Breaking Data Silos with AI
For Royal Dutch Shell, adopting a data-driven culture was very challenging.
Getting the diferent departments on board, breaking down siloes and building networked teams all proved to involve major changes to the way Shell personnel had worked in the past. There was resistance in changing the way teams operated, and moving away from statisticbased decision to data-based decisions.
As the head of the legal department for Royal Dutch Shell put it: “The best-planned process [of data-driven decision-making] in the world can be derailed by lack of stakeholder buy-in. Both execution and culture eat strategy for breakfast. Thus, as part of the panel review outreach initiative, senior legal leadership participated in a follow-the-sun communication workshop that focused on integrating legal units scattered around the globe and fostering a cohesive team ethos that cuts across time and distance.”
But Shell’s successfully made the transition. As a study by Accenture and Microsoft in 2016 showed, 91% of oil industry executives felt that decision-making at their companies had been substantially improved by a data driven approach.
Royal Dutch Shell is one of the four largest energy producers in the world. Operating in more than 70 countries, Shell employs 92,000 people, produces 3.7 million barrels of oil per day from its 22 refineries around the world, and 57.1 million tonnes of liquid natural gas per year.
In its Upstream division, Shell focuses on the exploration for new liquids and natural gas reserves and on developing major new projects where technology and know-how add value for resource holders. In its Downstream division, the company focuses on turning crude oil into a range of refined products, which are moved and marketed around the world for domestic, industrial and transport use. In addition, they produce and sell petrochemicals for industrial use worldwide. Shell’s oil sands mining activities in North America are also part of the Downstream organization.
The search for new hydrocarbon deposits demands a huge amount of materials, manpower, logistics and costs. Drilling an oil well can cost in the hundreds of millions of dollars
Data Driven Energy
For some time now, Shell has been developing the idea of the ‘data-driven oilfield’ in an attempt to bring down the cost of drilling for oil. But Shell is also using big data to redefine its approach to consumer products and in improving the efficiency of its transport and other operations.
“Oil and gas industry leaders continue to look to digital technologies as a way to address some of the key challenges the industry faces today in this lower crude oil price cycle,” said Rich Holsman, global head of digital in Accenture’s energy industry group. “Making the most of big data, the Internet of Things and automation are indeed the next big opportunities for energy and oilfield services companies, and many are already starting work in these areas.”
They are increasing investments in enabling people and assets, with a growing emphasis on developing data supply chains to support analytics projects that can improve efciencies, manage cost and provide a competitive edge.
Focusing on tangible value
Shell focuses their digital investments on areas where they see tangible business value. In the short term, given the low oil prices, oil and gas companies are focusing these investments on areas that deliver more immediate benefits in cost reduction. This includes lower operations costs through increased worker productivity with mobility, lower infrastructure costs through the use of cloud technology and better asset management through analytics. Over the next three to five years, investments are expected to shift to focus on areas that deliver the greatest long-term value, such as more comprehensive predictive operations management capabilities.
Building the right skills in teams
As a result, they are engaged in building the right skills in teams. Shell has implemented a mandatory training programme for data-driven communications, according to Sherine Yap, global head of CRM at the company. Yap explains that business-wide initiatives help create an understanding of information and insight.
The training is, above all, about, ‘understanding’, so sharing insights from data and reports acquired at the front lines are used to meet customer demands.
She says communication is crucial and that language barriers often persist. “Marketing and data teams should move closer together and explain in simple terms the likely outcomes of the insights being creating,” says Yap. “Working in that way has allowed me to take information, package it up and share it with the wider teams across the business.”
Very informative and on the money, dealing with the behaviors and actions required to get a culture working that will deliver real business benefits of AI.