How often have you heard leaders state we need to treat data as an asset?
In today’s world, every company is a data company. It’s integral to almost every decision we make, and data management and quality can make the difference between a successful field development that delivers as expected or millions sunk into a project that doesn’t return what it should.
But if we’re honest with ourselves, we rarely treat data with the same care as our other business assets.
In a mature small or medium-size E&P firm you might have terabytes of raw data, all describing dozens of different platforms, metrics and KPIs. That might be spread across multiple sites worldwide, each with their own approaches and measurements, with everything flowing through a complex hairball architecture of IT applications.
All too often the result is that data gets handled in silos. People look at what matters to their particular function or region and don’t always share insights beyond that. Data cleanses happen on one application without thought for the impact that will have on the business as a whole. And most of all, we overlook the potential value not just for efficiency, but also for driving strategy and change.
In order to fully harness the power of their data, companies need a roadmap to digital transformation – one which should include these five steps.
1. Lead the change
Leadership is vital.
While every company will have a CEO, COO and CFO, very few have a Chief Data Officer. But this is starting to change.
Forward thinking firms are now appointing CDOs or VPs of data analytics, underlining the strategic importance of data to company growth – and recognising that traditional C-suite appointees may not have the skills to drive change in the digital world. In reality, companies need a blend, with key personnel that are digitally savvy but still understand the core business.
Often the changes required are less technical and more cultural, and we see that the most successful companies are the ones with senior leaders tasked to deliver cultural changes.
These leaders don’t need to fear the kind of cross-functional, hairball architecture IT systems that make data in oil and gas so complex. New software tools such as digital twins can deliver enriched dashboards that track pan-organisation activities in real time – equipping leaders with the information they need to deliver improved efficiency.
The next step once data leaders are at the board table is for leadership teams to focus on simplifying systems to ensure better data quality. This will involve:
- Standardising how work is done to reduce variations in different regions and departments
- Reducing the number of sources and improving their quality
- Implementing management systems that are easier to control, measure, and improve
- Training people to a new way of working and how best to treat data as an asset
2. Develop a business case
It’s easy to present a business case for developing an oil and gas asset. You have your information about what you expect the field has to offer, you have an estimate of the investment the field will need, and it’s a simple case of showing that the returns justify the resources.
However, data driven initiatives often have many interlinked parts, and while projected benefits might be substantial, they don’t always land in the department which creates the corresponding data. Unless these are clearly articulated, leaders have a harder time seeing the value.
Again, leadership is vital. If, for instance, the executive committee knows that better data controls will result in a 20 percent improvement in overall staff productivity, those leaders will ensure that all departments get on board, whether they stand to benefit directly or not. For instance we saw improving operational data for a major led to the biggest benefit occurring in the efficiency of the finance teams that report on the data.
3. Tackle the root cause
When problems rear their head, we often create “data heroes” of the people who spend their nights and weekends trying to correct bad or find missing data.
But far better than having data heroes fly in is to tackle the root cause issues. To do that, we need to invest in root cause analysis to find out where this bad data is coming from and why.
As a data customer, leaders should be intolerant of accepting bad data. They should give clear feedback to those who created it so that they can improve the next business cycle. With constructive feedback and by breaking down siloes, leaders can make sure data is delivered in the right format, the right way, every time.
A routine area we see this occurring is the end of year budget and production forecasting cycle. Often we see year on year last minute delivery of data and “management changes”, leading to rework and frustrations. Instead of taking time to learn and fixing the root cause, this frustration is repeated year-on-year. The budget process has not been improved upon.
4. Measure what matters
It’s easy to think that when it comes to data, the more the merrier. But when you have potential terabytes of data across your organisation flowing from multiple data sources, there can be such a thing as too much.
Data should be about making better, more informed decisions – so it becomes a problem if you have so much data to sift through that it leads to decision paralysis instead.
What firms need to keep in mind is “the critical few vs the trivial many”. In other words, what makes the most difference as a value driver, and what data is just adding more things to evaluate?
For example, the focus in oil and gas is often around seismic data. This makes sense, as the volumes are enormous, but seismic is only used by geophysicists and has limited influence over the rest of the value chain. On the other hand, well data is vital not only for geophysicists but also drilling and finance teams, and therefore provides more value.
To achieve this you need to invest in data literacy, giving people the tools to identify which metrics matter most to business strategy and contribute to where the firm wants to go. Most of the value for the company is going to be in understanding forecasts, costs and inventory – so make that a focus.
5. Keep learning
In any business, lessons are learned over time, and all good companies look to capture these learnings so the next business cycle can be improved upon.
But have you ever performed a look back on your data quality? Have you ever tried to learn and improve so you’re ready for the next data type? And if not, why not?
Leaders have two primary responsibilities: first to run the business and second to improve how the business is run. Data quality is vital to both. Getting high quality data the first time avoids unnecessary rework as well as more reliable business insights.
It’s tempting to look at new technologies to help modernise and unlock new ways of working. But ultimately the technology will be of limited use if its inputs are not correct. But the other side of the coin also holds true – once you have systems generating high quality data, you’ll be able to plug in AI and machine learning technologies and find real traction.
For more on how to create value from your data processes, listen to our podcast on digital innovations in oil and gas with Lewis Gillhespy and Geoffrey Cann, available on Spotify or Apple Podcasts.
