implement use cases first. Don’t walk past the low-hanging fruit.


Our panel dropped more than a few pearls of wisdom, so we thought we’d share a few here in summation:

  • Understanding the processes of whatever you’re trying to automate or digitize is crucial; in fact, it’s impossible to do effectively without. The true value comes from a combination of deep analytics and subject-matter expertise.
  • Don’t create a solution in search of a problem; embrace the problem fully and let it drive the solutions.
  • Data is cheap to produce, limitless in volume and inherently has no value. Falling into the thinking that data has value in-and-of itself is a common error. Data’s value comes from using it to create insight and linking that insight to decision. Success comes from action informed by information.


Tim Westhoven, Technology Scouting and Ventures Advisor, ExxonMobil
Caryn Ogier, Project Manager, Operations Technology, Noble Energy
Hani Elshahawi, Deepwater Digitalization Lead and Formation Testing and Sampling Principal Expert, Shell
Ravi Srivastava, Vice President, Data and Ops Technology, CNX

Bob Baughman, CEO, HUVRdata

Share this article

Back to blog