Data Monetization in Oil and Gas Sector

Demystifying data monetization

Every company operating, trading, and, interacting with suppliers, vendors, and customers is a data company. It generates a huge amount of data from various nodes like purchases, settlements, operations, services, and customer interactions. While data has value, but the insights that are generated from the data substantially increases that value. Consider an upstream asset fitted with smart tools that predict the maintenance well in advance, but if the equipment manufacturer fails to de-code this information, it will neither be able to supply on time, nor improvise its product to mitigate future losses.

Data Monetization refers to the process of using data to obtain a quantifiable economic benefit. Internal or indirect methods include using data to make measurable business performance improvements and inform decisions. External or direct methods include data sharing to gain beneficial terms or conditions from business partners, information bartering, selling data outright (via a data broker or independently), or offering information products and services (for example, including information as a value-added component of an existing offering).

Data monetization in oil and gas

The oil and gas industry turns a large volume and variety of data into intelligence for improved asset productivity. Real-time data analysis is essential for deriving value out of unstructured data generated from sensors present in the oil field. Predictive and prescriptive analytics are the approaches that help oil and gas companies to minimize expenses and earn money by turning this data into valuable assets.

The creation and consumption of data continue to grow in the oil and gas industry and with it, the investment in data analytics and data management software and services. Data monetization can be used to leverage insights to identify new revenue opportunities, trigger products, process & service innovation, and optimization, improve production, and enhance service quality in the oil and gas industry.

Countries with large proven oil reserves like Venezuela, Saudi Arabia, Canada, Iran, Iraq, Kuwait, UAE, Russia, the U.S, and China brings an opportunity to drive the growth of the oil and gas data monetization market as there is a significant growth opportunity for adoption of indirect data monetization i.e., the software and services for driving insights for the development of the fields and direct data monetization i.e., mainly the exploration data products. Seismic surveys and geophysical surveys are conducted in these regions to find new exploration sites and their potential, and the anticipated use of software solutions will continue to drive the market.

Predictive and prescriptive analytics are the approaches that help oil and gas companies to minimize expenses and earn money by turning this data into valuable assets. Real-time data analysis is essential for deriving insights from unstructured data generated from sensors present in the oilfield.

Such highly analyzed and streamlined data is being traded from oil and gas service companies and national data repositories in collaboration with oil companies. The oil and gas data monetization market is expected to flourish in the future, driven mainly by indirect data monetization i.e., software/platforms and professional services used to increase operational efficiencies deriving value from data for oil and gas companies.

Below are some examples in oil and gas industry where players monetized their data

Shell: It uses digital technology to create complementary services for existing customers, boosting downstream sales. It estimates that digital tools will contribute to one-fifth of its commercial revenue growth by 2025.
British Petroleum: BP sells technologies to oil peers and other industries such as construction and smart cities, creating new revenue decoupled from its usual clients. BP has spun off its seismic analytics technology, which has achieved $100 million savings for the company, among three other start-ups in its portfolio.
Wintershall DEA: It utilizes the maintenance data internally for improving system efficiencies with help of external digital service providers
OMV: OMV collaborated with Aker BP in areas of operational efficiency, drilling and subsurface. Together, the two companies collaborated on projects that explore how to set up and execute wide-scale digital transformation processes. Aker BP shared best practices from Eureka, their digital transformation program that started a few years ago. These insights advanced the digital maturity of OMV Upstream and also benefited a wide range of suppliers and partners connected to OMV’s operations. In turn, OMV shared the insights from its digitalization program DigitUP to advance the digital transformation of Aker BP.

Industries where data monetization has already begun

Different industries have already identified “data” as a powerful tool not only to take decisions but also to make revenues. Below are some of the examples across different industries where data monetization has gained attention:

Healthcare: Patient’s healthcare data like X-rays, medical prescriptions, genetic reports, pathology reports, etc. are shared with various value chain actors like pharmaceuticals companies, R&D organizations, and insurance companies for a fee. Depending on the business model of the data aggregator, the benefit is seldom given back to patients in the form of rewards points or vouchers. Few examples are GlaxoSmithKline’s agreement with 23andMe, LunaDNA, and KYT App.
Agriculture: Farming equipment companies like John Deere are fitted with sensors to track various land conditions, weeds, water reports, nutrition, etc. which is shared with a variety of value chain players like seed suppliers, fertilizer and pesticides manufacturers, agro consultants, insurance agencies and banks.
Mobility: Data of car owners is monitored through dashboard systems, wearable, and is shared with OEMs, health monitoring companies, insurance, and retail industry. Otonomo and Mojio are some examples.
Dairy: Cattle herds health, productivity, breeding, etc. can be tracked via collar devices. This information is monetized by sharing it with local dairies, veterinary practitioners, food nutrition companies, etc. Dairy Data Warehouse is one such example.

Key challenges of data monetization

Organizations that are implementing analytics programs to carry out data monetization face four critical challenges:

Low usage of advanced analytics across businesses: Most of the analytics revolves around creating use cases and enhancing the performance rather than building different lines of business. There is a need to scale up analytics to make it the core part of an organization’s business model. To make this happen, business focus, ownership, and accountability are essential.
Reskilling and hiring the right talent has become difficult: To enable data monetization, organizations need to build the right kind of ecosystem to develop, hire and retain the right talent.
Adherence to legal and compliance policies: Monetizing data will require organizations to properly understand the privacy and legal compliance policies from the beginning and add them in the design principles of products and services. To develop expertise, they need to involve compliance and risk experts to provide the necessary knowledge and capabilities.
Re-imagination of organizational structures is required: Due to advancements in technologies such as artificial intelligence and machine learning, there is a need for the evolution of roles, responsibilities, and metrics across organizations. For this proper change management programs need to be carried out.

Data Monetization in Oil and Gas Sector

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Demystifying data monetization

Data monetization in oil and gas

Industries where data monetization has already begun

Key challenges of data monetization

References

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Data Monetization in Oil and Gas Sector

Listen to this Article

Demystifying data monetization

Data monetization in oil and gas

Industries where data monetization has already begun

Key challenges of data monetization

References

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