Optimizing Oil and Gas Data Analytics
Oil and gas isn't lacking for data. It's swimming in it. And yet some companies are still leaving heaping servings of business intelligence on the data analytics table. So what gives? While we don’t want to paint with an unfairly broad brush and label the entire industry as behind the advanced analytics times, how can some – dare we say many – energy companies still suffer beneath the weight of frequent, often severe information gaps and silos?
The answer to these critical questions revolves around data democratization. Or lack thereof, in the case of the oil and gas companies in question. But we're about to get to the root of the underlying issues and, most importantly, discuss what such enterprises can do to right the data ship and start leveraging the type of data analytics that truly move the profitability needle. Spoiler alert – it all begins in accounting and finance.
Big Data and the Oil and Gas Industry
Oil and gas is at a crossroads, the rope in a tug-of-war between often diverging dynamics – volatile commodity prices, skyrocketing consumer fuel costs, ESG concerns, regulations, domestic and global politics. We could go on and on, but you get the drift – a lot is happening in energy. And there's no sign of these forces letting off the gas any time soon, pun absolutely intended.
Put another way, the sector – upstream, midstream, and downstream all included, just in varying ways – doesn't need any help creating a complex, sometimes cantankerous environment for itself since those external dynamics already exert more than enough operational curveballs to go around. Even so, in many respects, the oil & gas industry can't seem to get out of its own way, particularly regarding data and analytics.
But that's not to say oil and gas is data averse, of course. Quite the opposite, in fact. Few industries rival energy for the sheer volume of data it creates in day-to-day operations, with geological, reserves, and seismic data heading that list. Throw in the wealth of information flowing from IoT (Internet-of-things) and predictive maintenance sources, and it's a downright informational smorgasbord. The problem, as we set up top, is the democratization of those mountains of information.
In reality, stakeholders – intentionally or not – build barriers and segment that massive amount of data, the different teams involved usually not wanting to share their data with others. Much of that stems from the somewhat autonomous nature of the various groups within individual companies, typically having the power to make decisions on their own and, therefore, never have to play with the other kids on the playground.
Similarly, teams sometimes choose and implement applications without much thought on how they might connect with other systems or groups. For example, one of the more common mishaps is a sales platform that doesn’t connect to an ERP. In this case, companies will generate duplicative, often incorrect inventory and resource data. And that’s not great.
Thus, when there’s little communication between the different groups involved in operations, the enterprise itself never reaps the rewards of a genuinely data-driven culture. Obviously, when it comes to revelatory, forward-looking analytics to guide operational decision-making, the more relevant data leadership has, the better. So, for oil and gas, subsurface data collection isn't what's preventing companies from reaching business intelligence nirvana, for instance. Instead, it's the connectivity between the many groups and stakeholders.
In a truly democratized data environment, the stakeholders would have equal access to data permissions and rights. Ultimately, many oil and gas companies lack a business-wide data strategy that consolidates, normalizes, and reports on data, drives more nuanced insights, and generates business intelligence. And that’s the crux of its festering data analytics woes.
Technology and Capital Allocation
It's not just the data and systems infrastructure that impedes advanced analytics in energy. Being such a capital-intensive sector, technology too often gets set aside for the millions of dollars required to be a functioning E&P company.
With so much attention going toward shareholder value and profitability, it's easy to see why oil and gas, as an industry, focuses so much attention on drilling wells and transporting that black gold. And while we'll take a closer look at the intricacies in just a bit, continually kicking the technology can down the road is often a shortsighted decision that actually sacrifices long-term shareholder value for short-term benefits.
Data Science in Accounting and Finance
Enough of the negative talk, though. Now let's focus on what oil and gas companies can do to start leveraging analytics on an enterprise-wide basis. As we're seeing more frequently in Embark's recent engagements, energy companies now seem more open to digital transformation than they were just a few years ago, and that's certainly good news.
Granted, between the pandemic, exploding prices at the pump, wars, politics, and everything else impacting the industry, it only makes sense that companies would use these challenges as motivation to find better solutions and processes. And ideally, this process starts within the finance organization, a historically underserved area in technology and data within the energy sector.
However, thanks to the potential efficiencies and value a finance transformation can drive – not to mention the bevy of solutions already available – accounting and finance are an obvious place to begin a transformational process, one that spreads across the enterprise with time. In the end, you're left with a data-driven culture that seamlessly shares information across different groups and smashes the gaps and silos preventing fully informed decision-making.
Data Analytics and Employee Engagement
Transformation and data culture aren't just about the usual list of buzzwords and tools – automation, machine learning, data visualization, artificial intelligence, and algorithms, amongst others. Yes, operational and production efficiencies, improved data quality and timeliness, and deeper insights are essential. However, they also have definite spillover effects that can dramatically boost employee engagement while helping retain top talent in an environment where qualified people are hard to come by.
For example, simply pulling production data and funneling it into a data dashboard provides benefits beyond more comprehensive, nuanced operational insights. Yes, real-time data in an industry filled with real-time dynamics is never a bad thing. However, data dashboards make it easier for your people to make a tangible difference in your company's operations.
With convenient, seamless, timely information provided by dashboards, the people in the fields don't have to wade through endless spreadsheets to make heads or tails of what's happening – not that they would, anyway. Because, as you know, field operations prefer to turn wrenches rather than sort data columns. Likewise, since data dashboards and the information they report are automated, you won't need an army of accountants to manually input and sort operating data.
The point is, a single tool like a data dashboard can improve people's lives across the organization, from the field to the back office and many in between. As you know, engagement levels rise – along with productivity metrics – when people feel more satisfied with their work, the ultimate win-win situation.
Simply put, dashboards and other analytics tools help people feel more fulfilled. And since the boundaries between work and home lives erode a bit more every day through the wonders of cloud computing and remote working, a more fulfilled work life usually correlates directly with a more fulfilled home life. The result, naturally, is a more fulfilled human being that's actively engaged in helping your organization thrive.
Make the Finance Organization the Ultimate Business Partner
Let's fast-forward a touch and assume you're already on the data democratization bandwagon, cleaning and centralizing your data environment, streamlining your systems, and automating essential but resource-heavy accounting and finance processes.
At this point, you're knee-deep in your finance transformation and word is starting to spread across the company. Now, rather than an afterthought, accounting and finance is the in-house poster child for all things analytics, a shining example of everything data science has to offer. This prompts other teams and leaders to come to the accounting and finance functions for critical data, along with best practices in transforming their own departments.
This transformation makes the finance organization the ultimate business partner for the company, effectively becoming a hub for leadership and decision-making. In essence, accounting and finance have become the foundation of a data-driven culture that methodically spreads across the enterprise.
Further, as more groups enter the fold and integrate their own insights and ideas, there is a groundswell that eventually transforms the entire company. And it all started with accounting and finance – who would've thunk?
Becoming More Predictive, Agile, and Efficient with Big Data Analytics
So far, we've discussed the limitations oil and gas companies face in better leveraging analytics, as well as the critical role the finance organization can take in alleviating those limitations. Now, let's look at some specific benefits you stand to realize through data analytics, aside from the several we've already examined.
Discounted Cash Flows and Capital
Needless to say, it's tough to find capital these days. Thus, discounted cash flow forecasts are under a particularly bright spotlight since they play such a pivotal role in acquiring capital for oil & gas exploration and production.
But what happens when the petroleum engineering, reservoir engineering, and drilling operations teams – not to mention everyone along the supply chain in oil and gas operations – can't provide finance with the most accurate and complete digital oilfield data? Obviously, that can negatively impact those critical forecasts, which, in turn, can make it even more challenging to fund the drilling program, for instance.
Thus, providing accurate, relevant, real-time data to a modern, data-driven FP&A team and its predictive models can be a real difference-maker in acquiring the capital you need for operations. Of course, that means centralized data repositories, streamlined systems, and efficient processes between the data sources and finance are incredibly important during this very real struggle for capital.
Not trainspotting, because that's something entirely different. In the case of data analytics, efficient data collection and democratization across your company's many nooks and crannies provide finance with a more global perspective of your operations. Put another way, far-reaching data analytics relies on you eliminating the gaps and silos that are so prevalent in many of today's oil and gas companies.
With these far-reaching insights, finance can identify even subtle trends in massive real-time and historical data sets, helping you address potential issues before they turn into full-blown, four-alarm production fires. And since a post-transformation finance organization is the data and decision-making hub of the enterprise, there is no other group better equipped to identify such issues as finance across various use cases.
Start with a Data Strategy
Depending on where you're starting on the predictive analytics highway, these musings may or may not seem overwhelming at first. Therefore, while it's impossible to provide personalized advice through a single blog, we recommend beginning your journey toward data analysis supremacy with a rock-solid data strategy.
Although your first inclination might be to knock on IT's door to develop this strategy, we recommend keeping it within the finance organization as much as possible. After all, finance owns most of the data you will be using, not IT. Also, IT won't be familiar with the different processes involved in the development, implementation, and optimization of your data analytics tools and workflows.
So, to begin, we recommend looking at some of our previous insights on structured and unstructured data, repositories, and systems to get a handle on the current state of your data environment. From there, our blog on project and change management will help you get the wheels turning.
Now, as digital transformation takes root and spreads, you'll eventually develop the need for data scientists, architects, and engineers, at least if you want to live the transformation life. But that’s for a future discussion. In the meantime, if you need some guidance putting the pieces together, Embark’s Finance Transformation team is always ready to step in wherever and whenever you need us. It's what we do.