The data foundation imperative for energy
September 1, 2020
September 1, 2020
Today, there are a new set of priorities for the oil and gas industry. These are driven largely by the looming global shift from fossil fuel-based to renewable energy sources. Future growth amid this energy transition requires a total transformation of cost structures with new customer-centric operating models, the maximization of every molecule’s value across the value chain, and the use of data as an engine of growth.
Industry leaders are starting to invest in new data capabilities, artificial intelligence (AI) solutions and cloud infrastructures to help them gather, process and use the information at their disposal. But thus far, they’ve captured just a fraction of their data’s value.
To enable better, faster decisions at this critical juncture, energy companies must embark on (or accelerate) their intelligent enterprise data journeys. With the energy transition fast approaching, there’s not a moment—or data-driven insight—to waste.
Holistic intelligent enterprise data models help leaders enhance agility and resilience, boost competitiveness and enable sustainability.
A small number of leading energy players are aggressively investing in what it takes to get their data houses in order. They are unlocking the potential of analytics by overcoming the data foundation maturity chasm. Yet, for most energy companies, the promise of clean, connected data and end-to-end AI-enabled business intelligence has not yet materialized. Why? Because companies’ data foundations and operating models have not yet achieved the maturity required.
Energy industry's data foundation maturity
Figure 1. The existing data maturity gaps for energy companies are significant
These maturity gaps are typically caused by five constraints. Analytics leaders are free from these:
With these foundational elements in place, an oil and gas company can enter the final stretch of its intelligent enterprise data journey. Advanced AI solutions can be deployed to overcome previously insurmountable hurdles. Entirely new ways of working can be implemented, based on new AI-driven insights. This is when the organization finally realizes the full potential of its data.
There are six key attributes of an intelligent data foundation:
Bring together the set of diverse, prioritized data sets that will unlock trapped value when blended together
Make data searchable and provide the tooling and collaboration capabilities to create cross-functional insight that unlocks trapped value
Enable addition of business context to data, boosting its usability, and provide visibility of its changes from point of origination to consumption boosting trust in data
Deploy a flexible ‘data supply chain’ architecture underpinned by modern platform architecture and engineering for a fast, scalable path to production
Combine risk plus value lenses to secure critical data, implementing data security and privacy controls without impacting data exploration
Continuously innovate, implementing a combination of cloud platforms, modern data and machine learning engineering, and AI techniques to enable speed, automation and agility
While the energy industry has lagged others on the journey to data maturity, energy companies now have the chance to close the gap. With the right focus and investment, they can leap forward with their data and analytics transformations and achieve in 12 to 18 months what would have taken more than five years in the past.
What steps can energy companies start taking today to accelerate the transformation that is needed?
Harnessing data and AI is not a luxury anymore. It is a matter of survival. The question is no longer “when should we start?” but “how fast can we get it done?”
About the Authors