The data foundation imperative for energy
September 1, 2020
RESEARCH REPORT
In brief
In brief
- 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.
- Accenture shares steps energy companies can start taking now to accelerate the transformation that is needed for the journey to data maturity.
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.
Mind the gap. Then close it.
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:
- Data is generally not considered a key element of business strategy. An integrated data strategy must be in place to support the new business strategy. In many cases, the data strategy and business strategy will be indistinguishable.
- Data platforms and connectivity are typically viewed as IT issues, not a business priority.
Data siloes, complex tooling and monolithic architectures constrain the ability for energy companies to perform cross-functional analytics. Leaders invest in an "intelligent data foundation" to overcome the platform and data engineering barriers. - Data governance is ill-defined or inconsistently applied.
Most oil and gas companies still operate in a siloed manner. Enterprise-wide data practices and usage/security protocols are quite rare. Where they are in place, they are typically maintained by understaffed teams relative to analytics leaders. - Data literacy is low.
New skills will be needed to align with these capabilities and governance. - Change management is under-appreciated.
Enterprise-wide adoption of analytics, AI and other data tools represent a major cultural change within an organization. How leaders think about data, the types of problems they can solve, and the innovative solutions they can scale are by-products of a truly data-driven organization.
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:
Provide rapid access to priority data sets
Bring together the set of diverse, prioritized data sets that will unlock trapped value when blended together
Enable data exploration
Make data searchable and provide the tooling and collaboration capabilities to create cross-functional insight that unlocks trapped value
Augment data trust and usability
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
Deliver a fast-path from ideation to deployment
Deploy a flexible ‘data supply chain’ architecture underpinned by modern platform architecture and engineering for a fast, scalable path to production
Implement data security and privacy by design
Combine risk plus value lenses to secure critical data, implementing data security and privacy controls without impacting data exploration
Apply innovations in platforms, engineering and AI
Continuously innovate, implementing a combination of cloud platforms, modern data and machine learning engineering, and AI techniques to enable speed, automation and agility
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Data for speed. Data at speed.
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?
Develop an analytics vision
Include use cases with aspirational performance improvement targets across the enterprise
Perform an audit across the five key capabilities
Identify the most critical gaps to deliver the vision
Design a robust operating model
Encourage the use of analytics and new ways of working
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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?”
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